Multiprocessor scheduling 3
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This document discusses task allocation and scheduling in a multi-processor environment. It proposes using different task-processor assignment policies like next fit and bin packing algorithms to optimize processor utilization. The tasks will be dynamically scheduled using the Earliest Deadline First algorithm. The objectives are to generate synthetic tasks, assign priorities, allocate tasks to processors, and schedule all tasks across the multi-processor system.
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Multiprocessor scheduling 1
This document discusses multiprocessor scheduling and aims to find optimal task-processor assignment policies to improve processor utilization. The methodology generates synthetic tasks, assigns them priorities using EDF, performs task-processor assignment, and schedules the tasks across multiple processors. The expected results are a high processor utilization with optimal task assignment and run-time scheduling.
Multiprocessor scheduling 2
This document discusses multiprocessor scheduling. It describes generating synthetic tasks, assigning priorities using static and dynamic methods like Rate Monotonic and Earliest Deadline First algorithms, and allocating tasks to processors using static and dynamic allocation strategies. Real-time scheduling is important for applications like patient monitoring, smart environments, and mobile devices. The work aims to find better processor utilization by evaluating different task-processor assignment policies and scheduling tasks dynamically.
Sara Afshar: Scheduling and Resource Sharing in Multiprocessor Real-Time Systems
PhD Candidate,
Department of Computer science
Mälardalen University
Time: Tuesday, Dec. 30, 2014, 11:30 a.m.
Location: Computer Engineering Department, Urmia University
Abstract:
The processor is the brain of a computer system. Usually, one or more programs run on a processor where each program is typically responsible for performing a particular task or function of the system. The performance of all the tasks together results in the system functionality. In many computer systems, it is not only enough that all tasks deliver correct output, but it is also crucial that these activities are delivered in a proper time. This type of systems that have timing requirements are known as real-time systems. A scheduler is responsible for scheduling all tasks on the processor, i.e., it dictates which task to run and when to run to ensure that all tasks are carried out on time. Typically, such tasks/programs need to use the computer system’s hardware and software resources to perform their calculation. Examples of such type of resources that are shared among programs are I/O devices, buffers and memories. Technology that is used for the management of shared resources is known as resource sharing synchronization protocol.
In recent years, a shift from single-processor platforms to multiprocessor platforms has become inevitable due to availability of processor chips and requirements on increased performance. Scheduling and resource sharing protocols have been well studied for uniprocessor systems. However, in the context of multiprocessors, still such techniques are not fully mature. The shift towards multi-core technology has revealed the demand for real-time scheduling algorithms along with synchronization protocols to support real-time applications on multiprocessors, both with and without dependencies.
In this talk, we first have an introduction to real-time embedded systems. Next, we look at scheduling and resource sharing policies in uniprocessor platforms. Further, we discuss the extension of scheduling and resource sharing policies for multiprocessor platforms and present the recent challenges arisen in this context.
Biography:
Sara Afshar is a PhD student at Mälardalen University. She has received her B.Sc. degree in Electrical Engineering from Tabriz University, Iran in 2002. She worked at different engineering companies until 2009. In the year 2010 she started her M.Sc. in Embedded Systems at Mälardalen University. She obtained her Master degree in 2012 and at the same year she started her PhD studies in Mälardalen University. Currently she is working on the topic of resource sharing in multiprocessor systems. She is part of the Complex Real-Time Embedded Systems group at Mälardalen University.
Real time scheduling - basic concepts
About real time system task scheduling basic concepts.It deals with task, instance,data sharing and their types.It also covers various important terminologies regarding scheduling algorithms.
Bounded ant colony algorithm for task Allocation on a network of homogeneous ...
This document summarizes a research paper that proposes a bounded ant colony algorithm (BTS-ACO) for task scheduling on a network of homogeneous processors using a primary site. The algorithm uses an initial bound on each processor's load to control task allocation. It investigates scheduling tasks from a sorted list (SLoT) versus a random list (RLoT). Simulation results show that BTS-ACO with a sorted task list achieves better performance than a random list in terms of scheduling time, makespan, and load balancing.
