Task intensive electronic control units (ECUs) in automotive domain, equipped with multicore processors ,
real time operating systems (RTOSs) and various application software, should perform efficiently and time
deterministically. The parallel computational capability offered by this multicore hardware can only be
exploited and utilized if the ECU application software is parallelized. Having provided with such
parallelized software, the real time operating system scheduler component should schedule the time critical
tasks so that, all the computational cores are utilized to a greater extent and the safety critical deadlines
are met. As original equipment manufacturers (OEMs) are always motivated towards adding more
sophisticated features to the existing ECUs, a large number of task sets can be effectively scheduled for
execution within the bounded time limits. In this paper, a hybrid scheduling algorithm has been proposed,
that meticulously calculates the running slack of every task and estimates the probability of meeting
deadline either being in the same partitioned queue or by migrating to another. This algorithm was run and
tested using a scheduling simulator with different real time task models of periodic tasks . This algorithm
was also compared with the existing static priority scheduler, which is suggested by Automotive Open
Systems Architecture (AUTOSAR). The performance parameters considered here are, the % of core
utilization, average response time and task deadline missing rate. It has been verified that, this proposed
algorithm has considerable improvements over the existing partitioned static priority scheduler based on
each performance parameter mentioned above.
DYNAMIC TASK SCHEDULING ON MULTICORE AUTOMOTIVE ECUSVLSICS Design
Automobile manufacturers are controlled by stringent govt. regulations for safety and fuel emissions and
motivated towards adding more advanced features and sophisticated applications to the existing electronic
system. Ever increasing customer’s demands for high level of comfort also necessitate providing even more
sophistication in vehicle electronics system. All these, directly make the vehicle software system more
complex and computationally more intensive. In turn, this demands very high computational capability of
the microprocessor used in electronic control unit (ECU). In this regard, multicore processors have
already been implemented in some of the task rigorous ECUs like, power train, image processing and
infotainment. To achieve greater performance from these multicore processors, parallelized ECU software
needs to be efficiently scheduled by the underlaying operating system for execution to utilize all the
computational cores to the maximum extent possible and meet the real time constraint. In this paper, we
propose a dynamic task scheduler for multicore engine control ECU that provides maximum CPU
utilization, minimized preemption overhead, minimum average waiting time and all the tasks meet their
real time deadlines while compared to the static priority scheduling suggested by Automotive Open Systems
Architecture (AUTOSAR).
Dynamic task scheduling on multicore automotive ec usVLSICS Design
Automobile manufacturers are controlled by stringent govt. regulations for safety and fuel emissions and
motivated towards adding more advanced features and sophisticated applications to the existing electronic
system. Ever increasing customer’s demands for high level of comfort also necessitate providing even more
sophistication in vehicle electronics system. All these, directly make the vehicle software system more
complex and computationally more intensive. In turn, this demands very high computational capability of
the microprocessor used in electronic control unit (ECU). In this regard, multicore processors have
already been implemented in some of the task rigorous ECUs like, power train, image processing and
infotainment. To achieve greater performance from these multicore processors, parallelized ECU software
needs to be efficiently scheduled by the underlaying operating system for execution to utilize all the
computational cores to the maximum extent possible and meet the real time constraint. In this paper, we
propose a dynamic task scheduler for multicore engine control ECU that provides maximum CPU
utilization, minimized preemption overhead, minimum average waiting time and all the tasks meet their
real time deadlines while compared to the static priority scheduling suggested by Automotive Open Systems
Architecture (AUTOSAR).
DYNAMIC TASK SCHEDULING ON MULTICORE AUTOMOTIVE ECUSVLSICS Design
Automobile manufacturers are controlled by stringent govt. regulations for safety and fuel emissions and motivated towards adding more advanced features and sophisticated applications to the existing electronic system. Ever increasing customer’s demands for high level of comfort also necessitate providing even more
sophistication in vehicle electronics system. All these, directly make the vehicle software system more complex and computationally more intensive. In turn, this demands very high computational capability of the microprocessor used in electronic control unit (ECU). In this regard, multicore processors have already been implemented in some of the task rigorous ECUs like, power train, image processing and infotainment. To achieve greater performance from these multicore processors, parallelized ECU software needs to be efficiently scheduled by the underlaying operating system for execution to utilize all the computational cores to the maximum extent possible and meet the real time constraint. In this paper, we propose a dynamic task scheduler for multicore engine control ECU that provides maximum CPU
utilization, minimized preemption overhead, minimum average waiting time and all the tasks meet their real time deadlines while compared to the static priority scheduling suggested by Automotive Open Systems Architecture (AUTOSAR).
SOFTWARE AND HARDWARE DESIGN CHALLENGES IN AUTOMOTIVE EMBEDDED SYSTEMVLSICS Design
Modern automotives integrate large amount of electronic devices to improve the driving safety and comfort. This growing number of Electronic Control Units (ECUs) with sophisticated software escalates the vehicle system design complexity. In this paper we explain the complexity of ECUs in terms of hardware and software and also we explore the possibility of Common Object Request Broker Architecture (CORBA) architecture for the integration of add-on software in ECUs. This reduces the complexity of the embedded system in vehicles and eases the ECU integration by reducing the total number of ECUs in the vehicles.
SIMULATION-BASED APPLICATION SOFTWARE DEVELOPMENT IN TIME-TRIGGERED COMMUNICA...IJSEA
This paper introduces a simulation-based approach for design and test of application software for timetriggered
communication systems. The approach is based on the SIDERA simulation system that supports
the time-triggered real-time protocols TTP and FlexRay. We present a software development platform for
FlexRay based communication systems that provides an implementation of the AUTOSAR standard
interface for communication between host application and FlexRay communication controllers. For
validation, we present an application example in the course of which SIDERA has been deployed for
development and test of software modules for an automotive project in the field of driving dynamics
control.
ENERGY EFFICIENT SCHEDULING FOR REAL-TIME EMBEDDED SYSTEMS WITH PRECEDENCE AN...IJCSEA Journal
Energy consumption is a critical design issue in real-time systems, especially in battery- operated systems. Maintaining high performance, while extending the battery life between charges is an interesting challenge for system designers. Dynamic Voltage Scaling and Dynamic Frequency Scaling allow us to adjust supply voltage and processor frequency to adapt to the workload demand for better energy management. Usually, higher processor voltage and frequency leads to higher system throughput while energy reduction can be obtained using lower voltage and frequency. Many real-time scheduling algorithms have been developed recently to reduce energy consumption in the portable devices that use voltage scalable processors. For a real-time application, comprising a set of real-time tasks with precedence and resource constraints executing on a distributed system, we propose a dynamic energy efficient scheduling algorithm with weighted First Come First Served (WFCFS) scheme. This also considers the run-time behaviour of tasks, to further explore the idle periods of processors for energy saving. Our algorithm is compared with the existing Modified Feedback Control Scheduling (MFCS), First Come First Served (FCFS), and Weighted scheduling (WS) algorithms that uses Service-Rate-Proportionate (SRP) Slack Distribution Technique. Our proposed algorithm achieves about 5 to 6 percent more energy savings and increased reliability over the existing ones.
DYNAMIC TASK SCHEDULING ON MULTICORE AUTOMOTIVE ECUSVLSICS Design
Automobile manufacturers are controlled by stringent govt. regulations for safety and fuel emissions and
motivated towards adding more advanced features and sophisticated applications to the existing electronic
system. Ever increasing customer’s demands for high level of comfort also necessitate providing even more
sophistication in vehicle electronics system. All these, directly make the vehicle software system more
complex and computationally more intensive. In turn, this demands very high computational capability of
the microprocessor used in electronic control unit (ECU). In this regard, multicore processors have
already been implemented in some of the task rigorous ECUs like, power train, image processing and
infotainment. To achieve greater performance from these multicore processors, parallelized ECU software
needs to be efficiently scheduled by the underlaying operating system for execution to utilize all the
computational cores to the maximum extent possible and meet the real time constraint. In this paper, we
propose a dynamic task scheduler for multicore engine control ECU that provides maximum CPU
utilization, minimized preemption overhead, minimum average waiting time and all the tasks meet their
real time deadlines while compared to the static priority scheduling suggested by Automotive Open Systems
Architecture (AUTOSAR).
Dynamic task scheduling on multicore automotive ec usVLSICS Design
Automobile manufacturers are controlled by stringent govt. regulations for safety and fuel emissions and
motivated towards adding more advanced features and sophisticated applications to the existing electronic
system. Ever increasing customer’s demands for high level of comfort also necessitate providing even more
sophistication in vehicle electronics system. All these, directly make the vehicle software system more
complex and computationally more intensive. In turn, this demands very high computational capability of
the microprocessor used in electronic control unit (ECU). In this regard, multicore processors have
already been implemented in some of the task rigorous ECUs like, power train, image processing and
infotainment. To achieve greater performance from these multicore processors, parallelized ECU software
needs to be efficiently scheduled by the underlaying operating system for execution to utilize all the
computational cores to the maximum extent possible and meet the real time constraint. In this paper, we
propose a dynamic task scheduler for multicore engine control ECU that provides maximum CPU
utilization, minimized preemption overhead, minimum average waiting time and all the tasks meet their
real time deadlines while compared to the static priority scheduling suggested by Automotive Open Systems
Architecture (AUTOSAR).
