An energy optimization with improved QOS approach for adaptive cloud resources

Abstract

In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (ACRR) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model ACRR in terms of average run time, power consumption and average power required than any other state-of-art techniques.

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

“…Where, the single service class 𝐶 𝑗 contains f (f>1) functionally equivalent services with different QoS values. So, the quality of SC can be determined using (3).…”

“…It 639 is deployed according to three deployment models: public, private, and hybrid models. Furthermore, software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are the three service categories it provides [3], [4]. These services draw in more taking into account scaling up and down, dependable resources for high-performance hardware and software, as well as eliminating security risks and maintenance costs.…”

“…Henceforth, blending IT and services is commendable for present-day intelligent systems. Users can carry out their requirements identified with education, transportation, commerce, social networking, and numerous other utility cloud services [3], [5]. The foremost critical objective for users is to effectively utilize their requested services for lower operational cost, increasing scalability, improving performance, and utilizing their resources in efficient ways.…”