This document proposes algorithms for dynamic task scheduling on a DVS system to minimize total system energy consumption. It presents two algorithms:
1) duSYS, which uses optimal speed setting and limited preemption to reduce device standby energy.
2) duSYS_PC, which further reduces preemptions compared to duSYS to achieve additional energy savings.
The algorithms achieve up to 43% energy savings compared to prior work and up to 30% savings compared to a CPU-energy efficient algorithm, showing their effectiveness in minimizing total system energy.
DYNAMIC VOLTAGE SCALING FOR POWER CONSUMPTION REDUCTION IN REAL-TIME MIXED TA...cscpconf
The reduction in energy consumption without any deadline miss is one of the main challenges in real-time embedded systems. Dynamic voltage scaling (DVS) is a technique that reduces the power consumption of processors by utilizing various operating points provided to the DVS processor. These operating points consist of pairs of voltage and frequency. The selection of operating points can be done based on the load to the system at a particular point of time. In this work DVS is applied to both periodic and sporadic tasks, and an average of 40% of energy is reduced. The energy consumption of the processor is further reduced by 2-10% by reducing the number of pre-emption and frequency switching
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.
An Improved Particle Swarm Optimization for Proficient Solving of Unit Commit...IDES Editor
This paper presents a new approach to solving the
short-term unit commitment problem using an improved
Particle Swarm Optimization (IPSO). The objective of this
paper is to find the generation scheduling such that the total
operating cost can be minimized, when subjected to a variety
of constraints. This also means that it is desirable to find the
optimal generating unit commitment in the power system for
the next H hours. PSO, which happens to be a Global
Optimization technique for solving Unit Commitment
Problem, operates on a system, which is designed to encode
each unit’s operating schedule with regard to its minimum
up/down time. In this, the unit commitment schedule is coded
as a string of symbols. An initial population of parent
solutions is generated at random. Here, each schedule is
formed by committing all the units according to their initial
status (“flat start”). Here the parents are obtained from a predefined
set of solution’s i.e. each and every solution is adjusted
to meet the requirements. Then, a random decommitment is
carried out with respect to the unit’s minimum down times. A
thermal Power System in India demonstrates the effectiveness
of the proposed approach; extensive studies have also been
performed for different power systems consist of 10, 26, 34
generating units. Numerical results are shown comparing the
cost solutions and computation time obtained by using the
IPSO and other conventional methods like Dynamic
Programming (DP), Legrangian Relaxation (LR) in reaching
proper unit commitment.
Battery Aware Dynamic Scheduling for Periodic Task GraphsNicolas Navet
V. Rao, N. Navet, G. Singhal, A. Kumar, G.S. Visweswaran, "Battery Aware Dynamic Scheduling for Periodic Task Graphs", Proc. of the 14th International Workshop on Parallel and Distributed Real-Time Systems (WPDRTS 2006), Island of Rhodes, Greece, April 25-26, 2006.
A Framework and Methods for Dynamic Scheduling of a Directed Acyclic Graph on...IDES Editor
The data flow model is gaining popularity as a
programming paradigm for multi-core processors. Efficient
scheduling of an application modeled by Directed Acyclic
Graph (DAG) is a key issue when performance is very
important. DAG represents computational solutions, in which
the nodes represent tasks to be executed and edges represent
precedence constraints among the tasks. The task scheduling
problem in general is a NP-complete problem[2]. Several static
scheduling heuristics have been proposed. But the major
problem in static list scheduling is the inherent difficulty in
exact estimation of task cost and edge cost in a DAG and also
its inability to consider and manage with runtime behavior of
tasks. This underlines the need for dynamic scheduling of a
DAG. This paper presents how in general, dynamic scheduling
of a DAG can be done. Also proposes 4 simple methods to
perform dynamic scheduling of a DAG. These methods have
been simulated and experimented using a representative set
of DAG structured computations from both synthetic and real
problems. The proposed dynamic scheduler performance is
found to be in comparable with that of static scheduling
methods. The performance comparison of the proposed
dynamic scheduling methods is also carried out.
A Study on Task Scheduling in Could Data Centers for Energy Efficacy Ehsan Sharifi
Abstract: The increasing energy consumption of Physical Machines (PM) in cloud data centers is nowadays a major problem, it has a negative impact on the environment while at the same time increasing the operational costs of data centers. This fosters the development of more energy-efficient scheduling approaches. In this study, we study the barriers of knowledge in energy efficiency for cloud data centers.
DYNAMIC VOLTAGE SCALING FOR POWER CONSUMPTION REDUCTION IN REAL-TIME MIXED TA...cscpconf
The reduction in energy consumption without any deadline miss is one of the main challenges in real-time embedded systems. Dynamic voltage scaling (DVS) is a technique that reduces the power consumption of processors by utilizing various operating points provided to the DVS processor. These operating points consist of pairs of voltage and frequency. The selection of operating points can be done based on the load to the system at a particular point of time. In this work DVS is applied to both periodic and sporadic tasks, and an average of 40% of energy is reduced. The energy consumption of the processor is further reduced by 2-10% by reducing the number of pre-emption and frequency switching
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.
