An Efficient Task scheduling and Data security in Heterogeneous Cloud Computing using Hybrid Meta-Heuristic Algorithm and Block Chain based Key Aggregate Cryptosystem

Abstract

Cloud computing is a rapidly growing technology which is hugely used in various fields and it offers a strong and fashionable corporate environment. However, cloud computing environment faces many challenges in scheduling and securing the huge amount of data provided by various users. To overcome the conventional performance limitations in resource scheduling and data security various optimization and encryption models are evolved in recent times. However, the performances can be improved if hybrid models are employed. Thus, a hybrid Ant Colony Optimization-Variable Neighborhood Search(ACO-VNS) metaheuristic algorithm is presented in this research work to improved task scheduling. Furthermore, to handle user data security and privacy a combination of Attribute Based Encryption (ABE) with (BKAC) blockchain based key aggregate cryptosystem technique is presented. simulation analysis includes standard benchmark datasets and the performances are compared to existing techniques such as Energy -Efficient Dynamic Scheduling Scheme (EDS), Cross Entropy based Virtual Machine (CEVP), Meta-Heuristic Crow Search Algorithm (H3CSA), PPSO (Parallel Particle Swarm Optimization) and Block Chain based CP-ABE Scheme with Partially Hidden Access Policy (BCP-ABE-PHAS) to validate the superior performance. The experimental result demonstrates the performance of the proposed meta-heuristic and ABE-BKAC model in terms of completion ratio, response time, make span, energy consumption, keygen time, Encryption Time and Decryption Time.