Paper Info
Title
Adaptive Optimization of a System's Load
Abstract
Applications of modeling techniques based on queueing theory to computer system performance analysis normally assume the existence of steady-state conditions. However, these conditions are often violated since the unpredictable composition of workload causes peaks having highly variable intensities and durations. Furthermore, computer system performance is highly dependent on how the system reacts to workload fluctuations. Automatic control mechanisms are required to take care of the high variance of resource demands. Real-time optimization of the overall performance of a computer system requires the introduction of adaptive control on the controlled functions, An adaptive scheduling algorithm which controls the input of the system in order to maximize a given performance criterion, such as the system throughput, is presented. The system load is adjusted depending on the characteristics of both the mix of jobs in execution and the mix of jobs submitted to the system and waiting in the input queue. The asymptotic analysis of the performance bounds provides useful information about the limits on the performance indexes that can be achieved with a multiclass workload. The evaluation of the adaptive control system is performed through simulation experiments using data collected from two real workloads. This technique could be used to optimize the throughput of a centralized system as well as for the automatic load balancing in a distributed environment.
Year
DOI
Venue
1984
10.1109/TSE.1984.5010312
IEEE Trans. Software Eng.
Keywords
Field
DocType
system load,adaptive optimization,computer system,computer system performance analysis,centralized system,performance bound,computer system performance,adaptive control system,overall performance,system throughput,system reacts,scheduling algorithm,adaptive control,distributed environment,performance index,simulation experiment,throughput,optimal control,data mining,steady state,data collection,automatic control,application software,load balancing,probability density function,asymptotic analysis,load balance,system performance,optimization,real time,queueing theory,control systems
Optimal control,Adaptive optimization,Load balancing (computing),Computer science,Scheduling (computing),Workload,Real-time computing,Automatic control,Queueing theory,Adaptive control
Journal
Volume
Issue
ISSN
10
6
0098-5589
Citations 
PageRank 
References 
4
0.52
13
Authors
2
Name
Order
Citations
PageRank
Giuseppe Serazzi1909123.23
Maria Calzarossa216119.38