An Improved Particle Swarm Optimization for Proficient Solving of Unit Commit...IDES Editor
This paper presents a new approach to solving the
short-term unit commitment problem using an improved
Particle Swarm Optimization (IPSO). The objective of this
paper is to find the generation scheduling such that the total
operating cost can be minimized, when subjected to a variety
of constraints. This also means that it is desirable to find the
optimal generating unit commitment in the power system for
the next H hours. PSO, which happens to be a Global
Optimization technique for solving Unit Commitment
Problem, operates on a system, which is designed to encode
each unit’s operating schedule with regard to its minimum
up/down time. In this, the unit commitment schedule is coded
as a string of symbols. An initial population of parent
solutions is generated at random. Here, each schedule is
formed by committing all the units according to their initial
status (“flat start”). Here the parents are obtained from a predefined
set of solution’s i.e. each and every solution is adjusted
to meet the requirements. Then, a random decommitment is
carried out with respect to the unit’s minimum down times. A
thermal Power System in India demonstrates the effectiveness
of the proposed approach; extensive studies have also been
performed for different power systems consist of 10, 26, 34
generating units. Numerical results are shown comparing the
cost solutions and computation time obtained by using the
IPSO and other conventional methods like Dynamic
Programming (DP), Legrangian Relaxation (LR) in reaching
proper unit commitment.
Battery Aware Dynamic Scheduling for Periodic Task GraphsNicolas Navet
V. Rao, N. Navet, G. Singhal, A. Kumar, G.S. Visweswaran, "Battery Aware Dynamic Scheduling for Periodic Task Graphs", Proc. of the 14th International Workshop on Parallel and Distributed Real-Time Systems (WPDRTS 2006), Island of Rhodes, Greece, April 25-26, 2006.
A Framework and Methods for Dynamic Scheduling of a Directed Acyclic Graph on...IDES Editor
The data flow model is gaining popularity as a
programming paradigm for multi-core processors. Efficient
scheduling of an application modeled by Directed Acyclic
Graph (DAG) is a key issue when performance is very
important. DAG represents computational solutions, in which
the nodes represent tasks to be executed and edges represent
precedence constraints among the tasks. The task scheduling
problem in general is a NP-complete problem[2]. Several static
scheduling heuristics have been proposed. But the major
problem in static list scheduling is the inherent difficulty in
exact estimation of task cost and edge cost in a DAG and also
its inability to consider and manage with runtime behavior of
tasks. This underlines the need for dynamic scheduling of a
DAG. This paper presents how in general, dynamic scheduling
of a DAG can be done. Also proposes 4 simple methods to
perform dynamic scheduling of a DAG. These methods have
been simulated and experimented using a representative set
of DAG structured computations from both synthetic and real
problems. The proposed dynamic scheduler performance is
found to be in comparable with that of static scheduling
methods. The performance comparison of the proposed
dynamic scheduling methods is also carried out.
A Study on Task Scheduling in Could Data Centers for Energy Efficacy Ehsan Sharifi
Abstract: The increasing energy consumption of Physical Machines (PM) in cloud data centers is nowadays a major problem, it has a negative impact on the environment while at the same time increasing the operational costs of data centers. This fosters the development of more energy-efficient scheduling approaches. In this study, we study the barriers of knowledge in energy efficiency for cloud data centers.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
COMPARISON OF LABVIEW WITH SAP2000 AND NONLIN FOR STRUCTURAL DYNAMICS PROBLEMSIAEME Publication
Structural dynamics is a module of structural analysis that encapsulates the behavior of
structures subjected to dynamic loadings. The problems of structural dynamics can be
categorized as SDOF, MDOF or Continuous systems. LabVIEW is simulating software
possessing extensive graphical representation and data acquisition capabilities. It is of
particular interest to the engineers working in the general area of structural health
monitoring. The graphical user interface and the capability to acquire & process real time
data makes it the ideal choice of programming language. In this study, an attempt has been
made to develop a dynamic system using a SDOF model in LabVIEW. The results were
compared with NONLIN program and SAP2000 software. Parametric study has been done
using Newmark β method and the results were plotted for accelerations, velocities and
displacements.
Max Min Fair Scheduling Algorithm using In Grid Scheduling with Load Balancing IJORCS
This paper shows the importance of fair scheduling in grid environment such that all the tasks get equal amount of time for their execution such that it will not lead to starvation. The load balancing of the available resources in the computational grid is another important factor. This paper considers uniform load to be given to the resources. In order to achieve this, load balancing is applied after scheduling the jobs. It also considers the Execution Cost and Bandwidth Cost for the algorithms used here because in a grid environment, the resources are geographically distributed. The implementation of this approach the proposed algorithm reaches optimal solution and minimizes the make span as well as the execution cost and bandwidth cost.
Classification of Virtualization Environment for Cloud ComputingSouvik Pal
Cloud Computing is a relatively new field gaining more popularity day by day for ramified applications among the Internet users.
Virtualization plays a significant role for managing and coordinating the access from the resource pool to multiple virtual machines on
which multiple heterogeneous applications are running. Various virtualization methodologies are of significant importance because it
helps to overcome the complex workloads, frequent application patching and updating, and multiple software architecture. Although
a lot of research and study has been conducted on virtualization, a range of issues involved have mostly been presented in isolation
of each other. Therefore, we have made an attempt to present a comprehensive survey study of different aspects of virtualization. We
present our classification of virtualization methodologies and their brief explanation, based on their working principle and underlying
features.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
Study of Compensation of Variable Delay in Communication Link Using Communica...ijsrd.com
With growing technology, number of control system elements is increasing. So, it is not possible to place entire control system at a same place. Therefore, separate control elements connected by a communication link are required, it introduces delay. This delay is either constant or random in nature depending on communication link. This delay destabilizes the overall system and can be compensated using smith predictor. But smith predictor is only applicable to constant delay communication links. In this paper, communication disturbance observer (CDOB) and network disturbance (ND) have been introduced to compensate variable delay in communication link.
