The increasing demand of computational and storage resources is shifting users toward the adoption of cloud technologies. Cloud computing is based on the vision of computing as utility, where users no more need to buy machines but simply access remote resources made available on-demand by cloud providers. The relationship between users and providers is defined by a service-level agreement, where the non-fulfillment of its terms is regulated by the associated penalty fees. Therefore, it is important that the providers adopt proper monitoring and managing strategies. Despite their reduced application, intelligent agents constitute a feasible technology to add autonomic features to cloud operations. Furthermore, the volunteer computing paradigm—one of the Information and Communications Technology (ICT) trends of the last decade—can be pulled alongside traditional cloud approaches, with the purpose to ‘green’ them. Indeed, the combination of data center and volunteer resources, managed by agents, allows one to obtain a more robust and scalable cloud computing platform. The increased challenges in designing such a complex system can benefit from a simulation-based approach, to test autonomic management solutions before their deployment in the production environment. However, currently available simulators of cloud platforms are not suitable to model and analyze such heterogeneous, large-scale, and highly dynamic systems. We propose the AVOCLOUDY simulator to fill this gap. This paper presents the internal architecture of the simulator, provides implementation details, summarizes several notable applications, and provides experimental results that measure the simulator performance and its accuracy. The latter experiments are based on real-world worldwide distributed computations on top of the PlanetLab platform.

AVOCLOUDY: a simulator of volunteer clouds / Sebastio, Stefano; Amoretti, Michele; Lafuente, Alberto Lluch. - In: SOFTWARE, PRACTICE AND EXPERIENCE. - ISSN 1097-024X. - 46:1(2016), pp. 3-30. [10.1002/spe.2345]

AVOCLOUDY: a simulator of volunteer clouds

SEBASTIO, STEFANO;AMORETTI, Michele;
2016

Abstract

The increasing demand of computational and storage resources is shifting users toward the adoption of cloud technologies. Cloud computing is based on the vision of computing as utility, where users no more need to buy machines but simply access remote resources made available on-demand by cloud providers. The relationship between users and providers is defined by a service-level agreement, where the non-fulfillment of its terms is regulated by the associated penalty fees. Therefore, it is important that the providers adopt proper monitoring and managing strategies. Despite their reduced application, intelligent agents constitute a feasible technology to add autonomic features to cloud operations. Furthermore, the volunteer computing paradigm—one of the Information and Communications Technology (ICT) trends of the last decade—can be pulled alongside traditional cloud approaches, with the purpose to ‘green’ them. Indeed, the combination of data center and volunteer resources, managed by agents, allows one to obtain a more robust and scalable cloud computing platform. The increased challenges in designing such a complex system can benefit from a simulation-based approach, to test autonomic management solutions before their deployment in the production environment. However, currently available simulators of cloud platforms are not suitable to model and analyze such heterogeneous, large-scale, and highly dynamic systems. We propose the AVOCLOUDY simulator to fill this gap. This paper presents the internal architecture of the simulator, provides implementation details, summarizes several notable applications, and provides experimental results that measure the simulator performance and its accuracy. The latter experiments are based on real-world worldwide distributed computations on top of the PlanetLab platform.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2797684
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