DYNAMIC TASK SCHEDULING ON MULTICORE AUTOMOTIVE ECUSVLSICS Design
Automobile manufacturers are controlled by stringent govt. regulations for safety and fuel emissions and motivated towards adding more advanced features and sophisticated applications to the existing electronic system. Ever increasing customer’s demands for high level of comfort also necessitate providing even more
sophistication in vehicle electronics system. All these, directly make the vehicle software system more complex and computationally more intensive. In turn, this demands very high computational capability of the microprocessor used in electronic control unit (ECU). In this regard, multicore processors have already been implemented in some of the task rigorous ECUs like, power train, image processing and infotainment. To achieve greater performance from these multicore processors, parallelized ECU software needs to be efficiently scheduled by the underlaying operating system for execution to utilize all the computational cores to the maximum extent possible and meet the real time constraint. In this paper, we propose a dynamic task scheduler for multicore engine control ECU that provides maximum CPU
utilization, minimized preemption overhead, minimum average waiting time and all the tasks meet their real time deadlines while compared to the static priority scheduling suggested by Automotive Open Systems Architecture (AUTOSAR).
SOFTWARE AND HARDWARE DESIGN CHALLENGES IN AUTOMOTIVE EMBEDDED SYSTEMVLSICS Design
Modern automotives integrate large amount of electronic devices to improve the driving safety and comfort. This growing number of Electronic Control Units (ECUs) with sophisticated software escalates the vehicle system design complexity. In this paper we explain the complexity of ECUs in terms of hardware and software and also we explore the possibility of Common Object Request Broker Architecture (CORBA) architecture for the integration of add-on software in ECUs. This reduces the complexity of the embedded system in vehicles and eases the ECU integration by reducing the total number of ECUs in the vehicles.
SIMULATION-BASED APPLICATION SOFTWARE DEVELOPMENT IN TIME-TRIGGERED COMMUNICA...IJSEA
This paper introduces a simulation-based approach for design and test of application software for timetriggered
communication systems. The approach is based on the SIDERA simulation system that supports
the time-triggered real-time protocols TTP and FlexRay. We present a software development platform for
FlexRay based communication systems that provides an implementation of the AUTOSAR standard
interface for communication between host application and FlexRay communication controllers. For
validation, we present an application example in the course of which SIDERA has been deployed for
development and test of software modules for an automotive project in the field of driving dynamics
control.
ENERGY EFFICIENT SCHEDULING FOR REAL-TIME EMBEDDED SYSTEMS WITH PRECEDENCE AN...IJCSEA Journal
Energy consumption is a critical design issue in real-time systems, especially in battery- operated systems. Maintaining high performance, while extending the battery life between charges is an interesting challenge for system designers. Dynamic Voltage Scaling and Dynamic Frequency Scaling allow us to adjust supply voltage and processor frequency to adapt to the workload demand for better energy management. Usually, higher processor voltage and frequency leads to higher system throughput while energy reduction can be obtained using lower voltage and frequency. Many real-time scheduling algorithms have been developed recently to reduce energy consumption in the portable devices that use voltage scalable processors. For a real-time application, comprising a set of real-time tasks with precedence and resource constraints executing on a distributed system, we propose a dynamic energy efficient scheduling algorithm with weighted First Come First Served (WFCFS) scheme. This also considers the run-time behaviour of tasks, to further explore the idle periods of processors for energy saving. Our algorithm is compared with the existing Modified Feedback Control Scheduling (MFCS), First Come First Served (FCFS), and Weighted scheduling (WS) algorithms that uses Service-Rate-Proportionate (SRP) Slack Distribution Technique. Our proposed algorithm achieves about 5 to 6 percent more energy savings and increased reliability over the existing ones.
System on Chip Based RTC in Power ElectronicsjournalBEEI
Current control systems and emulation systems (Hardware-in-the-Loop, HIL or Processor-in-the-Loop, PIL) for high-end power-electronic applications often consist of numerous components and interlinking busses: a micro controller for communication and high level control, a DSP for real-time control, an FPGA section for fast parallel actions and data acquisition, multiport RAM structures or bus systems as interconnecting structure. System-on-Chip (SoC) combines many of these functions on a single die. This gives the advantage of space reduction combined with cost reduction and very fast internal communication. Such systems become very relevant for research and also for industrial applications. The SoC used here as an example combines a Dual-Core ARM 9 hard processor system (HPS) and an FPGA, including fast interlinks between these components. SoC systems require careful software and firmware concepts to provide real-time control and emulation capability. This paper demonstrates an optimal way to use the resources of the SoC and discusses challenges caused by the internal structure of SoC. The key idea is to use asymmetric multiprocessing: One core uses a bare-metal operating system for hard real time. The other core runs a “real-time” Linux for service functions and communication. The FPGA is used for flexible process-oriented interfaces (A/D, D/A, switching signals), quasi-hard-wired protection and the precise timing of the real-time control cycle. This way of implementation is generally known and sometimes even suggested–but to the knowledge of the author’s seldomly implemented and documented in the context of demanding real-time control or emulation. The paper details the way of implementation, including process interfaces, and discusses the advantages and disadvantages of the chosen concept. Measurement results demonstrate the properties of the solution.
CS 301 Computer ArchitectureStudent # 1 EID 09Kingdom of .docxfaithxdunce63732
CS 301 Computer Architecture
Student # 1
E
ID: 09
Kingdom of Saudi Arabia Royal Commission at Yanbu Yanbu University College Yanbu Al-Sinaiyah
Student # 2
H
ID: 09
Kingdom of Saudi Arabia Royal Commission at Yanbu Yanbu University College Yanbu Al-Sinaiyah
1
1. Introduction
High-performance processor design has recently taken two distinct approaches. One approach is to increase the execution rate by increasing the clock frequency of the processor or by reducing the execution latency of the operations. While this approach is important, much of its performance gain comes as a consequence of circuit and layout improvements and is beyond the scope of this research. The other approach is to directly exploit the instruction-level parallelism (ILP) in the program and to issue and execute multiple operations concurrently. This approach requires both compiler and microarchitecture support.
Traditional processor designs that issue and execute at most one operation per cycle are often called scalar designs. Static and dynamic scheduling techniques have been used to achieve better-than scalar performance by issuing and executing more than one operation per cycle. While Johnson[7] defines a superscalar processor as a design that achieves better-than scalar performance, popular usage of this term refers exclusively to those processors that use dynamic scheduling techniques. For clarity, we use instruction-level parallel processors to refer to the general class of processors that execute more than one operation per cycle of the computer both at the personal level, or the level of a small network of computers to do not require more of these types.
The primary static scheduling technique uses the compiler to determine sets of operations that have their source operands ready and have no dependencies within the set. These operations can then be scheduled within the same instruction subject only to hardware resource limits. Since each of the operations in an instruction is guaranteed by the compiler to be independent, the hardware is able to is- sue and execute these operations directly with no dynamic analysis. These multi-operation instructions are very long in comparison with traditional single-operation instructions and processors using .
D1.2 analysis and selection of low power techniques, services and patternsBabak Sorkhpour
Goal: SAFEPOWER has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687902.
SAFEPOWER’s goal is to enable the development of low power mixed-criticality systems through the provision of a reference architecture, platforms and tools to facilitate the development, testing, and validation of these kinds of systems according to the market needs
It is expected that the SAFEPOWER reference architecture and platforms will enable the integration and partitioning of mixed-criticality applications on a single device while reducing the total power consumption by 50%, compared to the non-integrated multi-chip implementation. To address this goal, SAFEPOWER needs to address a number of technology development challenges, that will afterwards be applied to the main project outputs, namely the SAFEPOWER low power reference architecture, the platforms and tools for the development, testing, and validation of low power mixed criticality systems.
PROJECT PARTNER(S):IKERLAN, S. Coop.CAF Signalling, S.L.fent Innovative Software SolutionsImperas Software Ltd.Kungliga Tekniska Högskolan (Royal Institute of Technology)SAAB ABUniversität Siegen
Methods: Railway Engineering, Energy Efficiency, Multi-Core Systems, Safety-Critical Systems, Industrial Safety, Avionics, MPSOCs, NoC, Fault Tolerance, Scheduling Theory, Power Management, Dependable Systems, MIXED CRITICALITY, predictable architectures and communication, Low Power Techniques, ENERGY MINIMIZATION TECHNIQUES, Energy and Power efficiency, Low power multicore embedded systems, Fault Isolation, hypervisor
Social media links:
a.Twitter : https://twitter.com/SAFEPOWER_H2020
b.Linkdin : https://www.linkedin.com/groups/7045467
d. Website : http://safepower-project.eu/
d.ResearchGate : https://www.researchgate.net/project/SAFEPOWER
SCHEDULING DIFFERENT CUSTOMER ACTIVITIES WITH SENSING DEVICEijait
Most periodic tasks are assigned to processors using partition scheduling policy after checking feasibility conditions. A new approach is proposed for scheduling different activities with one periodic task within the system. In this paper, control strategies are identified for allocating different types of tasks (activities) to
individual computing elements like Smartphone or microphones. In our simulation model, each periodic task generates an aperiodic tasks are taken into consideration. Different sets of periodic tasks and aperiodic tasks are scheduled together. This new approach proves that when all different activities are
scheduled with one periodic tasks leads to better performance.
Unit 1 Introduction to Embedded computing and ARM processorVenkat Ramanan C
INTRODUCTION TO EMBEDDED COMPUTING AND ARM PROCESSORS
Complex systems and microprocessors – Embedded system design process – Formalism for system design– Design example: Model train controller- ARM Processor Fundamentals- Instruction Set and Programming using ARM Processor.
DESIGN OF AN EMBEDDED SYSTEM: BEDSIDE PATIENT MONITORijesajournal
Embedded systems in the range of from a tiny microcontroller-based sensor device to mobile smart phones
have vast variety of applications. However, in the literature there is no up to date system-level design of
embedded hardware and software, instead academic publications are mainly focused on the improvement
of specific features of embedded software/hardware and the embedded system designs for specific
applications. Moreover, commercially available embedded systems are not disclosed for the view of
researchers in the literature. Therefore, in this paper we first present how to design a state of art embedded
system including emerged hardware and software technologies. Bedside Patient monitor devices used in
intensive cares units of hospitals are also classified as embedded systems and run sophisticated software
and algorithms for better diagnosis of diseases. We reveal the architecture of our, commercially available,
bedside patient monitor to provide a design example of embedded systemsrelating to emerged technologies.