Embedded Linux Conference 2013
https://github.com/ystk/sched-deadline/tree/dlmiss-detection-dev
Real-time system need to meet deadline. In this point of view, the system is required two functions to have determinism. One is interrupt latency stabilization and the other one is processing time reservation. SCHED_DEADLINE has a feature to reserve CPU time in advance to ensure predictable behavior. However there is a lack of feature to control deadline missed processes.
In this presentation, we would like to discuss the requirement for the feature and also show a sample implementation to control deadline missed processes.
Temporal workload analysis and its application to power aware schedulingijesajournal
Power
-
aware scheduling reduces CPU energy consumption in hard real
-
time systems through dynamic
voltage scaling(DVS). The basic idea of power
-
aware scheduling
is to find slacks available to tasks and
reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal
workload of a system which specifies how much busy its CPU is to complete the tasks at current time.
Analyzin
g temporal workload provides a sufficient condition of schedulability of preemptive early
-
deadline
first scheduling and an effective method to identify and distribute slacks generated by early completed
tasks. The simulation results show that proposed algo
rithm reduces the energy consumption by 10
-
70%
over the existing algorithm and its algorithm complexity is O(n). So, practical on
-
line scheduler could be
devised using the proposed algorithm.
Temporal workload analysis and its application to power aware schedulingijesajournal
Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling(DVS). The basic idea of power-aware scheduling is to find slacks available to tasks and reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal workload of a system which specifies how much busy its CPU is to complete the tasks at current time. Analyzing temporal workload provides a sufficient condition of schedulability of preemptive early-deadline first scheduling and an effective method to identify and distribute slacks generated by early completed tasks. The simulation results show that proposed algorithm reduces the energy consumption by 10-70% over the existing algorithm and its algorithm complexity is O(n). So, practical on-line scheduler could be devised using the proposed algorithm.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...Naoki Shibata
Shohei Gotoda, Naoki Shibata and Minoru Ito : "Task scheduling algorithm for multicore processor system for minimizing recovery time in case of single node fault," Proceedings of IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2012), pp.260-267, DOI:10.1109/CCGrid.2012.23, May 15, 2012.
In this paper, we propose a task scheduling al-gorithm for a multicore processor system which reduces the
recovery time in case of a single fail-stop failure of a multicore
processor. Many of the recently developed processors have
multiple cores on a single die, so that one failure of a computing
node results in failure of many processors. In the case of a failure
of a multicore processor, all tasks which have been executed
on the failed multicore processor have to be recovered at once.
The proposed algorithm is based on an existing checkpointing
technique, and we assume that the state is saved when nodes
send results to the next node. If a series of computations that
depends on former results is executed on a single die, we need
to execute all parts of the series of computations again in
the case of failure of the processor. The proposed scheduling
algorithm tries not to concentrate tasks to processors on a die.
We designed our algorithm as a parallel algorithm that achieves
O(n) speedup where n is the number of processors. We evaluated
our method using simulations and experiments with four PCs.
We compared our method with existing scheduling method, and
in the simulation, the execution time including recovery time in
the case of a node failure is reduced by up to 50% while the
overhead in the case of no failure was a few percent in typical
scenarios.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
COMPARISON OF LABVIEW WITH SAP2000 AND NONLIN FOR STRUCTURAL DYNAMICS PROBLEMSIAEME Publication
Structural dynamics is a module of structural analysis that encapsulates the behavior of
structures subjected to dynamic loadings. The problems of structural dynamics can be
categorized as SDOF, MDOF or Continuous systems. LabVIEW is simulating software
possessing extensive graphical representation and data acquisition capabilities. It is of
particular interest to the engineers working in the general area of structural health
monitoring. The graphical user interface and the capability to acquire & process real time
data makes it the ideal choice of programming language. In this study, an attempt has been
made to develop a dynamic system using a SDOF model in LabVIEW. The results were
compared with NONLIN program and SAP2000 software. Parametric study has been done
using Newmark β method and the results were plotted for accelerations, velocities and
displacements.
Max Min Fair Scheduling Algorithm using In Grid Scheduling with Load Balancing IJORCS
This paper shows the importance of fair scheduling in grid environment such that all the tasks get equal amount of time for their execution such that it will not lead to starvation. The load balancing of the available resources in the computational grid is another important factor. This paper considers uniform load to be given to the resources. In order to achieve this, load balancing is applied after scheduling the jobs. It also considers the Execution Cost and Bandwidth Cost for the algorithms used here because in a grid environment, the resources are geographically distributed. The implementation of this approach the proposed algorithm reaches optimal solution and minimizes the make span as well as the execution cost and bandwidth cost.
Classification of Virtualization Environment for Cloud ComputingSouvik Pal
Cloud Computing is a relatively new field gaining more popularity day by day for ramified applications among the Internet users.
Virtualization plays a significant role for managing and coordinating the access from the resource pool to multiple virtual machines on
which multiple heterogeneous applications are running. Various virtualization methodologies are of significant importance because it
helps to overcome the complex workloads, frequent application patching and updating, and multiple software architecture. Although
a lot of research and study has been conducted on virtualization, a range of issues involved have mostly been presented in isolation
of each other. Therefore, we have made an attempt to present a comprehensive survey study of different aspects of virtualization. We
present our classification of virtualization methodologies and their brief explanation, based on their working principle and underlying
features.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
UnaCloud is an opportunistic based cloud infrastructure
(IaaS) that allows to access on-demand computing
capabilities using commodity desktops. Although UnaCloud
tried to maximize the use of idle resources to deploy virtual
machines on them, it does not use energy-efficient resource
allocation algorithms. In this paper, we design and implement
different energy-aware techniques to operate in an energyefficient
way and at the same time guarantee the performance
to the users. Performance tests with different algorithms and
scenarios using real trace workloads from UnaCloud, show how
different policies can change the energy consumption patterns
and reduce the energy consumption in opportunistic cloud
infrastructures. The results show that some algorithms can
reduce the energy-consumption power up to 30% over the
percentage earned by opportunistic environment.
