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|Author:||Wasiur R. Khudabukhsh, Sounak Kar, Amr Rizk, Heinz Koeppl|
|Kind:||Article - use for journal articles only|
|Journal:||ACM Transactions on Modeling and Performance Evaluation of Computing Systems|
|Keywords:||Performance evaluation, Queuing systems, Fork-Join queues, Markov additive processes, Parallel systems|
|Research Area(s):||Communication Services|
|Abstract:||Parallel server frameworks are widely deployed in modern large-data processing applications. Intuitively, splitting and parallel processing of the workload provides accelerated application response times and scaling flexibility. Examples of such frameworks include MapReduce, Hadoop, and Spark. For many applications, the dynamics of such systems are naturally captured by a FJ queuing model, where incoming jobs are split into tasks each of which is mapped to exactly one server. When all the tasks that belong to one job are executed, the job is reassembled and leaves the system. We consider this behavior at the output as a synchronization constraint. In this paper, we study the performance of such parallel systems for different server properties, i.e., work-conservingness, phase-type behavior, and as suggested by recent evidence, for bursty input job arrivals. We establish a LDP for the steady-state job waiting times in an FJ system based on Markov-additive processes. Building on that, we present a performance analysis framework for FJ systems and provide computable bounds on the tail probabilities of the steady-state waiting times. We validate our bounds using estimates obtained through simulations. In addition, we define and analyze provisioning, a flexible division of jobs into tasks, in FJ systems. Finally, we use this framework together with real-world traces to show the benefits of an adaptive provisioning system that adjusts the service within an FJ system based on the arrival intensity.|
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