An Effective Secured Dynamic Network-Aware Multi-Objective Cuckoo Search Optimization for Live VM Migration in Sustainable Data Centers

Abstract

With the increasing use of cloud computing by organizations, cloud data centers are proliferating to meet customers’ demands and host various applications using virtual machines installed in physical servers. Through Live Virtual Machine Migration (LVMM) methods, cloud service providers can provide improved computing capabilities for server consolidation maintenance of systems and potential power savings through a reduction in the distribution process to customers. However, Live Virtual Machine Migration has its challenges when choosing the best network path for maximizing the efficiency of resources, reducing consumption, and providing security. Most research has focused on the load balancing of resources and the reduction in energy consumption; however, they could not provide secure and optimal resource utilization. A framework has been created for sustainable data centers that pick the most secure and optimal dynamic network path using an intelligent metaheuristic algorithm, namely, the Network-aware Dynamic multi-objective Cuckoo Search algorithm (NDCS). The developed hybrid movement strategy enhances the search capability by expanding the search space and adopting a combined risk score estimation of each physical machine (PM) as a fitness criterion for ensuring security with rapid convergence compared to the existing strategies. The proposed method was assessed using the Google cluster dataset to ascertain its worthiness. The experimental results show the supremacy of the proposed method over existing methods by ensuring services with a lower total migration time, lower energy consumption, less makespan time, and secure optimum resource utilization.

Reliability-Constrained Task Scheduling for DAG Applications in Mobile Edge Computing

“…As the cuckoo search (CS) algorithm [20] has the advantages of simplicity, high robustness, and high efficiency, it has been employed to solve multiobjective optimization problems in a variety of fields, including network awareness, resource allocation, and task scheduling, etc. [21][22][23]. These research studies indicate that it outperforms traditional optimization algorithms such as whale optimization and hybrid bee colony optimization, among others.…”

A New Hybrid Algorithm Based on the Firefly Algorithm and Cuckoo Search to Estimate Survival Analysis of Truncated Inverse Gompertz Distribution

“…Cuckoo search (CS) was created in 2009 by Suash Deb and Xin-She Yang. The CSA is a population based meta-heuristic algorithm inspired by the reproductive behaviors of the cuckoo bird [27].Cuckoo birds may remove other eggs from communal nests where they lay their eggs in order to increase the probability of hatch [28,29]. Because their eggs hatch before the host bird's eggs, the cuckoos lay their eggs in a nest where the host bird had just laid its own eggs.…”

A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

“… Agility: It allows businesses to quickly adapt to changing market conditions [13].  Security [14]: It offers high levels of security through encryption and data backups [15].  Efficiency [16]: CC enables businesses to access computing resources and optimize their IT infrastructure more efficiently.…”

“…A cloud computing environment that operates in real-time must be used to assess the proposed method. Venkata Subramanian et al [14] Dynamic multi-objective networkaware Cuckoo Search Method…”