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Distributed Process Scheduling: A System Performance Model

Vijay Jain

CSc 8320, Spring 2007

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Outline

• Overview• Process Interaction Models• A System Performance Model• Efficiency Loss• Processor Pool and Workstation Queuing

Models• Comparison of Performance for Workload

Sharing• References

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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

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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

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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

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Process Models

Partition 4 processes onto two nodes

Communication overhead

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System Performance Model

Attempt to minimize the total completion time of (makespan) of a set of interacting processes

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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

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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

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System Performance Model (Cont.)

Pi: the computation time ofthe concurrent algorithm onnode i

(RP 1)

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System Performance Model (Cont.)

(The smaller, the better)

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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

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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

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Efficiency Loss

Impact factors: scheduling, system, and communication

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Efficiency Loss (Cont.)

'

)()()',(

)',(

schedsyst

ideal

idealideal

ideal

ideal

ideal

ideal

OCPT

OCPTYCPT

OCPT

YCPTYXCPT

OCPT

OCPTYXCPT

'

)()(),(

),(

systsched

ideal

ideal

ideal

ideal

ideal

OCPT

OCPTXOCPT

OCPT

XOCPTZXCPT

OCPT

OCPTZXCPT

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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

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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.

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Processor-Pool and Workstation Queueing Models

Static Load SharingDynamic Load Balancing

M for Markovian distribution

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Comparison of Performance for Workload Sharing

(Communication overhead)

(Negligible Communication overhead)

=0 M/M/1 =M/M/2

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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