Optimization techniques for task scheduling criteria in IaaS cloud computing atmosphere using nature inspired hybrid spotted hyena optimization algorithm

Abstract

Summary Cloud computing has garnered unprecedented growth in recent years in the field of Information Technology. It has emerged as a high‐performance computing option owing to its infrastructure that comprises of heterogeneous collection of autonomous computers and adaptable network architecture. The tasks that are scheduled in an optimized manner for their execution could be classified under NP‐hard problems. Though meta‐heuristic scheduling algorithms emerge as scheduling options, they need to be much more consistent while dealing with the dynamic set up of the cloud environment. In this paper, we had proposed a multi‐objective meta‐heuristic scheduling algorithm namely Quasi Oppositional Genetic Spotted Hyena Optimization (QOGSHO) algorithm that globally optimizes the makespan, resource consumption and SLA violation QoS parameters, thereby improving the performance. The algorithm proposed is an amalgamated product of meta‐heuristic algorithms like Quasi Oppositional Based Learning (QOBL), Spotted Hyena Optimization (SHO), and Genetic Algorithm (GA). The performance efficiency of the proposed QOGSHO algorithm had been compared with various scheduling algorithms using uniform datasets by varying the data instance sizes in a simulated cloud environment. The obtained results clearly justify the task scheduling efficiency of the proposed algorithm with respect to the QoS parameters namely makespan, resource utilization and SLA violation.

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

“…It is difficult to identify solutions and converge towards solutions for any type of workload generated through various users. Therefore, the authors in [ 26 ] formulated a scheduling mechanism to address violations of SLA, resource utilization and makespan. A hybrid multi-objective technique, i.e., QOGSHO, was used as a methodology in this approach.…”

“…The model was compared to ACO, GA, PSO, and WOA algorithms and SOATS was compared to CJS, MSDE, and FUGE approaches, and it was observed that SOATS optimizes cost and energy by 10% and 25%, respectively. In [ 25 , 26 , 27 , 28 , 29 , 30 ], the authors concentrated mainly on datacenter processing time, QoS parameters, and task processing time, and only one author in [ 28 ] discussed failure rate as all the existing task-scheduling algorithms discussed various parameters. But many of the authors did not tackle the relationship between fault tolerance and trust-based parameters.…”

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

“…RCOA shown huge impact while minimizing makespan. Multi objective scheduling technique developed by authors in 26 using QOGSHO by combining QOBL, SHO by designing an adaptive fitness function to evaluate SLA Violation, makespan, resource utilization. This experimentation conducted on a customized cloud environment.…”

Heuristic-Driven Approach for Efficient Workflow Scheduling in Infrastructure as a Service Using Hybrid Optimization Algorithms

“…In our past web journal post, we expressed how to select between a private cloud and virtualization robotization. In this paper, we present the five questions you must ask after recently using IaaS [7][8][9]. IT infrastructure, like storage, servers, and networking resources, is managed by the cloud provider in the IaaS model and delivered to subscriber businesses via internet-accessible virtual machines.…”

“…Scheduling involves the allocation of virtual resources, the coordination of tasks, and the optimization of resource utilization. It plays a pivotal role in ensuring that cloud infrastructure operates at peak performance, meets service level agreements (SLAs), and makes the best use of available resources [8]. Infrastructure as a Service (IaaS) has revolutionized the way organizations manage and utilize computing resources.…”