B. Gomathi¹, B. Saravana Balaji², V. Krishna Kumar³, Mohamed Abouhawwash^4,5,*, Sultan Aljahdali⁶, Mehedi Masud⁶, Nina Kuchuk⁷

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1771-1785, 2022, DOI:10.32604/iasc.2022.024052 - 24 March 2022

Abstract Cloud computing enables cloud providers to outsource their Information Technology (IT) services from data centers in a pay-as-you-go model. However, Cloud infrastructure comprises virtualized physical resources that consume huge amount of energy and emits carbon footprints to environment. Hence, there should be focus on optimal assignment of Virtual Machines (VM) to Physical Machines (PM) to ensure the energy efficiency and service level performance. In this paper, The Pareto based Multi-Objective Particle Swarm Optimization with Composite Mutation (PSOCM) technique has been proposed to improve the energy efficiency and minimize the Service Level Agreement (SLA) violation in… More >

Sangeetha Ganesan^*, Sumathi Ganesan

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 387-401, 2021, DOI:10.32604/iasc.2021.019024 - 11 August 2021

Abstract Cloud Computing has been economically famous for sharing the resources of third-party applications. There may be an increase in the exploitation of the prevailing Cloud resources and their vulnerabilities as a result of the aggressive growth of Cloud Computing. In the Cache Side Channel Attack (CSCA), the attackers can leak sensitive information of a Virtual Machine (VM) which is co-located in a physical machine due to inadequate logical isolation. The Cloud Service Provider (CSP) has to modify either at the hardware level to isolate their VM or at the software, level to isolate their applications.… More >

Muhammad Abdullah^1,*, Saad Ahmad Khan¹, Mamdouh Alenez², Khaled Almustafa³, Waheed Iqbal¹

Intelligent Automation & Soft Computing, Vol.26, No.1, pp. 13-25, 2020, DOI:10.31209/2018.100000047

Abstract An efficient placement of virtual machines (VMs) in a cloud datacenter is important to maximize the utilization of infrastructure. Most of the existing work maximises the number of VMs to place on a minimum number of physical machines (PMs) to reduce energy consumption. Recently, big data applications become popular which are mostly hosted on cloud datacenters. Big data applications are deployed on multiple VMs and considered data and communication intensive applications. These applications can consume most of the datacenter bandwidth if VMs do not place close to each other. In this paper, we investigate the More >