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Reisi, Zahra, Bushehrian, Omid, Rezaeian Zadeh, Farahnaz. (1394). A Workload-Adaptable Method for Migrated Software to the Cloud. , 2(4), 271-279.
Zahra Reisi; Omid Bushehrian; Farahnaz Rezaeian Zadeh. "A Workload-Adaptable Method for Migrated Software to the Cloud". , 2, 4, 1394, 271-279.
Reisi, Zahra, Bushehrian, Omid, Rezaeian Zadeh, Farahnaz. (1394). 'A Workload-Adaptable Method for Migrated Software to the Cloud', , 2(4), pp. 271-279.
Reisi, Zahra, Bushehrian, Omid, Rezaeian Zadeh, Farahnaz. A Workload-Adaptable Method for Migrated Software to the Cloud. , 1394; 2(4): 271-279.

A Workload-Adaptable Method for Migrated Software to the Cloud

Journal of Computing and Security
مقاله 2، دوره 2، شماره 4، دی 2015، صفحه 271-279    اصل مقاله (1015.92 K)
نویسندگان
Zahra Reisi1؛ Omid Bushehrian* 1؛ Farahnaz Rezaeian Zadeh2
1Computer and IT Dep., shiraz University of Tech.
2Foolad Institute of Technology
چکیده
Due to the elastic nature of the cloud environments, migration of the legacy software systems to the cloud has become a very attractive solution for service providers. To provide Software-as-a-Service (SaaS), an application provider has to migrate his software to the cloud infrastructure. The most challenging issue in the migration process is minimizing the cloud infrastructure (VM's) costs while preserving the quality requirements of the service consumers. In this paper a self-adaptive method for migrated applications to the cloud is proposed in which an intelligent auto-scaling component continuously monitors the incoming application workloads and performs vertical or horizontal scaling. Since reacting to the transient workload changes always results in useless sequences of ``acquire-release'' actions of cloud resources and imposes unwanted overhead costs on the service provider, the auto-scaling component recognizes the transient workloads using a Learning Automata and only reacts to the stable ones. The OpenStack platform is used for evaluating the applicability of proposed method in real cloud environments. The experimental results demonstrate the ability of the proposed method in recognizing the transient workloads and consequently reducing the overall costs of the service provider.
کلیدواژه‌ها
Migration to Cloud؛ Auto-Scaling؛ Learning Automata؛ Transient Workload
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