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Lookup NU author(s): Professor Raj Ranjan
Resource usage estimation for managing streaming workload in emerging applications domains such as enterprisecomputing, smart cities, remote healthcare, and astronomy, has emerged as a challenging research problem. Such resource estimation for processing continuous queries over streaming data is challenging due to: (i) uncertain stream arrival patterns, (ii) need to process different mixes of queries, and (iii) varying resource consumption. Existing techniques approximate resource usage for a query as a single point value which may not be sufficient because it is neither expressive enough nor does it capture the aforementioned nature of streaming workload. In this paper, we present a novel approach of using mixture density networks to estimate the whole spectrum of resource usage as probability density functions. We have evaluated our technique using the linear road benchmark andTPC-H in both private and public clouds. The efficiency and applicability of the proposed approach is demonstrated via two novel applications: i) predictable auto-scaling policy setting which highlights the potential of distribution prediction in consistent definition of cloud elasticity rules; and ii) a distribution based admission controller which is able to efficiently admit or reject incoming queries based on probabilistic service level agreements compliance goals.
Author(s): Khoshkbarforoushha A, Ranjan R, Gaire R, Abbasnejad E, Wang L, Zomaya AY
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Emerging Topics in Computing
Year: 2017
Volume: 5
Issue: 1
Pages: 120-133
Print publication date: 31/03/2017
Online publication date: 02/08/2016
Acceptance date: 12/07/2016
Date deposited: 20/08/2017
ISSN (electronic): 2168-6750
Publisher: IEEE
URL: https://doi.org/10.1109/TETC.2016.2597546
DOI: 10.1109/TETC.2016.2597546
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