Study of Compensation of Variable Delay in Communication Link Using Communica...ijsrd.com
With growing technology, number of control system elements is increasing. So, it is not possible to place entire control system at a same place. Therefore, separate control elements connected by a communication link are required, it introduces delay. This delay is either constant or random in nature depending on communication link. This delay destabilizes the overall system and can be compensated using smith predictor. But smith predictor is only applicable to constant delay communication links. In this paper, communication disturbance observer (CDOB) and network disturbance (ND) have been introduced to compensate variable delay in communication link.
Embedded Linux Conference 2013
https://github.com/ystk/sched-deadline/tree/dlmiss-detection-dev
Real-time system need to meet deadline. In this point of view, the system is required two functions to have determinism. One is interrupt latency stabilization and the other one is processing time reservation. SCHED_DEADLINE has a feature to reserve CPU time in advance to ensure predictable behavior. However there is a lack of feature to control deadline missed processes.
In this presentation, we would like to discuss the requirement for the feature and also show a sample implementation to control deadline missed processes.
Temporal workload analysis and its application to power aware schedulingijesajournal
Power
-
aware scheduling reduces CPU energy consumption in hard real
-
time systems through dynamic
voltage scaling(DVS). The basic idea of power
-
aware scheduling
is to find slacks available to tasks and
reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal
workload of a system which specifies how much busy its CPU is to complete the tasks at current time.
Analyzin
g temporal workload provides a sufficient condition of schedulability of preemptive early
-
deadline
first scheduling and an effective method to identify and distribute slacks generated by early completed
tasks. The simulation results show that proposed algo
rithm reduces the energy consumption by 10
-
70%
over the existing algorithm and its algorithm complexity is O(n). So, practical on
-
line scheduler could be
devised using the proposed algorithm.
Temporal workload analysis and its application to power aware schedulingijesajournal
Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling(DVS). The basic idea of power-aware scheduling is to find slacks available to tasks and reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal workload of a system which specifies how much busy its CPU is to complete the tasks at current time. Analyzing temporal workload provides a sufficient condition of schedulability of preemptive early-deadline first scheduling and an effective method to identify and distribute slacks generated by early completed tasks. The simulation results show that proposed algorithm reduces the energy consumption by 10-70% over the existing algorithm and its algorithm complexity is O(n). So, practical on-line scheduler could be devised using the proposed algorithm.
A MULTI-OBJECTIVE PERSPECTIVE FOR OPERATOR SCHEDULING USING FINEGRAINED DVS A...VLSICS Design
The stringent power budget of fine grained power managed digital integrated circuits have driven chip designers to optimize power at the cost of area and delay, which were the traditional cost criteria for circuit optimization. The emerging scenario motivates us to revisit the classical operator scheduling problem under the availability of DVFS enabled functional units that can trade-off cycles with power. We study the design space defined due to this trade-off and present a branch-and-bound(B/B) algorithm to explore this state space and report the pareto-optimal front with respect to area and power. The scheduling also aims at maximum resource sharing and is able to attain sufficient area and power gains for complex benchmarks when timing constraints are relaxed by sufficient amount. Experimental results show that the algorithm that operates without any user constraint(area/power) is able to solve the problem for mostavailable benchmarks, and the use of power budget or area budget constraints leads to significant performance gain.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The
increased availability of the cloud models and allied developing models creates easier computing cloud
environment. Energy consumption and effective energy management are the two important challenges in
virtualized computing platforms. Energy consumption can be minimized by allocating computationally
intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling
(DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the
required QoS. However, they do not control the internal and external switching to server frequencies,
which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm
minimizes consumption of energy and time during computation, reconfiguration and communication. Our
proposed model confirms the effectiveness of its implementation, scalability, power consumption and
execution time with respect to other existing approaches.
REAL-TIME ADAPTIVE ENERGY-SCHEDULING ALGORITHM FOR VIRTUALIZED CLOUD COMPUTINGijdpsjournal
Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive EnergyScheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud ComputingVirtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
ENERGY-AWARE DISK STORAGE MANAGEMENT: ONLINE APPROACH WITH APPLICATION IN DBMSijdms
Energy consumption has become a first-class optimization goal in design and implementation of dataintensive
computing systems. This is particularly true in the design of database management systems
(DBMS), which was found to be the major consumer of energy in the software stack of modern data
centers. Among all database components, the storage system is one of the most power-hungry elements. In
previous work, dynamic power management (DPM) techniques that make real-time decisions to transition
the disks to low-power modes are normally used to save energy in storage systems. In this paper, we tackle
the limitations of DPM proposals in previous contributions. We introduced a DPM optimization model
integrated with model predictive control (MPC) strategy to minimize power consumption of the disk-based
storage system while satisfying given performance requirements. It dynamically determines the state of
disks and plans for inter-disk data fragment migration to achieve desirable balance between power
consumption and query response time. Via analyzing our optimization model to identify structural
properties of optimal solutions, we propose a fast-solution heuristic DPM algorithm that can be integrated
in large-scale disk storage systems for efficient state configuration and data migration. We evaluate our
proposed ideas by running simulations using extensive set of synthetic workloads based on popular TPC
benchmarks. Our results show that our solution significantly outperforms the best existing algorithm in
both energy savings and response time.
