Upload
morgan
View
79
Download
1
Embed Size (px)
DESCRIPTION
Distributed Process Scheduling: A System Performance Model. Vijay Jain CSc 8320, Spring 2007. Outline. Overview Process Interaction Models A System Performance Model Efficiency Loss Processor Pool and Workstation Queuing Models Comparison of Performance for Workload Sharing References. - PowerPoint PPT Presentation
Citation preview
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
Process Models
Partition 4 processes onto two nodes
Communication overhead
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.)
'
)()()',(
)',(
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
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 SharingDynamic 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