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This document summarizes research on parallelizing the graceful graph labeling problem using OpenMP on multi-core processors. It introduces the concepts of parallelization, multi-core architecture, and OpenMP. An algorithm is designed to parallelize graceful labeling by distributing graph vertices across processor cores. Execution time and speedup are measured for graphs of increasing size, showing improved speedup and reduced time with parallelization. Results show consistent performance gains as graph size increases due to better utilization of the multi-core architecture.
(Slides) Task scheduling algorithm for multicore processor system for minimiz...
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.
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Recommended
Multiprocessor scheduling 1
This document discusses multiprocessor scheduling and aims to find optimal task-processor assignment policies to improve processor utilization. The methodology generates synthetic tasks, assigns them priorities using EDF, performs task-processor assignment, and schedules the tasks across multiple processors. The expected results are a high processor utilization with optimal task assignment and run-time scheduling.
Multiprocessor scheduling 2
This document discusses multiprocessor scheduling. It describes generating synthetic tasks, assigning priorities using static and dynamic methods like Rate Monotonic and Earliest Deadline First algorithms, and allocating tasks to processors using static and dynamic allocation strategies. Real-time scheduling is important for applications like patient monitoring, smart environments, and mobile devices. The work aims to find better processor utilization by evaluating different task-processor assignment policies and scheduling tasks dynamically.
Sara Afshar: Scheduling and Resource Sharing in Multiprocessor Real-Time Systems
PhD Candidate,
Department of Computer science
Mälardalen University
Time: Tuesday, Dec. 30, 2014, 11:30 a.m.
Location: Computer Engineering Department, Urmia University
Abstract:
The processor is the brain of a computer system. Usually, one or more programs run on a processor where each program is typically responsible for performing a particular task or function of the system. The performance of all the tasks together results in the system functionality. In many computer systems, it is not only enough that all tasks deliver correct output, but it is also crucial that these activities are delivered in a proper time. This type of systems that have timing requirements are known as real-time systems. A scheduler is responsible for scheduling all tasks on the processor, i.e., it dictates which task to run and when to run to ensure that all tasks are carried out on time. Typically, such tasks/programs need to use the computer system’s hardware and software resources to perform their calculation. Examples of such type of resources that are shared among programs are I/O devices, buffers and memories. Technology that is used for the management of shared resources is known as resource sharing synchronization protocol.
In recent years, a shift from single-processor platforms to multiprocessor platforms has become inevitable due to availability of processor chips and requirements on increased performance. Scheduling and resource sharing protocols have been well studied for uniprocessor systems. However, in the context of multiprocessors, still such techniques are not fully mature. The shift towards multi-core technology has revealed the demand for real-time scheduling algorithms along with synchronization protocols to support real-time applications on multiprocessors, both with and without dependencies.
In this talk, we first have an introduction to real-time embedded systems. Next, we look at scheduling and resource sharing policies in uniprocessor platforms. Further, we discuss the extension of scheduling and resource sharing policies for multiprocessor platforms and present the recent challenges arisen in this context.
Biography:
Sara Afshar is a PhD student at Mälardalen University. She has received her B.Sc. degree in Electrical Engineering from Tabriz University, Iran in 2002. She worked at different engineering companies until 2009. In the year 2010 she started her M.Sc. in Embedded Systems at Mälardalen University. She obtained her Master degree in 2012 and at the same year she started her PhD studies in Mälardalen University. Currently she is working on the topic of resource sharing in multiprocessor systems. She is part of the Complex Real-Time Embedded Systems group at Mälardalen University.
Real time scheduling - basic concepts
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Bounded ant colony algorithm for task Allocation on a network of homogeneous ...
This document summarizes a research paper that proposes a bounded ant colony algorithm (BTS-ACO) for task scheduling on a network of homogeneous processors using a primary site. The algorithm uses an initial bound on each processor's load to control task allocation. It investigates scheduling tasks from a sorted list (SLoT) versus a random list (RLoT). Simulation results show that BTS-ACO with a sorted task list achieves better performance than a random list in terms of scheduling time, makespan, and load balancing.
