Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.
The devices used for information and communication purpose consume an increasing amount of energy. As a result, the datacenters consume a lot of energy. Due to the expanding use of cloud services, there is considerable interest in reducing the energy consumption of datacenters. Effective resource utilization techniques and reduction in energy consumption are two crucial metrics in today's cloud computing environment. Many issues come along with contradicting objectives, viz., minimizing energy consumption raising the expenses of service supply. Autoscaling features of the cloud can bundle these issues. The major hurdle for effective resource scalability is the lack of proper management of energy supply to devices of servers in the datacenter. The main objective of the article is to supply energy to devices as per the allocated job-size on the interaction of complex energy, to increase the energy utilization of servers. Also, using renewable energy (RE) to supplement the operational energy of the cloud datacenter.In this direction, the article presents a predator-prey-based mathematical model for energy scaling that leverages the accessibility of RE source to decrease the high energy demands of the datacenter. It is formalized using ecological concepts of metabolism and allometric scaling. An allometric energy scaling (AES) algorithm is presented. The performance and evaluation of the proposed algorithm are implemented with the Cloudsim 4.0 simulator. The results are compared with the HEFT algorithm, and AES algorithm gives slightly better results in the case of optimum energy consumption and processing cost compared with the HEFT algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.