Low Energy Task Scheduling based on Work StealingLEGATO project
Abstract: Optimizing energy efficiency of parallel execution on computing systems, ranging from server farms, mobile devices to embedded systems, becomes increasingly one of the first-order concerns. A common way to express a parallel application is as a directed acyclic graph (DAG) in which each node represents a task. The problem of such task scheduling on multiprocessor systems is to find the proper execution processors. Especially nowadays asymmetric multiprocessor systems feature different type of cores with different performance and power consumption, e.g. Arm big.LITTLE and Intel Lakefield. However, naive task assignment without considering core types and task features could result in inefficient resources utilization and detrimentally impacts the overall energy consumption. Dynamic task scheduling is a widely used scheduling strategy, which does not require prior knowledge, e.g. architecture heterogeneity, task DAG structure, before execution but makes the decisions during runtime. Work stealing has been proven to be an effective method among dynamic task scheduling with better scalability in larger systems. DVFS is a common technique to achieve better energy efficiency, however, exploiting it costs reconfiguration overhead ranging from tens of microseconds to one millisecond. With fine-grained tasks as small as milliseconds, as required to expose large parallelism, it is not realistic to use DVFS on a per-task level. Also, it shows that the energy consumed in cores’ under-utilized period is significant.
Based on these problem statements, we come up with a low energy task scheduling work stealing runtime based on XiTAO where the system environment configurations are either fixed or managed by the O/S power governors or system administrators. The runtime contains dynamic performance tracing module, idleness tracing module, power profiling module and a task mapping algorithm. The dynamic performance model is able to give the accurate predictions for future tasks given a set of resources. It is independent of platforms and frequencies and achieves scalability and portability. Power profiling helps runtime systems to understand CPU power consumption trends with respect to number/type of cores and frequencies. Idleness tracing presents the real-time status of cores and contributes to the energy conservation of under-utilized period. It also provides the real-time parallel slackness of active cores, which allows the task mapping algorithm to attribute corresponding power consumption on each concurrent running task. The task mapping algorithm integrates the information from above three modules and outputs the predicted best resources placements for ready tasks.
Poster presented by jing Chen at the LEGaTO Final Event: 'Low-Energy Heterogeneous Computing Workshop'
A vm scheduling algorithm for reducing power consumption of a virtual machine...eSAT Journals
Abstract This paper concentrates on methods which provide efficient processing time of a virtual machine, CPU utilization time of a virtual machine. As the user increases, the performance may be significantly reduced if the tasks are not scheduled in a proper order. In this paper the performance of two already existing algorithms DSP (Dependency Structural Prioritization) algorithm and credit scheduling algorithm are analyzed and compared. A single virtual machine’s processing time and CPU utilization time are measured .Satisfactory results are achieved while comparing the two algorithms. This study concludes that the DSP algorithm can perform efficiently than the credit scheduling algorithm. Keywords: Virtual Machine, DSP algorithm, credit scheduling algorithm
A vm scheduling algorithm for reducing power consumption of a virtual machine...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Scientific workload execution on a distributed computing platform such as a
cloud environment is time-consuming and expensive. The scientific workload
has task dependencies with different service level agreement (SLA)
prerequisites at different levels. Existing workload scheduling (WS) designs
are not efficient in assuring SLA at the task level. Alongside, induces higher
costs as the majority of scheduling mechanisms reduce either time or energy.
In reducing, cost both energy and makespan must be optimized together for
allocating resources. No prior work has considered optimizing energy and
processing time together in meeting task level SLA requirements. This paper
presents task level energy and performance assurance-workload scheduling
(TLEPA-WS) algorithm for the distributed computing environment. The
TLEPA-WS guarantees energy minimization with the performance
requirement of the parallel application under a distributed computational
environment. Experiment results show a significant reduction in using energy
and makespan; thereby reducing the cost of workload execution in comparison
with various standard workload execution models.
How to Make a Field invisible in Odoo 17Celine George
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Model Attribute Check Company Auto PropertyCeline George
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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1. System-Level Energy-Efficient Dynamic Task Scheduling∗
Jianli Zhuo Chaitali Chakrabarti
Department of Electrical Engineering Department of Electrical Engineering
Arizona State University, Tempe, AZ, 85287 Arizona State University, Tempe, AZ, 85287
jianli.zhuo@asu.edu chaitali@asu.edu
ABSTRACT voltage / frequency. It achieves significant dynamic power sav-
Dynamic voltage scaling (DVS) is a well-known low power design ing due to the quadratic relationship between voltage and dynamic
technique that reduces the processor energy by slowing down the power.
DVS processor and stretching the task execution time. But in a In recent years, there has been significant amount of work done
DVS system consisting of a DVS processor and multiple devices, in energy-efficient task scheduling for DVS processors. The work
slowing down the processor increases the device energy consump- can be classified into static scheduling algorithms [2, 3, 4] based
tion and thereby the system-level energy consumption. In this pa- on apriori task information, two-phase algorithms [5, 6, 7, 8] that
per, we present dynamic task scheduling algorithms for periodic operate in two phases: an off-line phase (based on W CET or other
tasks that minimize the system-level energy (CPU energy + device execution time estimates) followed by an online phase where the
standby energy). The algorithms use a combination of (i) optimal slack is greedily absorbed, and pure dynamic algorithms [2, 9, 10]
speed setting, which is the speed that minimizes the system en- that only operate in the online phase. Most of these techniques
ergy for a specific task, and (ii) limited preemption which reduces absorb the system slack greedily to reduce the processor operating
the numbers of possible preemptions. For the case when the CPU voltage and thereby reduce the CPU dynamic power consumption.
power and device power are comparable, these algorithms achieve Now consider a DVS system which consists of a DVS proces-
up to 43% energy savings compared to [1], but only up to 12% over sor interacting with other devices such as SDRAM memory, flash
the non-DVS scheduling. If the device power is large compared to drive, wireless interface, etc. Extending the execution time of a
the CPU power, we show that DVS should not be employed. task results in a reduction in the CPU dynamic power consumption
but it also results in an increase in the device standby energy con-
sumption. Since the device energy consumption can be comparable
Categories and Subject Descriptors to that of a DVS processor (such as StrongArm), their contribution
D.4.1 [Operating Systems]: Process Management—Scheduling cannot be ignored.