Parallelization of Graceful Labeling Using Open MP
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(Slides) Task scheduling algorithm for multicore processor system for minimiz...
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.
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Unit 8
Partitioning involves dividing a program into tasks that can be assigned to individual processors. Scheduling involves determining the order tasks are executed to minimize completion time. Communication and synchronization ensure data is available when needed across tasks. Overhead from partitioning and scheduling can reduce potential speedup from parallel execution if it outweighs time savings from parallelism.
(Paper) Task scheduling algorithm for multicore processor system for minimiz...
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.
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Unit 8
Partitioning involves dividing a program into tasks that can be assigned to individual processors. Scheduling involves determining the order tasks are executed to minimize completion time. Communication and synchronization ensure data is available when needed across tasks. Overhead from partitioning and scheduling can reduce potential speedup from parallel execution if it outweighs time savings from parallelism.
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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.
Task Scheduling Algorithm for Multicore Processor Systems with Turbo Boost an...
Yosuke Wakisaka, Naoki Shibata, Keiichi Yasumoto, Minoru Ito, and Junji Kitamichi : Task Scheduling Algorithm for Multicore Processor Systems with Turbo Boost and Hyper-Threading, In Proc. of The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications(PDPTA'14), pp. 229-235
In this paper, we propose a task scheduling algorithm for multiprocessor systems with Turbo Boost and Hyper-Threading technologies. The proposed algorithm minimizes the total computation time taking account of dynamic changes of the processing speed by the two technologies, in addition to the network contention among the processors. We constructed a clock speed model with which the changes of processing speed with Turbo Boost and Hyper-threading can be estimated for various processor usage patterns. We then constructed a new scheduling algorithm that minimizes the total execution time of a task graph considering network contention and the two technologies. We evaluated the proposed algorithm by simulations and experiments with a multiprocessor system consisting of 4 PCs. In the experiment, the proposed algorithm produced a schedule that reduces the total execution time by 36% compared to conventional methods which are straightforward extensions of an existing method.
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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
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This document provides an overview of a reference model for real-time systems. It describes the key components of the model including the workload model (tasks and jobs), resource model (processors and resources), and scheduling algorithms. It defines temporal parameters for jobs, periodic and sporadic task models, and different types of dependencies. It also covers functional parameters, resource requirements, and defines concepts like feasibility and optimality for schedules. The goal is to characterize real-time systems and provide a framework for analyzing scheduling and resource allocation algorithms.
Srt algorithm
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Multiprocessor scheduling 3
- 1. TASK ALLOCATION AND SCHEDULING IN A MULTI-PROCESSOR ENVIRONMENT Guided by Submitted by Ms.ANJU S PILLAI MUTHU KUMAR .B Assistant professor(SG) CB.EN.P2EBS10012 Department of EEE Department of EEE
- 2. ABSTRACT Uniprocessor scheduling is widely used for its simplicity, reliability and ease for implementation. But it has got its limitations over less processor utilization factor. For a better processor utilization and performance multiprocessor scheduling is preferred. One of the major challenges is to find an optimal task-processor assignment. The work aims at finding different task processor assignment policies and finding a better Processor Utilization and schedule the tasks dynamically using EDF algorithm.
- 3. METHODOLOGY • The main objective of the project is to perform scheduling in a multiprocessor system. • Generation of a set of synthetic tasks. • Fix the number of processors needed for the system. • Static Priority assignment to all the tasks using RM policy. • Dynamic Priority assignment to all the tasks using EDF policy. • The next step is to perform task-processor assignment. • Implementation of 1. Next Fit Algorithm 2. Bin Packing Algorithm • The final stage is the scheduling of all the tasks in the multiprocessor system.
