Service resilience framework for enhanced end-to-end service quality

Mathews, DR and Lakshmi, J (2019) Service resilience framework for enhanced end-to-end service quality. In: 18th Workshop on Adaptive and Reflexive Middleware, ARM 2019 - Part of Middleware 2019, 09-13 December 2019, Davis; United States, pp. 7-12.

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Abstract

A major risk that cloud computing paradigm encounters in enabling computing as a utility is service disruption. With component failures, performance interferences, load dynamics, etc., increasing with scale, it is challenging to meet user expectations concerning the delivery of cloud services. Prevalent cloud solutions deal with service disruptions as application design choices and mostly adopt to redundant or replicated service instances. This black box approach makes the choice expensive and obviates interesting and smarter choices while dealing with disruptions. More so, as clouds are subjected to dynamic variations in workload, resources and disruptions. This paper details disruption scenarios where autonomous service resilience features are useful, to motivate the work. Further, to address the observed lacunae in cloud setups, the paper proposes and describes a service resilience framework that brings in autonomous cross-layer consciousness across cloud service layers for delivering improved service resilience. This framework is analyzed and explained with specific use-cases for availability and performance with goal-driven service resilience. y.

Item Type:	Conference Paper
Publication:	ARM 2019 - Proceedings of the 2019 18th Workshop on Adaptive and Reflexive Middleware, Part of Middleware 2019
Publisher:	Association for Computing Machinery, Inc
Additional Information:	Copyright of this article belongs to Association for Computing Machinery, Inc
Keywords:	Distributed database systems; Middleware; Web services, Cloud services; Dynamic variations; End-to-end service; High availability; Performance; Replicated services; Service disruptions; Service resiliences, Quality of service
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	27 Feb 2020 09:29
Last Modified:	27 Feb 2020 09:29
URI:	http://eprints.iisc.ac.in/id/eprint/64608

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