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Distributed Process Scheduling: A System Performance Model

This study explores a system performance model for optimizing distributed process scheduling to enhance overall system metrics, completion time, and processor utilization. It delves into process interaction models, communication overhead, and the impact of system architecture. The paper discusses different process models like DAG and communication process models, aiming to minimize completion time by optimizing communication and computation costs. It further examines system performance metrics like OSPT, CPT, and efficiency loss factors. Strategies for workload distribution, such as load sharing and load balancing, are analyzed to improve system performance, along with queueing models and comparison of workload sharing strategies. References include key texts on distributed operating systems and scheduling algorithms.

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Distributed Process Scheduling: A System Performance Model

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  1. Distributed Process Scheduling: A System Performance Model Vijay Jain CSc 8320, Spring 2007

  2. Outline • Overview • Process Interaction Models • A System Performance Model • Efficiency Loss • Processor Pool and Workstation Queuing Models • Comparison of Performance for Workload Sharing • References

  3. Overview • Before execution, processes need to be scheduled and allocated with resources • Objective • Enhance overall system performance metric • Process completion time and processor utilization • In distributed systems: location and performance transparency • In distributed systems • Local scheduling (on each node) + global scheduling • Communication overhead • Effect of underlying architecture

  4. Process Interaction Models • Precedence process model: Directed Acyclic Graph (DAG) • Represent precedence relationships between processes • Minimize total completion time of task (computation + communication) • Communication process model • Represent the need for communication between processes

  5. Process Interaction Models • Optimize the total cost of communication and computation • Disjoint process model • Processes can run independently and completed in finite time • Maximize utilization of processors and minimize turnaround time of processes

  6. Communication overhead Process Models Partition 4 processes onto two nodes

  7. System Performance Model Attempt to minimize the total completion time of (makespan) of a set of interacting processes

  8. System Performance Model (Cont.) • Related parameters • OSPT: optimal sequential processing time; the best time that can be achieved on a single processor using the best sequential algorithm • CPT: concurrent processing time; the actual time achieved on a n-processor system with the concurrent algorithm and a specific scheduling method being considered • OCPTideal: optimal concurrent processing time on an ideal system; the best time that can achieved with the concurrent algorithm being

  9. System Performance Model (Cont.) considered on an ideal n-processor system (no interprocessor communication overhead) and scheduled by an optimal scheduling policy • Si: ideal speedup obtained by using a multiple processor system over the best sequential time • Sd: the degradation of the system due to actual implementation compared to an ideal system

  10. System Performance Model (Cont.) Pi: the computation time ofthe concurrent algorithm onnode i (RP  1)

  11. System Performance Model (Cont.) (The smaller, the better)

  12. System Performance Model (Cont.) • RP: Relative processing • Shows how much loss of speedup is due to the substitution of the best sequential algorithm by an algorithm better adapted for concurrent implementation but which may have a greater total processing need • Sd • Degradation of parallelism due to algorithm implementation

  13. System Performance Model (Cont.) • RC: Relative concurrency • How far from optimal the usage of the n-processor is • RC=1  best use of the processors • : Efficiency Loss is loss of parallelism when implemented on a real machine. •  can be decomposed into two terms:  = sched + syst

  14. Efficiency Loss  Impact factors: scheduling, system, and communication

  15. Efficiency Loss  (Cont.)

  16. Workload Distribution • Performance can be further improved by workload distribution • Load sharing: static workload distribution • Dispatch process to the idle processors statically upon arrival • Corresponding to processor pool model • Load balancing: dynamic workload distribution • Migrate processes dynamically from heavily loaded processors to lightly loaded processors • Corresponding to migration workstation model

  17. Workload Distribution • Performance of systems described as queuing models can be computed using queuing theory. An X/Y/c queue is one where: • X: Arrival Process, Y: Service time distribution, c: Numbers of servers • : arrival rate; : service rate; : migration rate • : depends on channel bandwidth, migration protocol, context and state information of the process being transferred.

  18. Processor-Pool and Workstation Queueing Models Static Load Sharing Dynamic Load Balancing M for Markovian distribution

  19. Comparison of Performance for Workload Sharing (Communication overhead) (Negligible Communication overhead) =0  M/M/1=M/M/2

  20. References • “Distributed Operating Systems and Algorithms” by Randy Chow and Theodore Johnson • “Opearting System Concepts” by Silberschatz, Galvin and Gagne • “Time Comparative Simulator for Distributed Process Scheduling Algorithms”, Transactions on Engineering, Computing and Technology Volume 13 May 2006 ISSN 1305-5313, Nazleeni Samiha Haron, Anang Hudaya Muhamad Amin, Mohd Hilmi Hasan, Izzatdin Abdul Aziz,and Wirdhayu Mohd Wahid

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