Design of Intelligent Scheduling Algorithms for Cloud Computing

The humungous amount of fluctuating data is generated from every walk of life, such as cloud users, health care, IoT devices, high-performance computing data a daily basis. Therefore, it is of utmost essential to process those data within a determined span of time. Hence, traditional cluster computing or grid computing is unsuitable for processing those tremendous data, both parallelly and distributed fashion among multi-computing systems. The advent of cloud computing has paved the way to be a viable option for scheduling as well as balancing the loads. Since the nature of data is dynamic, independent and non-preemptive, cloud computing appears to be a prominent solution as it offers a virtualization technology for the dynamic scheduling of cloud requests. The cloud requests should get serviced within a satisfactory time in order to reduce the response time and completion time while effectively improving resource utilization. Since task scheduling is an NP-hard problem, it is thus required to be executed within a polynomial time so as to achieve overall performance. In order to make this possible, some effective mechanisms need to have in place. Due to the complexity of the tasks that can be performed in cloud computing, many metaheuristics, such as BSO, Modified PSO, chaotic JAYA and combined genetic Algorithm with JAYA and quantum-inspired binary chaotic salp swarm algorithm (QBCSSA) as hybrid algorithms are planned to address the various issues associated with the dynamic task scheduling in this paradigm. These algorithms are optimized to provide consistent and robust results. The continuous solutions are transformed into discrete solutions using binary algorithms for representing tasks–VMs assignment in cloud computing. Both task and resource heterogeneities are taken into account to assess the effectiveness of the implemented algorithms. The CloudSim is used as a simulation tool to experiment with the disparate test cases with the considered tasks set and heterogeneous resources. The conflicting quality of service (QoS) scheduling parameters are considered for appraising the efficacy of the proposed algorithms. The real-world benchmark datasets considering both dependent and independent tasks are considered to authenticate the diversifying nature of planned algorithms. This work is apparent as of the simulation outcome where chaotic JAYA (one of the variants of JAYA) and QBCSSA outperform among aforementioned metaheuristic and hybrid algorithms.