Recently, there has been some effort in developing task schedul-
General Terms: Algorithms. ing algorithms that minimize the system-level energy consumption
defined by CPU energy + device energy [11, 1]. These include
Keywords an algorithm that procrastinates the execution of tasks in a static
Dynamic task scheduling, energy minimization, optimal scaling schedule [11], and an algorithm that reduces the number of pre-
point, DVS system, real-time emptions for a dynamic schedule [1].
In this paper, we consider the problem of developing dynamic
task scheduling algorithms for periodic tasks that minimize the
1. INTRODUCTION system-level energy. The algorithms use a combination of (i) opti-
With the rapid growth in the portable and mobile device mar- mal speed setting, which is the speed that minimizes the system en-
ket, reducing the energy consumption to extend the battery lifetime ergy for a specific task, and (ii) limited preemption, which reduces
has become an important design metric. Dynamic voltage scaling the number of preemptions and thereby reduces the device standby
(DVS) is a well-known technique in low power design that trades energy. For the case when the CPU power and device power are
off performance for power consumption by lowering the operating comparable, the proposed algorithms duSYS and duSYS PC achieve
large energy savings (up to 30%) compared to the CPU-energy effi-
∗This research was funded in part by the NSF S/I/UCRC Center for cient algorithm duEDF, and up to 43% energy saving compared to
Low Power Electronics (EEC-9523338), and by the NSF I/UCRC the existing system-level energy efficient algorithm lpSEH DP [1].
center, Connection One (EEC-0226846). However, these algorithms save only up to 12% energy saving over
the non-DVS scheduling algorithm. If the device power is large
compared to the CPU power, then we show that a DVS scheme
does not result in lowest energy.
Permission to make digital or hard copies of all or part of this work for The rest of the paper is organized as follows. The paper begins
personal or classroom use is granted without fee provided that copies are with preliminaries like task definitions, DVS system configuration
not made or distributed for profit or commercial advantage and that copies and calculation of optimal speed setting in Section 2. The new
bear this notice and the full citation on the first page. To copy otherwise, to scheduling algorithms are presented in Section 3. The simulation
republish, to post on servers or to redistribute to lists, requires prior specific
permission and/or a fee.
results with random examples are described in Section 4. The paper
DAC 2005, June 13–17, 2005, Anaheim, California, USA. is concluded in Section 5.
Copyright 2005 ACM 1-59593-058-2/05/0006 ...$5.00.
2. 2. BACKGROUND Assume that a single task instance Ji (Ji ∈ T [k] ) is active and it
is scaled by factor s. Then the system energy during the execution
2.1 Task Definition of Ji is
[k]
In this paper, we consider periodic tasks in which the relative E(s) = Pproc · s · AETi + Pdev · s · AETi + E atv
deadline of a task is equal to its period. We denote the k − th [k]
= (s−2 Pref + s · Pdev )AETi + E atv
periodic task in the task set as T [k], which has period P [k] , worst
[k]
case execution time W CET [k], required device set Φ[k] , and opti- where Pdev = Dj ∈Φ[k] Pjstd is the summation of standby energy
mal scaling factor θ [k] which corresponds to the scaling factor that of all devices in the required device set Φ[k] . E atv does not depend
minimizes the energy consumption for executing task T [k] (more on s and is assumed constant during execution of task Ji .
details in Section 2.3). All task execution times are defined accord- [k]
Let Q(s) = s−2 · Pref + s · Pdev . Then E(s) = Q(s) · AETi +
ing to the highest frequency of the DVS processor. atv
E . Since AETi for a task is fixed and Eatv is constant, the
There are multiple instances of each task, and each instance has
value of s that minimizes Q(s) will also minimize E(s).
the same W CET , P , Φ and θ, but has different arrival time, dead-
Since Q(s) is a convex function, we can easily find its minimum
line and execution time. To make the notation simple, we relabel
by calculating the value of s which makes Q (s) = 0. This value
all task instances in the scheduling profile by label J with the in-
dex representative of the order of execution. Task instance Ji has is the optimal scaling factor θ [k] for task T [k].
parameters W CETi , Pi , Φi , θi , arrival time ai and deadline di . 2Pref
θ[k] = ( [k]
)1/3 (1)
2.2 DVS System Configuration Pdev
Consider a typical DVS system that consists of one DVS pro- Thus for every task, there is an optimal scaling factor for which
cessor (also referred to as CPU) and N devices denoted by D1 , the total energy (CPU + device) is minimized.
D2 ,..DN . The DVS processor can operate at different frequency [k]
If Pdev ≥ 2Pref , then θ[k] ≤ 1. In such cases, DVS should
and voltage settings in the active mode. The devices (e.g. SDRAM, [k]
not be employed. If Pdev << 2Pref , the CPU energy dominates,
Flash Drive, etc.), on the other hand, operate at a single frequency and we can ignore the device energy during task scheduling. If
and voltage in the active mode. CPU and device energy are comparable, the optimal scaling factor
θ plays an important role.
