For the reenactment of internet and cloud technologies, interoperability is an important touchstone. Most of the consumers are analyzing the appropriateness of cloud to employ themselves for their enterprise or personalized operations. Customers are self-satisfied at the inception, but expectation changes. Based on their business escalation it needs further adoption of modern cloud services the existing cloud provider fails to offer. Hence the user needs interoperability and portability to ship their assets from one cloud to other cloud. The complication faced by the customers in shifting their assets remains as a challenge to be addressed. In this paper we outline the existing cloud technologies, interoperability issues and possible solution to overcome the problems

Virtual machine placement in cloud computing considering multiple objectives is one of the significant issues in modern virtualized datacenters. Many businesses and organizations are outsourcing their computational workload to the cloud datacenters, which increases datacenter energy consumption and emission of CO2. In particular, allocating a virtual machine to a physical server in the community cloud model is even challenging due to its dynamic nature. Unlike public clouds, cloud servers are not always available in the same location. In this paper, a bio-inspired bat algorithm using decomposition (MOBA/D) is proposed to reduce three different objectives namely minimization of power consumption, minimization of network latency, and maximization of economical revenue. The performance of the proposed algorithm is compared with other multi-objective algorithms in terms of feasible solutions and execution time.

Current internet environment has demanded the need for cloud computing technology in handling large information. The paper’s central theme is to evaluate the effectiveness of resources in managing scientific processes in a cloud environment by scheduling, capacity planning, and auto-scaling. Current systems have been linked with high costs because of disturbance of the flow, unsuccessful jobs, inability to meet certain cutoff times and long make span times. Therefore, to overcome these challenges, this research presents the Conditional Random Field-based Incremental Auto-scaling algorithm (CRF-IASA) to improve the cloud efficiency. Here, the algorithm is accountable for cases of data, memory as well as CPU, making use of DAG to position VMs and also distribute loads. The evaluation of the CRF-IASA is done with a help of the following sample workflows: A Cybershake 1000, Montage 1000, LIGO 1000 and Epigenome 997. Thus, according to the values obtained in the experiment, it is proved that the proposed auto-scaling algorithm is effective with the identified evaluation criteria: This is the total time needed to complete all activities, the energy used in the computing process, the costs incurred, and the rate at which the activities are completed in a schedule. Therefore, the study demonstrates the potential of utilizing CRF-IASA to obtain increased results concerning cloud performance and resource’s consumption with less costs in a more adaptable way.