Resource scheduling techniques optimization using cuckoo search based algorithm for cloud computing

Cloud computing is the virtualization technology that becomes a milestone and deals with many services across the Internet. In cloud computing, resource scheduling is the main process that assigns the precise and accurate task to CPU, network and storage. The aim behind the scheduling is the optimum usage of resources. Moreover, well-organized scheduling is needed for both cloud providers and cloud users. Optimal resource scheduling is required due to the limited resources, resource heterogeneity, environmental requirements, locality limitations and dynamic types of resource demand in a cloud computing environment. Recently, a number of heuristics and meta-heuristics optimization techniques have been applied to address the challenges of resource scheduling in cloud computing without emphasis on the issues of optimization. In this thesis, resource scheduling techniques using the enhancement of nature-inspired Cuckoo Search (CS) meta-heuristic scheduling algorithm for Infrastructure-as-a-Service (IaaS) in cloud computing were proposed. The Hybrid Gradient Descent Cuckoo Search (HGDCS) technique was first presented to enhance the convergence rate for resource scheduling. Then the Multi-Objective Cuckoo Search Optimization (MOCSO) technique was presented for multiple optimizations parameters precisely, the cost, makespan and utilization. Finally, the Fuzzy Cuckoo Search (FCS) technique was used as a reliable resource scheduling for IaaS in a cloud computing environment. Experiments were carried out using CloudSim simulator and the comparison results of HGDCS, MOCSO and FCS techniques with MaxMin, Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Honey Bee (HB), League Championship Algorithm (LCA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) scheduling algorithms were presented. The experimental results and statistical analysis showed that the proposed HGDCS, MOCSO and FCS techniques produced remarkable performance improvement rate on the cost (61%), degree of imbalance (25.58%), failure rate (39.21%), makespan (19.16%), reliability (35.72%), throughput (21.93%) and utilization (12.37%), respectively. Based on experimental results, the proposed techniques provided better-quality and optimized solutions that were suitable for resource scheduling for IaaS in a cloud computing environment. In the future, the implementation help to provide solutions for the specific problem in cloud computing such as decision making, security, green computing, big data, Internet of Things(IoT) and CloudIoT