2.2.1 DVS Processor Energy Model Since most DVS processors operate on a limited set of voltage
The following parameters are defined for the DVS processor: and frequency levels, we can also numerically find the optimal scal-
fproc , Pproc represent the operating frequency and power; fref , Pref ing point for each task. Let Q(s) = Qproc(s) + Qdev (s), where
represent the reference frequency and power (which are also the Qproc (s) = s · Pproc and Qdev (s) = s · P d[k] .
highest values of fproc , Pproc ). The scaling factor is defined as
fref
s = fproc , s ≥ 1. For long channel devices, scaling the frequency
by a factor of s causes voltage to scale by a factor of s, the cur-
rent to scale by a factor of s2 , and the power to scale by a factor
of s3 . Thus Pproc = s−3 Pref . The optimal scaling factor which
minimizes the CPU energy is defined as Θ.
2.2.2 Device Energy Model
We assume that the devices have three modes of operation: ac-
tive, standby and shutdown. The standby power of device Dj is
Pjstd , and the standby energy is Ej = Pjstd · τ , where τ is the
std
atv
time during which Dj is on. Ej is the active energy of Dj , which
is the active power Pjatv times the number of access during task ex-
ecution. Ej f is the energy when the device is shutdown and we
of
assume that Ej f = 0. If Dj is required by a task, then the device
of
Figure 1: Q(s) vs s for a SA1100 based system
std atv
energy consumption is Ej + Ej when the task is being exe-
Fig 1 illustrates the variation of Qproc (s), Qdev (s) and Q(s)
cuted, and Ej when the task is preempted. If a task is scaled by a
std
with s for a DVS system that consists of a StrongArm SA1100 pro-
factor s (s ≥ 1), Ej is increased by a factor s. There is, however,
std
cessor [12] and devices with different standby power. Note that
atv
no change in Ej . Qproc (s) (line with cross) is not a monotonically decreasing func-
tion. This trend in Qproc (s) has also been pointed out in [4, 13].
2.3 Optimal Scaling Point In fact, if only the SA1100 processor is considered, the optimal
Our goal is to minimize the total energy consumption of the sys- scaling factor is Θ = 2.8. Note that Θ differs from processor to
tem Eproc + Edev , where Eproc is the processor energy, and Edev processor. For the system with P std = 0.2W (such as SDRAM
is the device energy given by Edev = E std + E atv . The optimal [14]), Qdev (s) is shown by the dotted line, and the corresponding
scaling factor for such a system is a function of Eproc + Edev . Q(s) is shown by the line with circles. The optimal scaling factor
Different tasks trigger different sets of devices, and the optimal for this case is θ [k] = 1.39, and the corresponding frequency is 148
scaling factor for each task is different. Let θ [k] be the optimal MHz. For the system when P std is 0.4W (such as flash drive [11]),
scaling factor of a task which minimizes the system energy when θ[k] is 1.07 as shown by the line with squares, and the correspond-
there are no deadline constraints. θ [k] is determined off-line and is ing frequency is 192 MHz. Thus we see that as the device standby
calculated only once for each periodic task T [k]. The procedure is power increases, the scaling factor reduces. Note that the optimal
described below. scaling factor does not depend on the active power of the device.
3. 3. DYNAMIC TASK SCHEDULING device power when tasks are preempted is larger, ϑact ≤ θact , the
Next we present task scheduling algorithms that (i) generate fea- scaling factor of the active task, sact , is smaller.
sible task sets, and (ii) minimize the energy consumption (CPU en-
ergy + device standby energy) of the system. We consider dynamic
3.3 duSYS PC - Preemption Control
scheduling where the AET of the task set is not known apriori. Task preemption helps in better slack utilization and has been
We use the following definitions in the algorithm description. extensively used in DVS schemes that are based on CPU energy
RunQ is the run queue containing all released tasks, Jact is the minimization. However, task preemption increases the lifetime of
active task executed by the processor, Jhigh is the task with highest the preempted task, resulting in an increase in the standby device
priority in RunQ, and Jpre is the active task in the previous cycle energy consumption of the preempted tasks.
which is not finished yet. Here, we describe an algorithm duSYS PC that reduces the pre-
We first use dynamic utilization information to get the maximum emption of tasks scheduled by duSYS. At time t, assume Jhigh is
scaling factor of Jact at time t. For a hyper-period H (L.C.M of about to preempt the execution of Jpre . If we can delay the execu-
periods of tasks), The maximum scaling factor is tion of Jhigh by a time duration τdelay without deadline violation,
and if Jpre finishes in τdelay , then we can successfully avoid pre-
H − t − µ−1 · (W − W CETact) emption. In some cases, Jpre may not be able to finish in τdelay .
du(t) = , (0 ≤ t ≤ H) (2) By delaying the preemption of Jpre , the standby energy of all de-
W CETact
[k]
vices used by Jpre is reduced. Note that τdelay has to respect the
where W is the estimated remaining workload, µ = m W CET k=1 P [k]
deadline of Jhigh .
is the static utilization in EDF (Earliest Deadline First) scheduling. The idea of delay preemption is borrowed from [1] and is used
The speed derived setting by equation (2) lets the active task ab- to calculate τ = W CEThigh × (du(t) − 1). In this algorithm,
sorb all the available slack and at the same time ensures that the τdelay is given by τdelay = τ × m x(i−1) , where x = µ·s1
i=1 pre
remaining tasks can be scaled by a factor not less than µ−1 . and m is typically taken to be 10. (details omitted.)
