Intelligent SLA-Aware VM Allocation and Energy Minimization Approach with EPO Algorithm for Cloud Computing Environment

Abstract

Cloud computing is the most prominent established framework; it offers access to resources and services based on large-scale distributed processing. An intensive management system is required for the cloud environment, and it should gather information about all phases of task processing and ensuring fair resource provisioning through the levels of Quality of Service (QoS). Virtual machine allocation is a major issue in the cloud environment that contributes to energy consumption and asset utilization in distributed cloud computing. Subsequently, in this paper, a multiobjective Emperor Penguin Optimization (EPO) algorithm is proposed to allocate the virtual machines with power utilization in a heterogeneous cloud environment. The proposed method is analyzed to make it suitable for virtual machines in the data center through Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). To compare with other strategies, EPO is energy-efficient and there are significant differences. The results of the proposed system have been evaluated through the JAVA simulation platform. The exploratory outcome presents that the proposed EPO-based system is very effective in limiting energy consumption, SLA violation (SLAV), and enlarging QoS requirements for giving capable cloud service.

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

“… In the future, the author in (Ababneh, 2021) suggested to use evolutionary algorithms such as PSO and GA, to solve the task scheduling problem  For future work, (Babu & Samuel, 2016) suggested enhancing the algorithm with hybridization of other nature inspired algorithms like Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc.  For future work, (Samriya, et al, 2021) the authors suggested extending their work with the combination of other methods for better energy efficiency and to consider other objectives like resource wastage of cloud computing VM placement.  For future work, (Madni, et al, 2019) suggested that hybridization of cuckoo search (CS) algorithm with other optimized heuristic and meta-heuristic algorithms may also prove to be beneficial for cloud computing environment.…”

“…The authors also want to improve the proposed algorithm using fuzzy theory. (Samriya, et al, 2021) have introduced a multi-objective algorithm using Emperor Penguin Optimization (EPO) technique to allocate virtual machines in a heterogeneous cloud environment to improve power utilization. The proposed algorithm is analyzed, and its performance is compared with three algorithms: the Binary Gravity Search Algorithm (BGSA), Ant Colony Optimization (ACO)-based algorithm, and Particle Swarm Optimization (PSO)-based algorithm.…”

Decision Scheduling for Cloud Computing Tasks Relying on Solving Large Linear Systems of Equations

“…The proposed method is analyzed through binary gravity search algorithm, ant colony optimization, and particle swarm optimization, which makes it suitable for virtual machines in data centers. Compared with other strategies, the algorithm he proposed is energy-efficient and has significant differences [ 5 ]. Jabir et al proposed an enhanced antlion optimization algorithm mixed with the popular particle swarm optimization algorithm to optimize workflow scheduling specifically for the cloud.…”

Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study

“…Although robots' resources have been improving in terms of energy, computation power, and storage, they still cannot satisfy the need of emerging applications [8]. As a solution, researchers focused on solutions that leverage the use of cloud computing [9]. A new paradigm has emerged, namely cloud robotics [8].…”