DESIGN OF AN EMBEDDED SYSTEM: BEDSIDE PATIENT MONITORijesajournal
Embedded systems in the range of from a tiny microcontroller-based sensor device to mobile smart phones
have vast variety of applications. However, in the literature there is no up to date system-level design of
embedded hardware and software, instead academic publications are mainly focused on the improvement
of specific features of embedded software/hardware and the embedded system designs for specific
applications. Moreover, commercially available embedded systems are not disclosed for the view of
researchers in the literature. Therefore, in this paper we first present how to design a state of art embedded
system including emerged hardware and software technologies. Bedside Patient monitor devices used in
intensive cares units of hospitals are also classified as embedded systems and run sophisticated software
and algorithms for better diagnosis of diseases. We reveal the architecture of our, commercially available,
bedside patient monitor to provide a design example of embedded systemsrelating to emerged technologies.
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System on Chip Based RTC in Power ElectronicsjournalBEEI
Current control systems and emulation systems (Hardware-in-the-Loop, HIL or Processor-in-the-Loop, PIL) for high-end power-electronic applications often consist of numerous components and interlinking busses: a micro controller for communication and high level control, a DSP for real-time control, an FPGA section for fast parallel actions and data acquisition, multiport RAM structures or bus systems as interconnecting structure. System-on-Chip (SoC) combines many of these functions on a single die. This gives the advantage of space reduction combined with cost reduction and very fast internal communication. Such systems become very relevant for research and also for industrial applications. The SoC used here as an example combines a Dual-Core ARM 9 hard processor system (HPS) and an FPGA, including fast interlinks between these components. SoC systems require careful software and firmware concepts to provide real-time control and emulation capability. This paper demonstrates an optimal way to use the resources of the SoC and discusses challenges caused by the internal structure of SoC. The key idea is to use asymmetric multiprocessing: One core uses a bare-metal operating system for hard real time. The other core runs a “real-time” Linux for service functions and communication. The FPGA is used for flexible process-oriented interfaces (A/D, D/A, switching signals), quasi-hard-wired protection and the precise timing of the real-time control cycle. This way of implementation is generally known and sometimes even suggested–but to the knowledge of the author’s seldomly implemented and documented in the context of demanding real-time control or emulation. The paper details the way of implementation, including process interfaces, and discusses the advantages and disadvantages of the chosen concept. Measurement results demonstrate the properties of the solution.
CS 301 Computer ArchitectureStudent # 1 EID 09Kingdom of .docxfaithxdunce63732
CS 301 Computer Architecture
Student # 1
E
ID: 09
Kingdom of Saudi Arabia Royal Commission at Yanbu Yanbu University College Yanbu Al-Sinaiyah
Student # 2
H
ID: 09
Kingdom of Saudi Arabia Royal Commission at Yanbu Yanbu University College Yanbu Al-Sinaiyah
1
1. Introduction
High-performance processor design has recently taken two distinct approaches. One approach is to increase the execution rate by increasing the clock frequency of the processor or by reducing the execution latency of the operations. While this approach is important, much of its performance gain comes as a consequence of circuit and layout improvements and is beyond the scope of this research. The other approach is to directly exploit the instruction-level parallelism (ILP) in the program and to issue and execute multiple operations concurrently. This approach requires both compiler and microarchitecture support.
Traditional processor designs that issue and execute at most one operation per cycle are often called scalar designs. Static and dynamic scheduling techniques have been used to achieve better-than scalar performance by issuing and executing more than one operation per cycle. While Johnson[7] defines a superscalar processor as a design that achieves better-than scalar performance, popular usage of this term refers exclusively to those processors that use dynamic scheduling techniques. For clarity, we use instruction-level parallel processors to refer to the general class of processors that execute more than one operation per cycle of the computer both at the personal level, or the level of a small network of computers to do not require more of these types.
The primary static scheduling technique uses the compiler to determine sets of operations that have their source operands ready and have no dependencies within the set. These operations can then be scheduled within the same instruction subject only to hardware resource limits. Since each of the operations in an instruction is guaranteed by the compiler to be independent, the hardware is able to is- sue and execute these operations directly with no dynamic analysis. These multi-operation instructions are very long in comparison with traditional single-operation instructions and processors using .
D1.2 analysis and selection of low power techniques, services and patternsBabak Sorkhpour
Goal: SAFEPOWER has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 687902.
SAFEPOWER’s goal is to enable the development of low power mixed-criticality systems through the provision of a reference architecture, platforms and tools to facilitate the development, testing, and validation of these kinds of systems according to the market needs
It is expected that the SAFEPOWER reference architecture and platforms will enable the integration and partitioning of mixed-criticality applications on a single device while reducing the total power consumption by 50%, compared to the non-integrated multi-chip implementation. To address this goal, SAFEPOWER needs to address a number of technology development challenges, that will afterwards be applied to the main project outputs, namely the SAFEPOWER low power reference architecture, the platforms and tools for the development, testing, and validation of low power mixed criticality systems.
PROJECT PARTNER(S):IKERLAN, S. Coop.CAF Signalling, S.L.fent Innovative Software SolutionsImperas Software Ltd.Kungliga Tekniska Högskolan (Royal Institute of Technology)SAAB ABUniversität Siegen
Methods: Railway Engineering, Energy Efficiency, Multi-Core Systems, Safety-Critical Systems, Industrial Safety, Avionics, MPSOCs, NoC, Fault Tolerance, Scheduling Theory, Power Management, Dependable Systems, MIXED CRITICALITY, predictable architectures and communication, Low Power Techniques, ENERGY MINIMIZATION TECHNIQUES, Energy and Power efficiency, Low power multicore embedded systems, Fault Isolation, hypervisor
Social media links:
a.Twitter : https://twitter.com/SAFEPOWER_H2020
b.Linkdin : https://www.linkedin.com/groups/7045467
d. Website : http://safepower-project.eu/
d.ResearchGate : https://www.researchgate.net/project/SAFEPOWER
SCHEDULING DIFFERENT CUSTOMER ACTIVITIES WITH SENSING DEVICEijait
Most periodic tasks are assigned to processors using partition scheduling policy after checking feasibility conditions. A new approach is proposed for scheduling different activities with one periodic task within the system. In this paper, control strategies are identified for allocating different types of tasks (activities) to
individual computing elements like Smartphone or microphones. In our simulation model, each periodic task generates an aperiodic tasks are taken into consideration. Different sets of periodic tasks and aperiodic tasks are scheduled together. This new approach proves that when all different activities are
scheduled with one periodic tasks leads to better performance.
Unit 1 Introduction to Embedded computing and ARM processorVenkat Ramanan C
INTRODUCTION TO EMBEDDED COMPUTING AND ARM PROCESSORS
Complex systems and microprocessors – Embedded system design process – Formalism for system design– Design example: Model train controller- ARM Processor Fundamentals- Instruction Set and Programming using ARM Processor.
DESIGN OF AN EMBEDDED SYSTEM: BEDSIDE PATIENT MONITORijesajournal
Embedded systems in the range of from a tiny microcontroller-based sensor device to mobile smart phones
have vast variety of applications. However, in the literature there is no up to date system-level design of
embedded hardware and software, instead academic publications are mainly focused on the improvement
of specific features of embedded software/hardware and the embedded system designs for specific
applications. Moreover, commercially available embedded systems are not disclosed for the view of
researchers in the literature. Therefore, in this paper we first present how to design a state of art embedded
system including emerged hardware and software technologies. Bedside Patient monitor devices used in
intensive cares units of hospitals are also classified as embedded systems and run sophisticated software
and algorithms for better diagnosis of diseases. We reveal the architecture of our, commercially available,
bedside patient monitor to provide a design example of embedded systemsrelating to emerged technologies.
DESIGN OF AN EMBEDDED SYSTEM: BEDSIDE PATIENT MONITORijesajournal
Embedded systems in the range of from a tiny microcontroller-based sensor device to mobile smart phones
have vast variety of applications. However, in the literature there is no up to date system-level design of
embedded hardware and software, instead academic publications are mainly focused on the improvement
of specific features of embedded software/hardware and the embedded system designs for specific
applications. Moreover, commercially available embedded systems are not disclosed for the view of
researchers in the literature. Therefore, in this paper we first present how to design a state of art embedded
system including emerged hardware and software technologies. Bedside Patient monitor devices used in
intensive cares units of hospitals are also classified as embedded systems and run sophisticated software
and algorithms for better diagnosis of diseases. We reveal the architecture of our, commercially available,
bedside patient monitor to provide a design example of embedded systemsrelating to emerged technologies.
PIP-MPU: FORMAL VERIFICATION OF AN MPUBASED SEPARATION KERNEL FOR CONSTRAINED...ijesajournal
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Pip-MPU offers user-defined on-demand multiple isolation levels guarded by the Memory Protection Unit
(MPU). Pip-MPU derives from the Pip protokernel, with a full code refactoring to adapt to the constrained
environment and targets equivalent security properties. The proofs verify that the memory blocks loaded in
the MPU adhere to the global partition tree model. We provide the basis of the MPU formalisation and the
demonstration of the formal verification strategy on two representative kernel services. The publicly
released proofs have been implemented and checked using the Coq Proof Assistant for three kernel
services, representing around 10000 lines of proof. To our knowledge, this is the first formal verification of
an MPU based separation kernel. The verification process helped discover a critical isolation-related bug.
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Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Embedded Systems & applications.
