Paper Info
Title
A Generalized Performance Evaluation Framework for Parallel Systems with Output Synchronization.
Abstract
Frameworks, such as MapReduce and Hadoop are abundant nowadays. They seek to reap benefits of parallelization, albeit subject to a synchronization constraint at the output. Fork-Join (FJ) queuing models are used to analyze such systems. Arriving jobs are split into tasks each of which is mapped to exactly one server. A job leaves the system when all of its tasks are executed. As a metric of performance, we consider waiting times for both work-conserving and non-work conserving server systems under a mathematical set-up general enough to take into account possible phase-type behavior of the servers, and as suggested by recent evidences, bursty arrivals. To this end, we present a Markov-additive process framework for an FJ system and provide computable bounds on tail probabilities of steady-state waiting times, for both types of servers separately. We apply our results to three scenarios, namely, non-renewal (Markov-modulated) arrivals, servers showing phase-type behavior, and Markov-modulated arrivals and services. We compare our bounds against estimates obtained through simulations and also provide a theoretical conceptualization of provisions in FJ systems. Finally, we calibrate our model with real data traces, and illustrate how our bounds can be used to devise provisions.
Year
Venue
Field
2016
arXiv: Performance
Synchronization,Computer science,Server,Parallel computing,Conceptualization,Real-time computing,Queueing theory,Distributed computing
DocType
Volume
Citations 
Journal
abs/1612.05543
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wasiur R. KhudaBukhsh100.68
Sounak Kar241.80
Amr Rizk320927.28
Heinz Koeppl415936.18