- 4. DEVELOPING A MULTIPROCESSOR SCHEDULE Make an allocation Schedule each processor based on the allocation Change Allocation Are all these schedules feasible Output schedule Continue Check stopping criterion Stop Declare Failure
- 5. EARLIEST DEADLINR FIRST ALGORITHM If D=T • Shortest of the deadline of given task is selected as higher priority to be executed. • The highest priority task is executed first. If D!=T If D>T The task is not schedulable probably which means it misses the deadline. If D<T 1.The task set is just checked for the feasible solution. 2.Feasible solution is found by using the Processor Demand Analysis.
- 6. DEADLINE = TIME PERIOD TASK COMPUTATION TIME TIME PERIOD(D=T) A 1 3 B 1 6 C 1 5 D 3 10 Priorities of tasks scheduled using EDF 132144143213124144312134144231
- 7. EARLIEST DEADLINE FIRST ALGORITHM ( EDF )
- 9. EARLIEST DEADLINE FIRST TRACE
- 10. DEADLINE < TIME PERIOD TASK COMPUTATION DEADLINE TIME PERIOD(D=T) TIME A 3 4 6 B 4 7 8 Priorities of tasks scheduled using EDF Deadline Miss ! 1 2 1 2 ….. Hence not schedulable.
- 12. TASK ALLOCATION USING NEXT FIT ALGORITHM PROCEDURE FOR NEXT FIT ALGORITHM 1.Tasks with utilization are divided into classes with uniform utilization range. 2.According to number of classes the number of processors are decided. 3.Each class is assigned to specific processors.
- 13. TASKS : A B C D E F G H I J COMPUTATION 5 7 3 1 10 16 1 13 9 17 TIME TIME PERIOD 10 21 22 24 30 40 50 55 75 100 Utilization 0.5 0.33 0.14 0.04 0.33 0.4 0.02 0.24 0.13 0.17 Processor or Class 4 3 1 1 3 3 1 2 1 1 if(U(i)<0.19) Task in processor P4 if(U(i)<0.26) Task in processor P3 if(U(i)<0.41) Task in processor P2 if(U(i)<=1) Task in processor P1
- 14. NEXT FIT ALGORITHM FOR TASK ALLOCATION
- 17. BIN PACKING ALGORITHM BIN PACKING ALGORITHM 1.First fit random algorithm 2.First fit decreasing algorithm PROCEDURE FOR FIRST FIT RANDOM 1.Random tasks with utilization are selected and packed in a bin. 2.If bin exceeds 100% on further addition of tasks bin2 is started to be packed. 3.Task Utilizations are always whether they can be packed fully on previous processors.
- 18. TASKS : A B C D E F G H I J COMPUTATION 5 7 3 1 10 16 1 13 9 17 TIME TIME PERIOD 10 21 22 24 30 40 50 55 75 100 Utilization 0.5 0.33 0.14 0.04 0.33 0.4 0.02 0.24 0.13 0.17 Processor or Class 1 1 1 2 2 2 2 3 3 3 1. After utilization exceeds 1 it changes to processor 2. 2. After utilization exceeds 2 it changes to processor 3. 3. So number of processors required is 3 to accommodate the given task set.
- 19. BIN PACKING ALGORITHM FOR TASK ALLOCATION
- 22. TIME FRAME I. Literature Review - Aug 2011 II. Synthetic task generation and priority assignment – Sep 2011 III. Implementation of Task-Processor allocation policies – Oct & Nov 2011 IV. Scheduling of tasks - Dec 2011 V. Analysis of the results – Jan 2011 VI. Hardware implementation– Feb to May 2012 VII. Documentation – Jun 2012
- 23. REFERENCES 1. P. Ancilotti, G. Buttazzo, M. D. Natale, and M. Spuri. “Design and programming tools for time critical applications.” Real-Time Systems, 14:3, pp. 251–269, May 1998. 2. R. Pellizzoni and G. Lipari Feasibility “Analysis of Real-Time Periodic Tasks with Offsets “Real-Time Systems Journal, 2005. 3. Eric W.Parsons and Kenneth C.Sevcik“Implementing multiprocessor algorithms”. 4. Haobo Yu, Andreas Gerstlauer and Daniel Gajski “RTOS Scheduling in transaction level models”
- 24. THANK YOU !