Pip-MPU: Formal Verification of an MPU-Based Separationkernel for Constrained...ijesajournal
Pip-MPU is a minimalist separation kernel for constrained devices (scarce memory and power resources). In this work, we demonstrate high-assurance of Pip-MPU’s isolation property through formal verification. Pip-MPU offers user-defined on-demand multiple isolation levels guarded by the Memory Protection Unit (MPU). Pip-MPU derives from the Pip protokernel, with a full code refactoring to adapt to the constrained environment and targets equivalent security properties. The proofs verify that the memory blocks loaded in the MPU adhere to the global partition tree model. We provide the basis of the MPU formalisation and the demonstration of the formal verification strategy on two representative kernel services. The publicly released proofs have been implemented and checked using the Coq Proof Assistant for three kernel services, representing around 10000 lines of proof. To our knowledge, this is the first formal verification of an MPU based separation kernel. The verification process helped discover a critical isolation-related bug.
International Journal of Embedded Systems and Applications (IJESA)ijesajournal
International Journal of Embedded Systems and Applications (IJESA) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Embedded Systems and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Embedded Systems and establishing new collaborations in these areas.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Embedded Systems & applications.
Call for papers -15th International Conference on Wireless & Mobile Network (...ijesajournal
15th International Conference on Wireless & Mobile Network (WiMo 2023) is dedicated to addressing the challenges in the areas of wireless & mobile networks. The Conference looks for significant contributions to the Wireless and Mobile computing in theoretical and practical aspects. The Wireless and Mobile computing domain emerges from the integration among personal computing, networks, communication technologies, cellular technology, and the Internet Technology. The modern applications are emerging in the area of mobile ad hoc networks and sensor networks. This Conference is intended to cover contributions in both the design and analysis in the context of mobile, wireless, ad-hoc, and sensor networks. The goal of this Conference is to bring together researchers and practitioners from academia and industry to focus on advanced wireless and Mobile computing concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.
Call for Papers -International Conference on NLP & Signal (NLPSIG 2023)ijesajournal
Scope & Topics
International Conference on NLP & Signal (NLPSIG 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Signal and Natural Language Processing (NLP).
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to:
Topics of interest include, but are not limited to, the following
Chunking/Shallow Parsing
Dialogue and Interactive Systems
Deep learning and NLP
Discourseand Pragmatics
Information Extraction, Retrieval, Text Mining
Interpretability and Analysis of Models for NLP
Language Grounding to Vision, Robotics and Beyond
Lexical Semantics
Linguistic Resources
Machine Learning for NLP
Machine Translation
NLP and Signal Processing
NLP Applications
Ontology
Paraphrasing/Entailment/Generation
Parsing/Grammatical Formalisms
Phonology, Morphology
POS tagging
Question Answering
Resources and Evaluation
Semantic Processing
Sentiment Analysis, Stylistic Analysis, and Argument Mining
Speech and Multimodality
Speech Recognition and Synthesis
Spoken Language Processing
Statistical and Knowledge based methods
Summarization
Theory and Formalism in NLP
Signal Processing & NLP
Computer Vision, Image Processing& NLP
NLP, AI & Signal
Paper Submission
Authors are invited to submit papers through the conference Submission System by May 06, 2023. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI) (Confirmed).
Selected papers from NLPSIG 2023, after further revisions, will be published in the special issue of the following journals.
International Journal on Natural Language Computing (IJNLC)
International Journal of Ubiquitous Computing (IJU)
International Journal of Data Mining & Knowledge Management Process (IJDKP)
Signal & Image Processing : An International Journal (SIPIJ)
International Journal of Ambient Systems and Applications (IJASA)
International Journal of Grid Computing & Applications (IJGCA)
Important Dates
Submission Deadline : May 06, 2023
Authors Notification : May 25, 2023
Final Manuscript Due : June 08, 2023
International Conference on NLP & Signal (NLPSIG 2023)ijesajournal
International Conference on NLP & Signal (NLPSIG 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Signal and Natural Language Processing (NLP).
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to:
11th International Conference on Software Engineering & Trends (SE 2023)ijesajournal
11th International Conference on Software Engineering & Trends (SE 2023)
May 27 ~ 28, 2023, Vancouver, Canada
https://acsit2023.org/se/index
Scope & Topics
11th International Conference on Software Engineering & Trends (SE 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Software Engineering. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of software engineering & applications. Topics of interest include, but are not limited to, the following.
Topics of interest include, but are not limited to, the following
The Software Process
Software Engineering Practice
Web Engineering
Quality Management
Managing Software Projects
Advanced Topics in Software Engineering
Multimedia and Visual Software Engineering
Software Maintenance and Testing
Languages and Formal Methods
Web-based Education Systems and Learning Applications
Software Engineering Decision Making
Knowledge-based Systems and Formal Methods
Search Engines and Information Retrieval
Paper Submission
Authors are invited to submit papers through the conference Submission System by April 08, 2023. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings (H index 35) in Computer Science & Information Technology (CS & IT) series (Confirmed).
Selected papers from SE 2023, after further revisions, will be published in the special issue of the following journals.
The International Journal of Software Engineering & Applications (IJSEA) -ERA indexed
International Journal of Computer Science, Engineering and Applications (IJCSEA)
Important Dates
Submission Deadline : April 08, 2023
Authors Notification : April 29, 2023
Final Manuscript Due : May 06, 2023
11th International Conference on Software Engineering & Trends (SE 2023)ijesajournal
11th International Conference on Software Engineering & Trends (SE 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Software Engineering. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts and establishing new collaborations in these areas.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of software engineering & applications. Topics of interest include, but are not limited to, the following.
PERFORMING AN EXPERIMENTAL PLATFORM TO OPTIMIZE DATA MULTIPLEXINGijesajournal
This article is based on preliminary work on the OSI model management layers to optimized industrial
wired data transfer on low data rate wireless technology. Our previous contribution deal with the
development of a demonstrator providing CAN bus transfer frames (1Mbps) on a low rate wireless channel
provided by Zigbee technology. In order to be compatible with all the other industrial protocols, we
describe in this paper our contribution to design an innovative Wireless Device (WD) and a software tool,
which will aim to determine the best architecture (hardware/software) and wireless technology to be used
taking in account of the wired protocol requirements. To validate the proper functioning of this WD, we
will develop an experimental platform to test different strategies provided by our software tool. We can
consequently prove which is the best configuration (hardware/software) compared to the others by the
inclusion (inputs) of the required parameters of the wired protocol (load, binary rate, acknowledge
timeout) and the analysis of the WD architecture characteristics proposed (outputs) as the delay introduced
by system, buffer size needed, CPU speed, power consumption, meeting the input requirement. It will be
important to know whether gain comes from a hardware strategy with hardware accelerator e.g or a
software strategy with a more perf
GENERIC SOPC PLATFORM FOR VIDEO INTERACTIVE SYSTEM WITH MPMC CONTROLLERijesajournal
Today, a significant number of embedded systems focus on multimedia applications with almost insatiable demand for low-cost, high performance, and low power hardware cosumption. In this paper, we present a re-configurable and generic hardware platform for image and video processing. The proposed platform uses the benefits offered by the Field Programmable Gate Array (FPGA) to attain this goal. In this context,
a prototype system is developed based on the Xilinx Virtex-5 FPGA with the integration of embedded processors, embedded memory, DDR, interface technologies, Digital Clock Managers (DCM) and MPMC.
The MPMC is an essential component for design performance tuning and real time video processing. We demonstrate the importance role of this interface in multi video applications. In fact, to successful the
deployment of DRAM it is mandatory to use a flexible and scalable interface. Our system introduces diverse modules, such as cut video detection, video zoom-in and out. This provides the utility of using this architecture as a universal video processing platform according to different application requirements. This platform facilitates the development of video and image processing applications.
This paper presents an inverting buck-boost DCDC converter design. A negative supply voltage is needed
in a variety of applications, but only a few DCDC converters are available on the market. OLED, a new
display type especially suited for small digital camera or mobile phone displays. Design challenges that
came up when negative voltages have to be handled on chip will be discussed, such as
continuous/discontinuous mode transition problems, negative voltage feedback and negative over-voltage
protection. Both devices operate in a fixed frequency PWM mode or alternatively in PFM mode. The single
inductor topology is called inverting buck-boost converter or simply inverter. The proposed converter has
been implemented with a TSMC 0.13-um 2P4M CMOS process, and the chip area is 325 x 300 um2.
A Case Study: Task Scheduling Methodologies for High Speed Computing Systems ijesajournal
High Speed computing meets ever increasing real-time computational demands through the leveraging of
flexibility and parallelism. The flexibility is achieved when computing platform designed with
heterogeneous resources to support multifarious tasks of an application where as task scheduling brings
parallel processing. The efficient task scheduling is critical to obtain optimized performance in
heterogeneous computing Systems (HCS). In this paper, we brought a review of various application
scheduling models which provide parallelism for homogeneous and heterogeneous computing systems. In
this paper, we made a review of various scheduling methodologies targeted to high speed computing
systems and also prepared summary chart. The comparative study of scheduling methodologies for high
speed computing systems has been carried out based on the attributes of platform & application as well.
