Paper Info
Title
Performance and Reliability of Non-Markovian Heterogeneous Distributed Computing Systems
Abstract
Average service time, quality-of-service (QoS), and service reliability associated with heterogeneous parallel and distributed computing systems (DCSs) are analytically characterized in a realistic setting for which tangible, stochastic communication delays are present with nonexponential distributions. The departure from the traditionally assumed exponential distributions for event times, such as task-execution times, communication arrival times and load-transfer delays, gives rise to a non-Markovian dynamical problem for which a novel age dependent, renewal-based distributed queuing model is developed. Numerical examples offered by the model shed light on the operational and system settings for which the Markovian setting, resulting from employing an exponential-distribution assumption on the event times, yields inaccurate predictions. A key benefit of the model is that it offers a rigorous framework for devising optimal dynamic task reallocation (DTR) policies systematically in heterogeneous DCSs by optimally selecting the fraction of the excess loads that need to be exchanged among the servers, thereby controlling the degree of cooperative processing in a DCSs. Key results on performance prediction and optimization of DCSs are validated using Monte-Carlo (MC) simulation as well as experiments on a distributed computing testbed. The scalability, in the number of servers, of the age-dependent model is studied and a linearly scalable analytical approximation is derived.
Year
DOI
Venue
2012
10.1109/TPDS.2011.285
IEEE Trans. Parallel Distrib. Syst.
Keywords
Field
DocType
average service time,heterogeneous dcss,markovian setting,realistic setting,key benefit,computing systems,heterogeneous parallel,age-dependent model,key result,event time,communication arrival time,monte carlo methods,renewal theory,exponential distribution,stochastic processes,distributed processing,distributed computing,load balance,monte carlo,queuing theory,reliability,measurement,stochastic process,queueing theory,load balancing,monte carlo simulation,servers,quality of service,vectors,software reliability
Markov process,Renewal theory,Computer science,Load balancing (computing),Server,Stochastic process,Real-time computing,Queueing theory,Exponential distribution,Scalability,Distributed computing
Journal
Volume
Issue
ISSN
23
7
1045-9219
Citations 
PageRank 
References 
5
0.41
22
Authors
2
Name
Order
Citations
PageRank
Jorge E. Pezoa111915.76
Majeed M. Hayat221326.36