Chapter 5 Distributed Process Scheduling. 5.1 A System Performance Model

1. Chapter 5 Distributed Process Scheduling.5.1 A System Performance Model --Niharika Muriki

2. Outline • Need for Scheduling • Process Interaction Models • System Performance Model • Efficiency Loss • Distribution of Workload • Comparison of Performance for Workload Sharing • Latest Relevant Applications • Future Work • References

3. Scheduling • As we have numerous number of processes running in parallel, scheduling these process plays a major role. • Before execution, processes need to scheduled and allocated with required resources. • Results of Scheduling: • Enhance overall system performance • Process completion time is minimized • Processor utilization is enhanced • Helps in achieving location and performance transparency in distributed systems.

4. Issues of Process Scheduling Process scheduling in distributed systems touches upon several practical considerations that are often omitted in the traditional multiprocessor scheduling. In distributed systems, • Communication overhead is non-negligible. • Effect of the underlying architecture cannot be ignored. • Dynamic behavior of the system must be addressed.

5. Process Interaction Models Based on the differences in interactions between processes, we have 3 types of process interaction models namely, • Precedence process model • Communication process model • Disjoint process model

6. Process Interaction Models[1](Contd.) We have depicted the differences in interactions between processes using a simple example of a program computation consisting of four processes mapped to a two-processor multiple computer system.

7. Precedence Process Model • Processes are represented by a Directed Acyclic Graph (DAG). • May incur communication overhead. • This model is best applied to the concurrent processes. • Use: Minimize the total completion time of the task. • Total Completion Time= Computation Time + Communication time

8. Communication Process Model • Processes communicate asynchronously. • Optimize the total cost of communication and computation. • The task is partitioned in such a way that minimizes the inter processor communication and computation costs of processes on processors.

9. Disjoint Process Model • Process interaction is implicit. • Processors utilization is maximized and turnaround time of the processes is minimized. • Partitioning a task into multiple processes for execution can result in a speedup of the total task completion time.

10. System Performance Model Speedup is a function of • Algorithm design • Underlying system architecture. • Efficiency of the scheduling algorithm .

11. System Performance Model[1] • S can also be written as : Where, • 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 considered on an ideal n-processor system(no inter-communication overhead) and scheduled by an optimal scheduling policy • Si:the ideal speedup 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

12. System Performance Model Si can be further derived as, • n=number of processors. • m=number of tasks in the algorithm. • RP=Relative Processing requirement. (RP  1) • RC=Relative Concurrency. RC=1  best use of the processors

13. System Performance Model Sd can be rewritten as •  ---the efficiency less • the ratio of the real system overhead due to all causes to the ideal optimal processing time. • Two parts:sched + syst • Finally we can get

14. Efficiency loss • Efficiency loss can be expressed as: • Ideal system • Non-Ideal system

15. Efficiency loss Following figure demonstrates the decomposition of efficiency loss due to scheduling and system communication. • The significance of the impact of communication on system performance must be carefully addressed in the design of distributed scheduling algorithm.

16. Workload Distribution • Load sharing: static workload distribution • Dispatch processes 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 • Model by queuing theory: X/Y/c • An arrival process X, a service time distribution of Y, and c 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 Queuing Models Static Load Sharing Dynamic Load Balancing *M for Markovian distribution

19. Comparison of Performance for Workload Sharing =0 M/M/1=M/M/2

20. Latest Relevant Application[2] • In the situation where there are multiple users or a networked computer system, you probably share a printer with other users. When you request to print a file, your request is added to the print queue. When your request reaches the front of the print queue, your file is printed. This ensures that only one person at a time has access to the printer and that this access is given on a first-come, first-served basis.

21. Latest Relevant Examples[2] • When you phone the toll-free number for your bank or any other customer service you may get a recording that says, "Thank you for calling XYZ Bank. Your call will be answered by the next available operator. Please wait." This is a queuing system. • Vehicles on toll-tax bridge: The vehicle that comes first to the toll tax booth leaves the booth first. The vehicle that comes last leaves last. Therefore, it follows first-in-first-out (FIFO) strategy of queue.

22. Future Work • Distributed flow scheduling in an unknown environment[3] Flow scheduling is crucial in the next-generation network but hard to address due to fast changing link states and tremendous cost to explore the global structure. • Pareto-Optimal Cloud Bursting[4] Large-scale Bag-of-Tasks (BoT) applications are characterized by their massively parallel, yet independent operations. The use of resources in public clouds to dynamically expand the capacity of a private computer system might be an appealing alternative to cope with such massive parallelism. To fully realize the benefit of this 'cloud bursting', the performance to cost ratio (or cost efficiency) must be thoroughly studied and incorporated into scheduling and resource allocation strategies.

23. References [1] Randy Chow, Theodore Johnson, Distributed Operating Systems & Algorithms, 1997 [2] 5 real life instances where queue operations are being used http://wiki.answers.com/Q/List_out_atleast_5_real_life_instances_where_queue_operations_are_being_used. [3] Yaoqing Yang., Kegin Liu, & Pingyi Fan, Distributed flow scheduling in an unknown environment. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6576397&sortType%3Ddesc_p_Publication_Year%26queryText%3DDistributed+scheduling [4] M. Reza HoseinyFarahabady, Young Choon Lee, Albert Y. Zomaya, "Pareto-Optimal Cloud Bursting," IEEE Transactions on Parallel and Distributed Systems, 27 Aug. 2013. IEEE computer Society Digital Library. IEEE Computer Society, http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.218

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