The attributes are execution time, nature of task, task handling capability, type of host & computing
platform. Finally a summary chart has been prepared and it demonstrates that the need of developing
scheduling methodologies for Heterogeneous Reconfigurable Computing Systems (HRCS) which is an
emerging high speed computing platform for real time applications.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
Payment industry is largely aligned in their desire to create embedded payment systems ready for the
modern digital age. The trend to embed payments into a software platform is often regarded as first step
towards a broader trend of embedded finance based on digital representation of fiat currencies. Since it
became clear to our research team that there are no technologies and protocols that are protected against
attacks of quantum computing, and that enable automatic embedded payments, online or offline with no
fear of counterfeit, P2P or device-to-device to be made in real time without intermediaries, in any
denomination, even continuous payments per time or service, while preserving the privacy of all parties,
without enabling illicit activities, we decided to utilize the Generic Innovation Engine [1] that is based on
the Artificial Intelligence Assistance Innovation acceleration methodologies and tools in order to boost the
progress of innovation of the necessary solutions. These methodologies accelerate innovation across the
board. It proposes a framework for natural and artificial intelligence collaboration in pursuit of an
innovative (R&D) objective The outcome of deploying these Artificial Innovation Assistant (AIA)
methodologies was tens of patents that yield solutions, that a few of them are described in this paper. We
argue that a promising avenue for automated embedded payment systems to fulfil people’s desire for
privacy when conducting payments, and national security agencies demand for quantum-safe security,
could be based on DeFi and digital currencies platforms that does not suffer from flaws of DLT-based
solutions, while introducing real advantages, in all aspects, including being quantum-resilient, enabling
users to decide with whom, if at all, to share information, identity, transactions details, etc., all without
trade-offs, complying with AML measures, and accommodating the potential for high transaction volumes.
It is not legacy bank accounts, and it is not peer-dependent, nor a self-organizing network.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
2 nd International Conference on Computing and Information Technology ijesajournal
2
nd International Conference on Computing and Information Technology Trends
(CCITT 2023) will provide an excellent international forum for sharing knowledge and
results in theory, methodology and applications of Computing and Information Technology
Trends. The Conference looks for significant contributions to all major fields of the
Computer Science, Compute Engineering, Information Technology and Trends in theoretical
and practical aspects.
A NOVEL METHODOLOGY FOR TASK DISTRIBUTION IN HETEROGENEOUS RECONFIGURABLE COM...ijesajournal
Modern embedded systems are being modeled as Heterogeneous Reconfigurable Computing Systems
(HRCS) where Reconfigurable Hardware i.e. Field Programmable Gate Array (FPGA) and soft core
processors acts as computing elements. So, an efficient task distribution methodology is essential for
obtaining high performance in modern embedded systems. In this paper, we present a novel methodology
for task distribution called Minimum Laxity First (MLF) algorithm that takes the advantage of runtime
reconfiguration of FPGA in order to effectively utilize the available resources. The MLF algorithm is a list
based dynamic scheduling algorithm that uses attributes of tasks as well computing resources as cost
function to distribute the tasks of an application to HRCS. In this paper, an on chip HRCS computing
platform is configured on Virtex 5 FPGA using Xilinx EDK. The real time applications JPEG, OFDM
transmitters are represented as task graph and then the task are distributed, statically as well dynamically,
to the platform HRCS in order to evaluate the performance of the designed task distribution model. Finally,
the performance of MLF algorithm is compared with existing static scheduling algorithms. The comparison
shows that the MLF algorithm outperforms in terms of efficient utilization of resources on chip and also
speedup an application execution.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
AN EFFICIENT HYBRID SCHEDULER USING DYNAMIC SLACK FOR REAL-TIME CRITICAL TASK SCHEDULING IN MULTICORE AUTOMOTIVE ECUs
1. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
DOI : 10.5121/ijesa.2015.5201 1
AN EFFICIENT HYBRID SCHEDULER
USING DYNAMIC SLACK FOR REAL-TIME
CRITICAL TASK SCHEDULING IN
MULTICORE AUTOMOTIVE ECUs
Geetishree Mishra1
, Maanasa RamPrasad2
, Ashwin Itagi3
and K S Gurumurthy4
1
BMS College of Engineering, Bangalore, India,
2
ABB India Limited, Bangalore, India
3
CISCO Systems, Bangalore, India
4
Reva Intitute of Technology, Bangalore, India
ABSTRACT:
Task intensive electronic control units (ECUs) in automotive domain, equipped with multicore processors ,
real time operating systems (RTOSs) and various application software, should perform efficiently and time
deterministically. The parallel computational capability offered by this multicore hardware can only be
exploited and utilized if the ECU application software is parallelized. Having provided with such
parallelized software, the real time operating system scheduler component should schedule the time critical
tasks so that, all the computational cores are utilized to a greater extent and the safety critical deadlines
are met. As original equipment manufacturers (OEMs) are always motivated towards adding more
sophisticated features to the existing ECUs, a large number of task sets can be effectively scheduled for
execution within the bounded time limits. In this paper, a hybrid scheduling algorithm has been proposed,
that meticulously calculates the running slack of every task and estimates the probability of meeting
deadline either being in the same partitioned queue or by migrating to another. This algorithm was run and
tested using a scheduling simulator with different real time task models of periodic tasks . This algorithm
was also compared with the existing static priority scheduler, which is suggested by Automotive Open
Systems Architecture (AUTOSAR). The performance parameters considered here are, the % of core
utilization, average response time and task deadline missing rate. It has been verified that, this proposed
algorithm has considerable improvements over the existing partitioned static priority scheduler based on
each performance parameter mentioned above.
KEY WORDS
ECU, Multicore, RTOS, Scheduling, OEM, AUTOSAR.
1.INTRODUCTION
Automotive electronic subsystems are the real time embedded systems expected to work
efficiently at various operating environments adhering to the safety critical requirements and
strict government regulations. Within short regular intervals, new advanced and complex features
are getting added to the existing systems. Original Equipment Manufacturers (OEMs) are
competing with each other to introduce more sophisticated applications to their automobiles [1].
Multicore processors are the driving architectures for these new evolutions in automotive domain
[2]. In this context, Automotive Open System Architecture (AUTOSAR) supported OEMs to add
2. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
2
more functionality into the existing ECUs and shift out from one function per ECU paradigm.
AUTOSAR suggested more centralized software architecture designs with appropriate protection
mechanisms. Due to this, high computational capabilities were expected from the ECU processor.
For several years there has been a steady rise in the amount of computational power required
from an ECU with architectural changes in the hardware and by increases in clock rate. But
increase in clock rate also increases power consumption and also creates complex
electromagnetic compatibility (EMC) issues. High performance and low power consumption
being the two classic, conflicting requirements of any embedded system design, further frequency
scaling the processor for achieving high performance was not a viable option any more. In this
consequence, multicore processors were introduced in ECUs as a cost effective solution to
achieve greater performance at moderate clock speed and low power consumption [2]. Because of
multicore implementation, the ECUs are computationally more capable of accommodating much
functionality which is an opportunity for OEMs to reduce the number of ECUs, the complex
network connections and communication buses. Multicore ECUs bring major improvements for
some applications that require high performance such as high-end engine controllers, electric and
hybrid powertrains and advanced driver assistance systems which sometimes involve realtime
image processing [1]. The inherent parallelism offered by multicore platforms helps in
segregating the safety critical functions and allocating to dedicated cores. The challenges lie in
the software architecture design which is also required to be parallelized to exploit the maximum
capability of parallelized hardware. Decomposition of the application tasks and interrupt handlers
into independent threads is required to achieve parallel execution by multiple cores [3,4]. To
optimize the performance of an application running on a multicore platform, real-time CPU
scheduling algorithms play the pivotal role. There are many existing methods and algorithms of
real time task scheduling which are required to be reanalyzed and modified to suit to the parallel
hardware and software architecture [5]. AUTOSAR has suggested strictly partitioned and static
priority scheduling for safety critical tasks in automotive domain but there are issues like
partitioning the tasks and mapping to a fixed core [2]. During heavy load condition, the lower
priority tasks always starve for a computing resource and are sometimes forced to miss their
deadlines. With the motivation of deriving a suitable task scheduling method and algorithm for
multicore ECUs, a hybrid scheduling model and an algorithm have been proposed in this work. A
simulation process with different task models has also been explained. The simulation results
with different performance parameters are demonstrated. The paper is organized as: section II,
introduces the existing task scheduling scenario in multicore automotive ECUs. section III
explains about the proposed scheduler model for a tricore ECU. In section IV the task model and
schedulability conditions are derived., In section V the proposed algorithm is presented, Section
VI provides an illustration on the results and discussions. Section VII presents the performance
analysis and Section VIII gives the conclusion of the work.
2.EXISTING SCHEDULING SCENARIO IN AUTOMOTIVE ECUs
AUTOSAR version 4.0 suggests preemptive static priority and partitioned scheduling for safety
critical tasks on multicore implemented ECUs [2]. In a current implementation, there are three
computational cores of a multicore processor used for the diesel gasoline engine control ECU.
The peripheral core, the performance core and the main core. The peripheral core is mostly a DSP
core, lock step mode is implemented in the main core to protect data consistency and the
performance core which is mostly kept as a support system otherwise used for periodic tasks in
high load conditions.
3. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
3
2.1 Task Partitioning
There are different task distribution strategies used to partition the tasks to achieve deterministic
behaviour of the system [6,7,8]. In one of the strategies, basic system software (BSW) and
complex drivers are allocated to the peripheral core. Crank teeth angle synchronous tasks of
application software (ASW) and monitoring relevant tasks are allocated to the main core [9].
Time triggered tasks of ASW are allocated to the performance core. The preemptive, non-
preemptive and cooperative tasks are segregated and allocated to different cores. Similarly in
another implementation strategy, tasks and interrupt clusters are defined based on the hardware
dependencies, data dependencies and imposed constraints [2,10]. All the tasks in any cluster are
allocated to the same core to be executed together to avoid the communication costs. An example
tasks/interrupts clusters are shown in the fig.1 below. Each task of a task cluster is mapped to an
OS Application. The OS Application is then mapped to a core by OS configuration [11]. Within a
task, there will be a chain of nunnables to be executed sequentially. Various runnable sequencing
algorithms are there in the literature to generate a proper flow of execution, but the performance
evaluation of such distribution is not satisfactory because it is difficult to partition between tasks,
ISRs and BSW with high load conditions [10]. As a result, there is poor load balancing between
the cores in heavy load conditions. Furthermore there cannot be a fixed task distribution strategy
for all the systems as each system has its own software design. There is a need of trade-off
between flexibility and effort in task distribution strategy. In this proposed algorithm, both global
and partitioned queues are used and task migrations are allowed to explore the availability of
computing cores.
