PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds

Kumbhare, Alok Gautam and Simmhan, Yogesh and Prasanna, Viktor K (2014) PLAStiCC: Predictive Look-Ahead Scheduling for Continuous dataflows on Clouds. In: 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), MAY 26-29, 2014, Chicago, IL, pp. 344-353.

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Abstract

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous dataflows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application's throughput. In this paper we propose PLAStiCC, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based lookahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from Amazon AWS IaaS public cloud. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.

Item Type:	Conference Proceedings
Series.:	IEEE-ACM International Symposium on Cluster Cloud and Grid Computing
Publisher:	IEEE
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords:	Continuous Dataflows; Predictive scheduling; IaaS Clouds; Elastic resource management; Stream processing
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	09 Oct 2015 05:36
Last Modified:	09 Oct 2015 05:36
URI:	http://eprints.iisc.ac.in/id/eprint/52523

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