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Deadline and Reliability Aware Multiserver Configuration Optimization for Maximizing Profit | IEEE Journals & Magazine | IEEE Xplore

Deadline and Reliability Aware Multiserver Configuration Optimization for Maximizing Profit


Abstract:

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining pr...Show More

Abstract:

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining profit and highly depend on multiserver configuration. Understanding the relationship between multiserver configuration and profit is important to service providers. Although existing articles have explored this issue, few of them consider deadline miss rate and soft error reliability of cloud services in multiserver configuration for profit maximization. Since deadline misses violate cloud services’ real-time requirements and soft error prevents successful processing of cloud services, it is necessary to consider the impact of deadline miss rate and soft error reliability on service providers’ profits when configuring the multiserver. This article introduces a deadline miss rate and soft error reliability aware multiserver configuration scheme for maximizing cloud service providers’ profit. Specifically, we derive the deadline miss rate considering the heterogeneity of cloud service requests, and propose an analytical method to compute the soft error reliability of multiserver systems. Based on the new deadline miss rate and soft error reliability models, we formulate the multiserver configuration optimization problem and introduce an augmented Lagrange multiplier-based iterative method to find the optimal multiserver configuration. Extensive experiments evaluate the efficacy of the proposed multiserver configuration approach. Compared with the two state-of-the-art methods, the profit gained by our scheme can be up to 11.92% higher.
Published in: IEEE Transactions on Parallel and Distributed Systems ( Volume: 33, Issue: 12, 01 December 2022)
Page(s): 3772 - 3786
Date of Publication: 26 April 2022

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