2.2 Static priority Scheduling
AUTOSAR suggests static priority cyclic scheduling for the tasks in every partitioned queue [2].
The number of runnables grouped under a task is always large. The runnables under OS services
or application tasks with same periodicity or ISRs of same category are grouped together. In the
static priority scheduling, priorities are assigned to each task prior to run time. Each task is
defined with four parameter values: initial offset, worst case execution time (WCET), its period
and priority. At any releasing instant at the partitioned queue, the higher priority task get
scheduled for execution. So at higher load conditions, low priority tasks do not get a CPU slot
and because of strict partitioning, they are unable to migrate to other core even though the
consequence is missing the deadlines [1,11]. CPU utilization is also not balanced with task
partitioning. So there is a scope for dynamic task scheduling as well [12]. This research work is
intended to address this issue and an effort has been made to develop an efficient hybrid task
scheduling algorithm for a multicore ECU which is tested with various open source scheduling
tools.
4. International Journal of Embedded systems and
3.PROPOSED SCHEDULER MODEL
The scheduler model is characterised by
identical cores and an intermediate module called task distributer.
parameters table and based on each activation
tasks distributer is an interface
decision on which task from the gl
calculated slack and number of tasks
implemented as a scheduler function and runs at every time quantum which is
model and runs on one of the cores
4. TASK MODEL DESCRIPTION
In this paper, we have consider
control ECU that consists of three identical cores.
decomposed into tasks and many processes run within a task. A
whenever appropriate. In an engine control unit
asynchronous or time-triggered tasks, which are activated
engine-triggered tasks, which are activated at a specific angular position of the engine crank shaft
International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June
Fig.1 Task Clusters
PROPOSED SCHEDULER MODEL
is characterised by a global queue, three partitioned queues serving for three
cores and an intermediate module called task distributer. The model refers to a task
and based on each activation instant, the tasks get added to the global queue.
stributer is an interface between the global and the partitioned queues that
from the global queue to move to which local queue based on
and number of tasks at the global queue. The hybrid scheduling
implemented as a scheduler function and runs at every time quantum which is fixed
cores.
Fig.2 Scheduler model
TASK MODEL DESCRIPTION
considered a set of 10 periodic tasks of application software for engine
control ECU that consists of three identical cores. In practice, the application
decomposed into tasks and many processes run within a task. A task is called from the OS kernel
In an engine control unit two types of tasks are executed: t
triggered tasks, which are activated periodically and the synchronous or
triggered tasks, which are activated at a specific angular position of the engine crank shaft
Applications(IJESA) Vol.5, No.2, June 2015
4
a global queue, three partitioned queues serving for three
refers to a task
to the global queue. The
l and the partitioned queues that takes the
queue based on the
scheduling algorithm is
fixed for a task
software for engine
the application software is
called from the OS kernel
two types of tasks are executed: the
periodically and the synchronous or
triggered tasks, which are activated at a specific angular position of the engine crank shaft
5. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
5
[9]. As a consequence the frequency of engine-triggered task arrival varies with the speed of the
engine. Additionally, the execution time of some of the time triggered tasks may also depend on
the speed of the engine. In this paper the proposed hybrid scheduler is tested with three different
task models considering the real time behavior of the engine.
4.1 Task Characteristics
The ith task is characterized by a three tuple Ti = (Ci, Ri, Pi). Quantities Ci, Ri, and Pi
correspond to the worst case execution time (WCET), the releasing instant and the period
[13,14,15]. Subsequent instances of the tasks are released periodically. As shown in fig.3, the
next activation instant of task Ti is Ri+Pi. For all the periodic tasks, deadline is equal to the
period. Slack of a task is the maximum amount of time it can wait in the queue before execution
and still meets its deadline. Slack is calculated for each task and is denoted by Si. Si= Pi – RET;
where RET= remaining execution time of a task. In this paper, slack is the utilized parameter to
find a feasible schedule.
Fig. 3 Task Model
4.1.1Assumptions
In this paper, we have a set of assumptions while creating a task model, considering the
automotive applications.
• Each task is periodically executed strictly. The initial releasing instant of a task can be chosen
freely within its period with the objective of balancing the central processing unit (CPU) load
over a scheduling cycle.
• Periods are chosen as 5ms, 10ms, 20 ms, 50 ms and 100ms for realistic automotive
applications.
• The WCET of tasks are randomly chosen to have a CPU utilization of 0.1 to 0.2 assuming
that, maximum 5 to 10 tasks will be waiting in the queue for execution at the same instant.
• Slack of a task should be integer multiple of its worst case execution time.
• All cores are identical with regard to their processing speed and hardware features. There are
no dependencies between tasks allocated on different cores.
• Tasks are free to migrate across the cores.
4.2 Schedulability Conditions
In this paper, tasks are considered as software components within which a large number of
processes can run in a definite sequential order once the task is scheduled. Intra-task parallelism
is not considered here. Slack Si of a task is the difference between its period and remaining
execution time, is the considered parameter to find out a feasible schedule at each core where all
tasks can meet their deadlines.
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The schedulability conditions are:
• the total CPU utilization µ by all the n tasks should be less than equal to the number of CPU
cores denoted by m [17,18].
µ ≤ m ; where m= number of CPU cores.
= ∑ /
, where n= number of tasks
• Slack of any task Ti should be an integer multiple of its WCETi.
• Slack Si = Pi – RET , where RET= remaining execution time.
• Remaining slack of a task in a local queue should be greater than the sum of remaining
execution time of tasks arranged before it.
5. PROPOSED HYBRID SCHEDULING ALGORITHM
In automotive domain, it is currently the static priority partitioned scheduling used for multicore
ECUs. Tasks are strictly partitioned and listed in ready queues from where they are scheduled for
execution based on their static priority. At any scheduling instant, the highest priority task in a
ready queue runs in the corresponding CPU core. OEMs are following the AUTOSAR
suggestions on this aspect looking into the safety criticality of the tasks [9]. The main drawback
in this approach is, CPU cores are not utilized properly due to strict partitioning of the tasks. Also
at heavy load conditions, low priority tasks are much delayed to get CPU slots for their execution.
In this paper, a hybrid scheduling algorithm has been proposed and tested with three different task
models using a JAVA based simulation tool for real time multiprocessor scheduling. This
algorithm has the features of both global and partitioned scheduling and tasks are allowed to
migrate from one core to other with a probability of meeting their deadlines [6,16,17,18]. Slack is
the utilized parameter in this algorithm. The task with minimum slack has highest priority.
Hybrid Scheduling Algorithm Pseudocode
1 On activate(Evtcontext)
2 {Get(task);
3 Compute(slack); // Slack= Period-WCET
4 Globalqueue.Add(task);}
5 Sort(Globalqueue); // Sort in ascending order of slack
9 OnTick(){
10 Q=quantum; ntick=0;
11 ntick=(ntick+1)%Q;
12 If (ntick==0)
13 {i=true; j=true;}
14 Schedule()
{ if (i) {
slackCompute(Globalqueue);
laxityCompute(Localqueue); // Laxity= Slack-Waiting time
i = false;}
if (j) {
Distribute(task);
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7
Localqueue.Add(task);
Globalqueue.remove(task);
Sort(Localqueue);
j = false;}}
15 Allocate core(task at Lqueuehead);
16 ET=SetET(T.running); // ET=Execution time
17 For(all localqueues){
(for i=0;iLqsize();i++)
{getET(Ti);
getWCET(Ti);
If(ET==WCET)
{Lqueue.remove(Ti);
Core.remove(Ti);
SetET(Ti=0);}}
}
18 For(all localqueues){
If(Trunning != Tqhead){
If(laxity(Tqhead) RET(T.running))
{Preempt();
Sort(Lqueue);}}
}
19 For(all queues){
If(Localqueue1.size () 1){
T=Task.Localqueue1(end);
X=Compute.laxity (T);}
If(Localqueue2.size()==0 | Localqueue3.size()==0)
{Migrate(T);}
If((Localqueue2.size() 0) (Localqueue3.size() 0)){
T1 = Task.Localqueue2(head);
T2 = Task.Localqueue3(head);
Y=Compute.Laxity(T1);
Z= Compute.Laxity(T2);} k-1
If((XCum_RET.Localqueue1) (XY)) // Cum_RET= ∑ RET(Ti); ( k=qlength)
{ Migrate(T); i=1
Sort(Localqueue2);}
Else If((XCum_RET.Localqueue1) (XZ))
{Migrate(T);
Sort(Localqueue3);}}
Fig.4 Hybrid Algorithm
5.1 Task Distribution
The inputs to the task distributor module are the tasks arranged in ascending order of
their slack at a releasing instant. As slack is the maximum delay a task can withstand
before meeting deadline, it is a constant value at task arrival. So based on the task model
under test, the distribution threshold can be set. In this paper, three different automotive
system representative task models are tested, for which distribution logics are suitably
chosen to balance the load as well as to reduce the number of migration [13,19]. If n is no
8. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
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of tasks arranged in ascending order of their slack at a scheduling instant and if n is an
even number, first n/2 tasks to core1, (n-(n/2))/2 to core2 and remaining tasks distributed
to core3 while if n is an odd number, (n+1)/2 to core1,( n-(n+1)/2)/2 to core 2 and
remaining tasks distributed to core 3. As the number of tasks released and their slacks
are independent of each other, in this paper, only number of tasks is considered for task
distributions.
