Complexity Reduction: Local Activity Ranking By Resource Entropy For QoS-aware Cloud Scheduling

Wang, Frank Z. (2016) Complexity Reduction: Local Activity Ranking By Resource Entropy For QoS-aware Cloud Scheduling. In: IEEE. 2016 IEEE 13th International Conference on Services Computing (ranked A at ERA). IEEE, USA (doi:https://doi.org/10.1109/SCC.2016.82) (Full text available)

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http://dx.doi.org/10.1109/SCC.2016.82

Abstract

The principle of local activity originated from electronic circuits, but can easily translate into other non-electrical homogeneous/heterogeneousmedia.Cloudresourceisanexample of a locally-active device, which is the origin of complexity in cloud scheduling system. However, most of the researchers implicitly assume the cloud resource to be locally passive when constructing new scheduling strategies. As a result, their research solutions perform poorly in the complex cloud environment. In this paper, we ?rst study several complexity factors caused by the locally-active cloud resource. And then we extended the ”Local Activity Principle” concept with a quantitative measurement based on Entropy Theory. Furthermore, we classify the scheduling system into ”Order” or ”Chaos” state with simulating complexity in the cloud. Finally, we propose a new approach to controlling the chaos based on resource’s Local Activity Ranking for QoS-aware cloud scheduling and implement such idea in Spark. Experiments demonstrate that our approach outperforms thenativeSparkFairSchedulerwithservercostreducedby23%, average response time improved by 15% - 20% and standard deviation of response time minimized by 30% - 45%.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: Local Activity Principle, Entropy Theory, Cloud Scheduling, Quality of Service, Complex System, Order and Chaos
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.C58 Computational grids
Divisions: Faculties > Sciences > School of Computing > Data Science
Depositing User: Frank Wang
Date Deposited: 21 Nov 2016 13:10 UTC
Last Modified: 27 Jun 2017 07:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/58823 (The current URI for this page, for reference purposes)

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