The skeleton of the proposed EDF based dynamic task schedul-
ing algorithms is given in Algorithm 1. 4. EXPERIMENTAL RESULTS
Algorithm 1 Skeleton of the proposed algorithms In this section, we compare the performance of the algorithms
for randomly generated tasks. We include lpSEH DP [1] in the
1: W = H · µ; comparison since it is the best algorithm (to date) for achieving
2: while time() < hyperperiod do system level energy efficiency for dynamic task scheduling. All the
3: determine sact and execute Jact using sact ; energy consumption values shown in this section are normalized
4: if Jact is not finished then with respect to the non-DVS scheduling result.
5: ExeP art = current duration/sact ; We vary the processor utilization of the tasks from 0.1 to 0.9
6: W = W − ExeP art; with a step of 0.2 and run 100 random task sets for each utilization
7: W CETact = W CETact − ExeP art; value. Each task set consists of 4 periodic tasks, and is generated
8: AETact = AETact − ExeP art; in the following way: period of the tasks is randomly chosen from
9: else 0.1s to 1s, W CET s of the tasks are chosen to satisfy the utilization
10: W = W − W CETact; constraint given apriori (0.1, 0.3, etc.), AET for each task instance
11: end if is given by a Gaussian distribution with mean m = 0.8 × W CET
12: end while and variance σ = 0.067 × W CET . All the task sets are run in a
hyper-period (i.e. the L.C.M of all periods).
The DVS processor is StrongArm SA1100 [12] with 11 frequency
3.1 duEDF - CPU energy only values, and power ranging from 0.1W to 0.54W. We assume that the
Algorithm duEDF minimizes the CPU energy (and not the sys- processor does not consume energy when it is idle and the overhead
tem energy). It is applicable to a system where the CPU energy is of moving from one voltage to another is negligible.
dominant. In this algorithm, Jhigh is always selected as Jact , and
the scaling factor is given by sact = min(du(t), WdCETact , Θ).
act −t 4.1 Experiment 1
Here the first term exploits the maximum available slack, the sec- In this experiment, the processor power and the device standby
ond term ensures that the deadline is not violated, and the third term power are comparable. The required device set for each task is
considers the optimal scaling factor of the processor, Θ. fixed: Φ[1] = {D1 }, Φ[2] = {D1 , D2 }, Φ[3] = D1 and Φ[4] = ∅.
std std
The standby power for devices is P1 = 0.2W and P2 = 0.4W
3.2 duSYS - CPU + device energy (typical values for SDRAM and flash drive [11]).
Algorithm duSYS considers the optimal scaling factor (that takes CPU energy: When only CPU energy is considered, algorithms
into account both the CPU energy and device energy) in the deriva- duEDF and lpSEH DP have energy saving up to 40% compared to
tion of sact . non-DVS scheduling (see Fig 2). Algorithms duSYS and duSYS PC,
on the other hand, have only 25% energy saving compared to the
1: IF Jpre exists, THEN sact = min(du(t), WdCETact , ϑact );
act −t non-DVS case.
dact −t
2: ELSE sact = min(du(t), W CETact , θact ); ENDIF System-level energy: When the processor + device energy is
considered, duEDF and lpSEH DP have consistently higher energy
consumption compared to duSYS and duSYS PC. In fact, duEDF
Here ϑact is the optimal scaling factor when we consider all the and lpSEH DP have much higher energy consumption compared
devices that are on. These include devices associated with pre- to non-DVS scheduling for low processor utilization. For high
empted tasks that are in standby and the devices associated with processor utilization, the increase is not as significant. duSYS and
Jact . θact , on the other hand, is the optimal scaling factor when duSYS PC have up to 12% energy saving compared to the non-DVS
we consider only the devices associated with Jact . Since the total case (see Fig 3).
4. ! "#$ % $& % !'! % !'!# ! "#$ % $& % ' % ' # !"# $ #% $ & $ & "
!
Figure 2: Processor energy (Exp.1) Figure 3: System energy (Exp.1) Figure 4: Complexity (Exp.1)
From Fig 3, we also see that the energy consumption of Algo- 5. CONCLUSION
rithms duSYS and duSYS PC is comparable. This implies that pre- In this paper, we consider the problem of developing dynamic
emption control may not really be required for algorithms that min- task scheduling algorithms for periodic tasks that minimize the
imize CPU + device energy. If, however, the cost of preemption is system-level energy. We first determine the ’optimal’ scaling factor
accurately taken into account, duSYS PC would have a noticeably by which a task should be scaled to minimize energy (if there are no
lower energy consumption compared to duSYS. deadline constraints). Then we propose scheduling algorithms that
Complexity: The complexity of each algorithm shown in Fig 4 use a combination of optimal speed setting and limited preemption.
is represented by the average processor time spent on running the When the CPU power and device power are comparable, experi-
algorithm. lpSEH DP has significantly high runtime complexity ments on randomly generated task sets show that the proposed al-
compared to the other algorithms. The proposed algorithms have gorithms duSYS and duSYS PC achieve large energy savings (up to
comparable complexity with duSYS PC being the most complex. 43%) compared to existing dynamic scheduling algorithm [1], and
up to 12% compared to the non-DVS case. We also show that if
4.2 Experiment 2 the device power is large compared to CPU power, then a non-DVS
In this experiment, we study the effect of standby power on the scheme is the one with the lowest energy consumption.
system level energy consumption. We consider three settings of
the standby power: (i) P1 = 20mW and P2 = 40mW , (ii)
std std
std std
P1 = 0.2W and P2 = 0.4W , and (iii) P1 = 2W and std 6. REFERENCES
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