5.2 Task Migration
Even though tasks are passed on to the partitioned queues, there are no strict characteristics of
tasks and CPU cores to be mapped on one to one. So tasks migration is possible from one queue
to other to meet deadlines [20,21]. Migration of tasks is allowed when tasks are going to miss
their deadlines waiting in the corresponding queue and there is a probability of meeting deadlines
at other queue. Probability of missing deadline in a queue is checked starting from the task at the
tail of the queue. If the remaining slack called laxity of the task at the end of the queue is less than
the sum of the remaining execution time (RET) of all the tasks waiting in the queue, the migration
logic searches for a probability at other queues. As the task distribution logic is so chosen that,
more number of tasks with less slacks get accumulated at local queue1, the tasks in this queue
look for a migration to other two queues. If the remaining slack of the task at the end of queue1 is
less than the same for task at queue head of either of the other queues, then a migration happens
from queue1 to queue2 or queue3. In the consequence when queue2 or queue3 is empty, a
migration occurs without any slack comparison from more populated queue to the empty queue.
In brief these can be presented as:
• When, ∑ RET(Ti)
, where Sk= remaining slack of ‘k’th task at the tail of queue1,
If Sk Sm | Sk Sl , where Sm and Sl are remaining slacks at the head of
queue2 and queue3 respectively, then task Tk migrates to queue2 or queue3.
• Remaining slack (Ti) = Si - Wt
• Wt (waiting time) = Pt-Ri-ET ; where, Pt=present time (System_tic) Ri = release time of
ith task and ET= execution time
6. RESULTS AND DISCUSSIONS
In this paper, the proposed hybrid scheduling algorithm is run and tested on a tricore processor
for three different task models, where each model has ten numbers of tasks (m=3 and n=10)
denoted as T1 to T10 defined with the parameter attributes defined and explained in section 4.1.
Then the currently used partitioned static priority scheduling algorithm was run for the same task
models to compare the performances based on core utilization, average response time and missed
deadline. The task models used in this paper are the representative task models of automotive
applications. To run and validate the algorithms, the JAVA based STORM tool is used which is a
scheduling simulator that gives a Gantt chart for each core as the result. The simulator kernel uses
an XML file as input, where all the task parameters, the CPU cores, the scheduler class name, the
scheduling quantum and also the simulation duration are defined. For clear visibility of results, in
this work, simulation duration is taken as 50 ms of CPU time and quantum as 1ms.
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6.1 Partition Static Priority Scheduler
Fig.5 Result Gantt Chart for partition static priority algorithm
The result Gantt charts for a periodic task model for the schedule using partition static priority
algorithm are shown in Fig.5. The task model used for simulation has tasks with 5ms, 10ms and
20ms periods. Due to strict partitioning of the tasks, those are with 5ms and 10ms periods
allocated to core1 and tasks with 20 ms period are allocated to core2. So from the used task
model, seven numbers of tasks are intended for execution in core1 and three numbers of tasks in
core2. It can be clearly observed from core1 Gantt chart that, only higher priority tasks T3 and T7
are getting scheduled at the beginning and after a threshold number of times, medium priority
tasks T1 and T5 are scheduled with high response time. The low priority tasks T2, T4 and T8 are
not getting a schedule slot for execution and are missing deadlines. As only three tasks are
allocated to core2, all are meeting their deadlines and core utilization is also very less. Since no
partition for core3, it is completely idle and utilization of the core is zero.
6.2 Hybrid Scheduler
Fig.6 Result Gantt Chart for hybrid scheduling algorithm
The result Gantt charts for the same periodic task model for the schedule using the proposed
hybrid scheduling algorithm are shown in Fig.6. As the result widows show, core 1 is fully
occupied with task T3 and T7 which have least slack and so highest priorities. At every task
releasing instant, after sorting of the tasks in ascending order of their slack at the global queue,
the task distribution logic passes atleast 50% of the tasks to local queue1 and as a result, it gets
10. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
10
more populated. With the help of its migration logic, the scheduler gets the tasks at the tail of the
queue, moved to other queues with the probability of meeting deadlines. The task model used
here has total utilization equal to 2.2 which is less than m= 3, the number of CPU cores. As per
the schedulability conditions of the algorithm, it can give a feasible schedule if total utilization of
the tasks, µ ≤ m. Since the considered task model is relaxed in this respect, the hybrid scheduler
gives a feasible schedule for all the tasks and also some idle time slots appear on core2 and core3
Gantt chart. Where partition static priority scheduler does not give a feasible schedule for the task
model even with less utilization factor, the proposed hybrid scheduler is able to schedule
efficiently with no deadline missing.
7. PERFORMANCE ANALYSIS
In this paper, the parameters considered for comparing the performance of proposed hybrid
scheduler with the existing partition static priority scheduler are: the percentage of CPU core
utilization, average response time for the scheduled tasks, number of missing deadlines, migration
and preemption overheads. The performance of the proposed algorithm based on each parameter
for each task model is discussed here in the subsections.
7.1 CPU utilization
CPU Utilization
Partition Static Priority Scheduler Hybrid Scheduler
Task Model Core1 Core2 Core3 Core1 Core2 Core3
M1 82% 80% 0 44% 84% 48%
M2 86% 90% 0 82% 56% 54%
M3 100% 28% 0 100% 62% 64%
Table.1 CPU Utilization
The total CPU utilization required by a task model is:
= ∑ /
, where n=10
For task model 1, utilization µ is 1.6, for task model 2, µ is 1.75 and for task model 3, it is 2.2. As
each task model has run with both the partition and hybrid scheduling algorithms, each has
different utilization of CPU cores based on their scheduling strategies. Table 1 gives the
utilization percentage of each core for each task model tested on both the algorithms. Utilization
of each core is calculated for the simulation duration which is taken as 50ms. As the WCET of
each task is taken as an integer value, fractional utilization is not considered. Utilization of each
core is the sum of execution time divided by the simulation time. As it is clearly shown in the
table 1, for partition static priority scheduling, there is load imbalance across the three cores and
no utilization of core3. It happens because of partitioning criteria based on periodicity and
interdependency of tasks due to which, in each task model, no task is scheduled for core3. In
contrast, in hybrid scheduler, all the three cores are utilized as migration of tasks is allowed
across cores and work load is distributed among the cores which can be seen in the result Gantt
charts in Fig.6. The comparison of percentage of core utilization on three CPU cores for three
task models is shown in Fig.7.
11. International Journal of Embedded systems and
Fig.7 Comparison of percentage of core utilization on three models
7.2 Average response time
Task Models Partition Static Priority Scheduler
M1
M2
M3 3ms
The response time for each scheduled task within the
task from its release till completion that is:
Response time (Rt) = ET+WT, where ET= execution time and WT= waiting time
Average response time is the sum of response times of all scheduled tasks divided
number of tasks scheduled that is:
Average response time
It can be observed from table 2 that,
tasks scheduled by hybrid algorithm
For task model 1, the improvement
3, higher priorities are assigned to high frequency 5ms tasks. So
not schedule for five low priority tasks
scheduled tasks. For this task model also there is an improvement in response t
hybrid scheduler. The comparison
with both the algorithms is shown in Fig.8.
0%
20%
40%
60%
80%
100%
Core1
Partition Static
Priority Scheduler
International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June
Comparison of percentage of core utilization on three models
Average Response Time
Partition Static Priority Scheduler Hybrid Scheduler
5.1ms 3.2ms
5.7ms 2.7ms
ms with 5 tasks scheduled 3.1ms
Table.2 Average Response Time
The response time for each scheduled task within the simulation duration is the time spent by a
task from its release till completion that is:
= ET+WT, where ET= execution time and WT= waiting time
Average response time is the sum of response times of all scheduled tasks divided
number of tasks scheduled that is:
time ∑ %
/', Where n= no of scheduled tasks [15]
It can be observed from table 2 that, there is a considerable improvement in response time
tasks scheduled by hybrid algorithm over the partitioned algorithm for the task models 1 and 2.
For task model 1, the improvement is of 1.9ms and for task model 2, it is of 3 ms. In
higher priorities are assigned to high frequency 5ms tasks. So partitioned static scheduler cou
low priority tasks due to which, response time is calculated only for five
scheduled tasks. For this task model also there is an improvement in response t
The comparison of average response time of tasks for the three task models
is shown in Fig.8.
Core1 Core2 Core3 Core1 Core2 Core3
Partition Static
Priority Scheduler
Hybrid Scheduler
M1
M2
M3
Applications(IJESA) Vol.5, No.2, June 2015
11
Hybrid Scheduler
simulation duration is the time spent by a
= ET+WT, where ET= execution time and WT= waiting time
Average response time is the sum of response times of all scheduled tasks divided by the
[15].
there is a considerable improvement in response time of
for the task models 1 and 2.
In task model
partitioned static scheduler could
which, response time is calculated only for five
scheduled tasks. For this task model also there is an improvement in response time with the
ks for the three task models
12. International Journal of Embedded systems and
Fig.8
7.3 Task deadline missing rate
Deadline of a task is not assigned as a parameter in the task model. Since all the tasks considered
are periodic, deadline of a task is
algorithms, the number of tasks missing their deadlines
duration was observed. It was observed that, as the total utilization of the CPU core exceeds 60%
for a task model, for partitioned st
deadlines or do not get scheduled. As the task model 3 has the utilization of 2.2, five of the tasks
miss their deadlines, as a remedial measure
be restricted to the range of 0.1≤
and ‘i’ varies from 1 to n. The number of tasks partitioned for a core should be
maximum 60 % work load. Experiments show that
feasible schedule for the task model
deadline missing rate is 20-40% of the waiting task as the utilization goes beyond 80%.
