Effective Load Balancing and Security in Cloud using Modified Particle Swarm Optimization Technique and Enhanced Elliptic Curve Cryptography Algorithm

Abstract

In recent decades, the rapid growth of cloud computing has led to increasing concerns about the security and energy requirement of cloud data centers. To overcome the concerns of load balancing and data security, a new hybrid model is proposed in this article. Firstly, a Modified Particle Swarm Optimization (MPSO) technique is proposed for balancing the tasks between heavy-loaded Virtual Machine (VMs) and light-loaded VMs. An effective load balancing improves the performance and availability of website, database, application and other computing resources. In the MPSO, a linear decreasing inertia weight is included to achieve optimal solutions in both VM migrations and load balancing. Secondly, an Enhanced Elliptic Curve Cryptography (EECC) algorithm is proposed for effective data security. In the proposed EECC algorithm, a new pseudo-random key is combined with a public key to improve the security of cloud data centers. In this work, the effectiveness of the proposed MPSO-EECC model is validated in terms of Service Level Agreement (SLA), execution time, energy consumption, energy SLA violation, encryption, and decryption comparison time. As represented in the experimental section, the proposed MPSO technique almost reduced 30%-70% of energy consumption compared to the existing optimization techniques. Similarly, the proposed EECC algorithm reduced 10ms to 30ms of decryption comparison time related to the comparative cryptography algorithms