Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization

Abstract

To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle Swarm Optimization is suitable for continuous data optimization, to use in discrete data as in the case of Virtual Machine placement we need to fine-tune some of the parameters in Particle Swarm Optimization. The Virtual Machine placement problem is addressed by our proposed model called Improved Particle Swarm Optimization (IM-PSO), where the main aim is to maximize the utilization of resources in the cloud datacenter. The obtained results show that the proposed algorithm provides an optimized solution when compared to the existing algorithms.

Particle Swarm Algorithm for Smart Contract Vulnerability Detection Based on Semantic Web

“…First, smart contracts will appear more homogeneous than traditional programs, which makes the matching effort more vulnerable to attacks by code segments unrelated to the vulnerability logic, the so-called "noise" (Madhumala & Tiwari, 2022). In the vulnerability contract shown in Figure 1, only lines 3 and 4 of the code are relevant to the vulnerability logic.…”