7.4. Migration and Preemption Overheads
As task migration is allowed in hybrid scheduling algorithm
utilize a comparatively less loaded core, it depends on the number of tasks get added to a local
ready queue by the distribution logic and the slack of the task at the end of a queue
ascending order. Since these parameters are
migrations cannot be derived. In this work, based on the task models, maximum number of task
released at any instant is four. So maximum
be total three tasks waiting in the
of the queue and looking at the load at
Since these task migrations happen prior to execution time, preemption of tasks is reduced
compared to the partitioned scheduling approach. As preemption incurs context switch overhead,
cost of preemption overhead is always more than migration overhead
algorithm implementing task migration logic has
scheduling.
0
2
4
6
M1
International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June
Fig.8 Average response time comparison
rate
Deadline of a task is not assigned as a parameter in the task model. Since all the tasks considered
are periodic, deadline of a task is assumed as equal to its period. For each of the scheduling
number of tasks missing their deadlines on every task model within the simulation
It was observed that, as the total utilization of the CPU core exceeds 60%
for a task model, for partitioned static priority scheduling, lower priority tasks miss their
deadlines or do not get scheduled. As the task model 3 has the utilization of 2.2, five of the tasks
, as a remedial measure for which, the average utilization of the tasks
≤ [1/n( ∑ Ci/Pi)] ≤ 0.2 where n is the number of tasks under test
he number of tasks partitioned for a core should be restricted
Experiments show that, hybrid scheduling algorithm provi
task models with 80% utilization of each core at any given time
40% of the waiting task as the utilization goes beyond 80%.
reemption Overheads
in hybrid scheduling algorithm either to meet the deadline or to
utilize a comparatively less loaded core, it depends on the number of tasks get added to a local
ready queue by the distribution logic and the slack of the task at the end of a queue after so
Since these parameters are independent of each other, a definite number of task
In this work, based on the task models, maximum number of task
released at any instant is four. So maximum two tasks could be added to a queue and there could
in the queue. In this consequence, after comparison of slack at the end
looking at the load at other cores, maximum 30% of the tasks could migrate
rations happen prior to execution time, preemption of tasks is reduced
compared to the partitioned scheduling approach. As preemption incurs context switch overhead,
cost of preemption overhead is always more than migration overhead. So hybrid scheduling
lgorithm implementing task migration logic has response time improvements over partitioned
M2 M3
Avg Rt (ms) Partition
Avg Rt (ms) Hybrid
Applications(IJESA) Vol.5, No.2, June 2015
12
Deadline of a task is not assigned as a parameter in the task model. Since all the tasks considered
For each of the scheduling
within the simulation
It was observed that, as the total utilization of the CPU core exceeds 60%
priority tasks miss their
deadlines or do not get scheduled. As the task model 3 has the utilization of 2.2, five of the tasks
utilization of the tasks should
0.2 where n is the number of tasks under test
restricted to have
, hybrid scheduling algorithm provides a
at any given time and
40% of the waiting task as the utilization goes beyond 80%.
either to meet the deadline or to
utilize a comparatively less loaded core, it depends on the number of tasks get added to a local
after sorting in
independent of each other, a definite number of task
In this work, based on the task models, maximum number of task
added to a queue and there could
queue. In this consequence, after comparison of slack at the end
tasks could migrate.
rations happen prior to execution time, preemption of tasks is reduced
compared to the partitioned scheduling approach. As preemption incurs context switch overhead,
. So hybrid scheduling
improvements over partitioned
13. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
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8.CONCLUSION
In this paper, a hybrid scheduling algorithm is proposed along with a scheduler model for
multicore automotive ECUs. The existing algorithm used in this domain is the partition static
priority scheduling wherein one core is utilized upto 60% of its capabilities and others remain idle
in normal working condition of the vehicle. When load increases, it gets transferred to other
cores. So two cores are underutilized in normal running conditions. In this paper, both the
algorithms have been tested for three task models, each comprising of ten numbers of tasks
representatives of Engine Control ECU functionalities. It has been verified that, this proposed
algorithm has considerable improvements over the existing partitioned static priority scheduler
based on the performance parameters such as: CPU core utilization, average response time of
tasks and deadline missing rate. Each of these parameters is calculated based on theoretical
knowledge. The main motive behind developing this hybrid scheduling algorithm was to
distribute the tasks among the available cores instead of strict partitioning according to the task
periods and executing at one core all through in normal working conditions of the vehicle. In the
proposed algorithm, tasks were allowed to migrate from one queue to another, hence able to
reduce the response time and meet the deadlines. As all the cores share the workload, higher
utilization of the cores has been achieved when the work load increases. The algorithm is yet to
be tested in different contingency conditions, could be considered as the future work.
9.REFERENCES
[1] ”Multicore Scheduling in Automotive ECUs” By Aurelien Monot, Nicolas Navet, Francoise Simonot,
Bernard Bavoux, Embedded Real Time Software and Systems- ERTSS May 2010.
[2] AUTOSAR version 4.2.1, www.autosar.org
[3] “Parallel Real Time Scheduling of DAGs”, By Saifullah A; Ferry D; Jing Li; Agarwal K, Parallel
and Distributed Systems, IEEE Transactions. Jan 2014.
[4] “Improvement of Real time Multicore Schedulability with forced Non Preemption.” By Jinkyu Lee;
Shin K G , Parallel and Distributed Systems, IEEE Transactions. Jan 2014.
[5] “A High utilization Scheduling scheme of stream programs on clustered VLIW stream
Architectures.” By Guoyue Jiang; Zhaolin Li; Fang Wang; Shaojun Wei, Parallel and Distributed
Systems, IEEE Transactions, March 2013.
[6] “Dynamic Scheduling for Emergency Tasks on Distributed Imaging Satellites with Task Merging.”
By Jianjiang Wang; Xiaomin Zhu; Dishan Qiu; Yang L T, Parallel and Distributed Systems, IEEE
Transactions, June 2013.
[7] “Harmonic Aware Multicore Scheduling for fixed priority real time systems.” By Ming Fan; Geng
Quan, Parallel and Distributed Systems, IEEE Transactions. March 2013
[8] “Understanding Multitask Schedulability in Duty-Cycling Sensor Networks.” By Mo li; Zhenjang Li;
Longfei Shanguan; Shaojie Tang; et al, Parallel and Distributed Systems, IEEE Transactions. March
2013.
[9] ”A Case Study in Embedded Systems Design: An Engine Control Unit” , By Tullio Cuatto, Claudio
Passerone, Claudio Sanso E, Francesco Gregoretti, Attila JuresKa, Alberto Sangiovanni, Design
Automation for Embedded Systems, Vol, 6, 2000.
[10] “An Efficient Hierarchical Scheduling Framework for the Automotive Domain” By Mike
Holenderski, Reinder J. Bril and Johan J. Lukkien, Real-Time Systems, Architecture, Scheduling, and
Application, www.intechopen.com.
[11] N. Navet, A. Monot, B. Bavoux, and F. Simonot-Lion. “Multi-source and multicore automotive ecus:
Os protection mechanisms and scheduling”. In Proc. of IEEE Int’l Symposium on Industrial
Electronics, Jul. 2010.
14. International Journal of Embedded systems and Applications(IJESA) Vol.5, No.2, June 2015
14
[12] “Dynamic Task Scheduling on Multicore Automotive ECUs” By Geetishree Mishra, K S
Gurumurthy, International Journal of VLSI design Communication Systems (VLSICS) Vol.5,
No.6, December 2014.
[13] “Hyperbolic Utilization Bounds for Rate Monotonic Scheduling on Homogeneous Multiprocessors.”
By Hongya Wang; Lihchyun Shu; Wei Yin; Yingyuan Xiao, Jiao Cao ,Parallel and Distributed
Systems, IEEE Transactions. August 2013
[14] “Real Time Scheduling Theory: A Historical Perspective” By Lui Sha, Tarek Abdelzaher, Karl Erik
Arzen, Anton Cervin, Theodore Baker, Alan Burns, Giorgio Buttazzo, Marco Caccamo, John
Lehoczky, Aloysios K.Mok; Real-Time Systems , Vol 28, PP-101-155, 2004.
[15] “Real-Time Scheduling Models: an Experimental Approach” By Antonio J Pessoa de Magalhaes,
Carlos J.A. Costa, Technical report Controlo Nov 2000.
[16] ”Scheduling Hard Real-Time Systems: A Review” By A Burns- Software Engineering Journal, Vol 6,
May1991, DOI: 10.1049/sej.1991.0015 IET.
[17] “Task Scheduling of Real-time Systems on Multi-Core Architectures” By Pengliu Tan, 2009 Second
International Symposium on Electronic Commerce and Security IEEE, DOI
10.1109/ISECS.2009.161.
[18] ”Demand-based Schedulability Analysis for Real Time Multicore Scheduling” By Jinkyu Lee, Insik
Shin, Journal of Systems and Software ELSEVIER October 2013.
[19] “An Experimental Comparison of Real Time Schedulers on Multicore Systems” By Juri lelli, Dario
Faggioli, Tommaso Cusinotta, Giuseppe Lipari, Journal of Systems and Software ELSEVIER June
2012.
[20] “Load-prediction Scheduling Algorithm for Computer Simulation of Electrocardiogram in Hybrid
Environments” By Wenfeng Shen, Zhaokai Luo, Daming Wei, Weimin Xu, Xin Zhu, Journal of
Systems and Software ELSEVIER January 2015.
[21] “Performance Analysis of EDF Scheduling in a Multi priority Preemptive M/G/1 Queue” By Gamini
Abhaya V; Tari Z; Zeeplongsekul P; Zomaya A Y , Parallel and Distributed Systems, IEEE
Transactions. July 2013.