A SYSTEM PERFORMANCE MODEL

CSC 8320 Advanced Operating Systems

Georgia State University

Yuan Long

OUTLINE Overview Basic Theory

• Process Integration Models• Process Models• System Performance Model• Efficiency Loss• Workload Distribution• Processor-Pool and Workstation Queuing Models• Comparison of Performance for Workload Sharing

Recent Work Future Work

OVERVIEWWhy scheduling? Communication and synchronization

facilities are essential system components for supporting concurrent execution of interacting processes.

Before the execution, processes need to be scheduled and allocated with resources.

OVERVIEW (CONT.)

What is the goal of scheduling?

Enhance overall system performance metrics.

• Process completion time • Processor utilization.

Achieve location and performance transparency in distributed systems.

OVERVIEW (CONT.)

Issues? The communication overhead can not

be ignored.

The effect of underlying architecture can not be ignored.

Dynamic behavior of the system.

PROCESS INTERACTION MODEL

Four processes mapped to a two-processor multiple computer system.

Precedence process model (Directed Acyclic Graph (DAG)) Communication process model Disjoint process model

PROCESS MODELSPrecedence process model

Represent precedence relationships between processes Minimize total completion time of task (computation + communication)

P1

P2

P3

P4

Communication overhead

PROCESS MODELSCommunication process model

Represent the need for communication between processes Optimize the total cost of communication and computation

SYSTEM PERFORMANCE MODELDisjoint process model

Processes can run independently and completed in finite time Maximize utilization of processors and minimize turnaround time of

processes

SYSTEM PERFORMANCE MODEL

Speedup• the algorithm design • underlying system architecture• efficiency of the scheduling algorithm.

SYSTEM PERFORMANCE MODELS can also be written as

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

n=number of processors. m=number of tasks in the

algorithm. =total computation of

the concurrent algorithm

SYSTEM PERFORMANCE MODELSi can be rewritten as

RP=Relative Processing requirement. (RP 1) RC=Relative Concurrency. RC=1 best use of the

processors

---the efficiency lessthe ratio of the real system

overhead due to all causes to the

ideal optimal processing time.

Two parts: sched + syst

SYSTEM PERFORMANCE MODELSd can be rewritten as

Finally we can get

(The bigger the better)

EFFICIENCY LOSS How to illustrate the interdependence between scheduling and

system factors ?

The efficiency loss p can be expressed as

Real system Ideal system

Multiple computer system

X’ X

Scheduling policy

Y’ Y

'

)()()',(

)',(

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

Ideal system Non-Ideal system

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.

WORKLOAD DISTRIBUTION Performance can be further improved by

workload distribution Loading 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

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.

PROCESSOR-POOL AND WORKSTATION QUEUING MODELS

Static Load SharingDynamic Load Balancing

M for Markovian distribution

COMPARISON OF PERFORMANCE FOR WORKLOAD SHARING

))((

1

2

1

TT

TT

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

RECENT WORK

Scheduling dynamic load-balancing in parallel and distributed computers

By developing effective methods the whole program time execution will be decreased and process utilization will be optimized.• Simple scheduling method• Round Robin algorithm• Genetic algorithm• using modified genetic algorithm

RECENT WORK

A performance model for analyzing large-scale systems

• Develop models of DNS(Direct Numerical Simulation )

• Captures its key performance characteristics.• Can be used for the prediction of performance on

existing as well as non-existing systems.

FUTURE WORKIntegrated Power and

Performance Model• Predict the optimal number of active processors

for a given application.• Can model the increases in power consumption

that resulted from the increases in temperature.

Unlike previous models, which may require• Measured execution times• Hardware performance counters• Or architectural simulation

FUTURE WORKScheduling in multi-processor

systems based on PSO.• Based on PSO method.(Particle swarm

optimization algorithm)• Each swarm is modeled by particles in

multidimensional space. Every particle is specified by a position and velocity and starts a search in the search space.

• Minimize the maximum span and average utilization of all processors in an optimal way.

REFERENCE [1]B.Veltman, Multiprocessor scheduling with

communication delays,1990.[2] Javad Mohammadzadeh, Scheduling dynamic load-

balancing in parallel and distributed computers using modified genetic algorithm with time dependent fitness function,2009

[3] Darren J.Kerbyson, A performance model of direct numerical simulation for analyzing large-scale systems,2011

[4] Sunpyo Hong, Integrated GPU Power and Performance Model,2010

[5] OmidReza Kiyarazm, A new method for scheduling load balancing in multi-processor systems based on PSO,2011

[6] Randy Chow, Theodore Johnson, Distributed Operating Systems & Algorithms, 1997