No abstract

No abstract

No abstract

Cloud computing is a paradigm in high performance computing, focuses on provisioning ubiquitous computing with the help of Software and/or Hardware Virtualization. In Mobile Cloud Computing (MCC), mobile/portable devices access cloud resources through wireless communication(GPRS/3G/WiFi etc). MCC enhances the mobility of the cloud user which solves cloud computing issues such as Unreliability, Quality-of-Service (QoS), etc. Recently QoS has emerged as a one of the challenging issue in MCC which impact to the large number of mobile users and businesses. The QoS in MCC degrades mainly due to its limited bandwidth, network congestion, user mobility, etc. In this paper, we have proposed a mobile cloud computing framework that facilitates the mobile client to access cloud services with a high degree of QoS based on the network condition of the connection. We proposed a new QoS based mobile cloud computing framework . Back Propagation Neural Network (BPNN) is being used for predicting and selecting appropriate cloud service for the mobile client. We have implemented the proposed framework taking QoS parameters: Packet Delivery Ratio (PDR), Transmission Rate, and Delay in a mobile cloud computing environment. At the end, we have compared our model with the random selection approach and it shows that the proposed model gives better performance.

Cloud computing supports the fast expansion of data and computer centers; therefore, energy and load balancing are vital concerns. The growing popularity of cloud computing has raised power usage and network costs. Frequent calls for computational resources may cause system instability; further, load balancing in the host requires migrating virtual machines (VM) from overloaded to underloaded hosts, which affects energy usage. The proposed cost-efficient whale optimization algorithm for virtual machine (CEWOAVM) technique helps to more effectively place migrating virtual machines. CEWOAVM optimizes system resources such as CPU, storage, and memory. This study proposes energy-aware virtual machine migration with the use of the WOA algorithm for dynamic, cost-effective cloud data centers in order to solve this problem. The experimental results showed that the proposed algorithm saved 18.6%, 27.08%, and 36.3% energy when compared with the PSOCM, RAPSO-VMP, and DTH-MF algorithms, respectively. It also showed 12.68%, 18.7%, and 27.9% improvements for the number of virtual machine migrations and 14.4%, 17.8%, and 23.8% reduction in SLA violation, respectively.