Distributed Process Scheduling

1. Distributed Process Scheduling A System Performance Model

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. For concurrent execution of interacting processes:- • Communication and • Synchronization between processes are the two essential system components Before processes can execute, they need to be:- • Scheduled and • Allocated with resources.

4. Why scheduling? 1.To enhance overall system performance metrices like: • Process completion time and • Processor utilization. 2. To achieve location and performance transparencies by distributed process scheduling.

5. Why scheduling in distributed systems is of special interest This is so because of the issues that are different from those in traditional multiprocessor systems: • The communication overhead is significant. • The effect of underlying architecture cannot be ignored. • And the dynamic behaviour of the system must be addressed.

6. Process Models(in brief) Precedence Process Model Processes are represented by a DAG. Nodes- sequential processes Arcs- eg: i to j requires that process I completes before j can start executing.

7. Communication Process Model • Processes are created to coexist and communicate synchronously. • So we have undirected edges.

8. Disjoint Process Model • We assume that processes can be run independently of each other. • So order in which processes are executed is not important.

9. System Performance • Speedup • -What are the factors on which it depends • How to calculate speedup when we apply these factors

10. Speedup depends on three factors • The design of the algorithm • The efficiency of the scheduling algorithm • The underlying system architecture. • So if we take ‘S’ as the speedup factor then the above dependencies can be represented as • S= F(Algorithm, System, Schedule)

11. 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; actual time achieved with the concurrent algorithm on an ideal n-processor system using an optimal scheduling policy. • OCPTideal =optimal concurrent processing time on an ideal system; • 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

12. Refined formula for speedup….. • n – number of processors • RP- Relative Processing requirement, • RC- Relative Concurrency

13. …. Refined formula for speedup • Sd- degradation of parallelism due to algorithm implementation.

14. Final formula for speedup • - Efficiency Loss, loss of parallelism when implemented on a real machine. •  can be decomposed into two terms:  = sched + syst

15. Efficiency Loss 

16. Efficiency Loss  (Cont.)

17. 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

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

19. Comparison of Performance for Workload Sharing

20. References • “Distributed Operating Systems and Algorithms” by Randy Chow and Theodore Johnson • “Operating System Concepts” by Silberschatz, Galvin and Gagne