The influence of different workload descriptions on a heuristic load balancing scheme
- 1 July 1991
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Software Engineering
- Vol. 17 (7) , 725-730
- https://doi.org/10.1109/32.83908
Abstract
A task scheduler based on the concept of a stochastic learning automation, implemented on a network of Unix workstations, is described. Creating an artificial, executable workload, a number of experiments were conducted to determine the effect of different workload descriptions. These workload descriptions characterize the load at one host and determine whether a newly created task is to be executed locally or remotely. Six one-dimensional workload descriptors are examined. Two workload descriptions that are more complex are also considered. It is shown that the best single workload descriptor is the number of tasks in the run queue. The use of the worst workload descriptor, the 1-min load average, resulted in an increase of the mean response time of over 32%, compared to the best descriptor. The two best workload descriptors, the number of tasks in the run queue and the system call rate, are combined to measure a host's load. Experimental results indicate that no performance improvements over the scheduler versions using a one-dimensional workload descriptor can be obtained.<>Keywords
This publication has 11 references indexed in Scilit:
- History, an intelligent load sharing filterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Communication-sensitive heuristics and algorithms for mapping compilersPublished by Association for Computing Machinery (ACM) ,1988
- Using stochastic learning automata for job scheduling in distributed processing systemsJournal of Parallel and Distributed Computing, 1986
- Adaptive load sharing in homogeneous distributed systemsIEEE Transactions on Software Engineering, 1986
- Load-balancing heuristics and process behaviorPublished by Association for Computing Machinery (ACM) ,1986
- Stability and Distributed Scheduling AlgorithmsIEEE Transactions on Software Engineering, 1985
- An Application of Bayesian Decision Theory to Decentralized Control of Job SchedulingIEEE Transactions on Computers, 1985
- Simulations of three adaptive, decentralized controlled, job scheduling algorithmsComputer Networks (1976), 1984
- Multiprocessor Scheduling with the Aid of Network Flow AlgorithmsIEEE Transactions on Software Engineering, 1977
- Learning Automata - A SurveyIEEE Transactions on Systems, Man, and Cybernetics, 1974