The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Moreover, consuming services exposed by different providers may alleviate the vendor lock-in issue. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get more challenging with large and complex applications. In this paper we propose QBROKAGE, a genetic approach for Cloud Brokering, aiming at finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of cloud applications. Our approach is capable of evaluating such requirements both for the single application service and for the application as whole. We performed a set of experiments with an implementation of such broker, by considering three-tier applications and scientific application workflows. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, providing optimized deployment solutions that includes data transferring cost across multiple clouds. (C) 2017 Elsevier B.V. All rights reserved.

QoS-aware genetic Cloud Brokering

Dazzi P
2017

Abstract

The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Moreover, consuming services exposed by different providers may alleviate the vendor lock-in issue. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get more challenging with large and complex applications. In this paper we propose QBROKAGE, a genetic approach for Cloud Brokering, aiming at finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of cloud applications. Our approach is capable of evaluating such requirements both for the single application service and for the application as whole. We performed a set of experiments with an implementation of such broker, by considering three-tier applications and scientific application workflows. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, providing optimized deployment solutions that includes data transferring cost across multiple clouds. (C) 2017 Elsevier B.V. All rights reserved.
Anastasi, Gf; Carlini, E; Coppola, M; Dazzi, P
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11568/1143848
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