Optimization of Load Balancing and Task Scheduling in Cloud Computing Environments Using Artificial Neural Networks-Based Binary Particle Swarm Optimization (BPSO)

Abstract

As more people utilize the cloud, more employment opportunities become available. With constraints such as a limited make-span, a high utilization rate of available resources, minimal execution costs, and a rapid turnaround time for scheduling, this becomes an NP-hard optimization issue. The number of solutions/combinations increases exponentially with the magnitude of the challenge, such as the number of tasks and the number of computing resources, making the task scheduling problem NP-hard. As a result, achieving the optimum scheduling of user tasks is difficult. An intelligent resource allocation system can significantly cut down the costs and waste of resources. For instance, binary particle swarm optimization (BPSO) was created to combat ineffective heuristic approaches. However, the optimal solution will not be produced if these algorithms are not paired with additional heuristic or meta-heuristic algorithms. Due to the high temporal complexity of these algorithms, they are less useful in real-world settings. For the NP problem, the binary variation of PSO is presented for workload scheduling and balancing in cloud computing. Considering the updating and optimization constraints stated in this research, our objective function determines if heterogeneous virtual machines (VMs) Phave the most significant difference in completion time. In conjunction with load balancing, we developed a method for updating the placements of particles. According to the experiment results, the proposed method surpasses existing metaheuristic and heuristic algorithms regarding work scheduling and load balancing. This level of success has been attainable because of the application of Artificial Neural Networks (ANN). ANN has demonstrated promising outcomes in resource distribution. ANN is more accurate and faster than multilayer perceptron networks at predicting targets.

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

“…For the cloud provider, it is difficult to map a large number of exponential increase in heterogeneous user tasks to various heterogeneous resources unless there is an intelligent task scheduler. For this, the authors in [ 15 ] formulated a task-scheduling algorithm to cut down costs and unnecessary usage of resources in the cloud environment. In this approach, a binary operator was used to place particles at certain places to search the solution space.…”

Dynamic Stability Evaluation of an Integrated Biodiesel-Geothermal Power Plant-Based Power System with Spotted Hyena Optimized Cascade Controller

“…The SHy trace victims using their highly developed sense of sight, hearing, and smell. This behavior of SHy led Dhiman et al [57] to develop a meta-heuristic algorithm, i.e., SHO. The authors created an arithmetical strategy based on SHy and mutual dexterity for optimization.…”

“…The enactment of any subordinate controllers is dominant solitary if the finest values of attributes are suitably selected. These could be prepared with the aid of traditional/optimization practices [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]. However, the traditional practice has moderate difficulty to deliver suboptimum consequences.…”

“…A few algorithms used for AGC are bacterial foraging optimization (BFO) [10], whale optimization algorithm (WOA) [19], sine-cosine algorithm (SCA) [20], cuckoo search (CS) [29], particle swarm optimization (PSO) [33], coyote optimizer algorithm (COA) [37], bat algorithm (BA) [39], salp swarm algorithm [50], grey wolf [51], firefly algorithm (FA) [53], flower pollination algorithm (FPA) [54], differential evolution (DE) [55], and Jaya algorithm [56]. A newly established bioinspired meta-heuristic procedure labeled as SHO has also been developed [57]. SHO was recognized from spotted hyenas' behavioral nature, which depicts the communal promise amidst spotted hyena and their cooperative performances.…”

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

“…It is a challenging problem in this paradigm as variety of requests from heterogeneous resources comes to cloud application console where the scheduler need to look up all those requests and it should assign it to an appropriate suitable VM which can process this request. Many of existing authors proposed various task scheduling algorithms such as MOABCQ 4 , RATSHM 5 , AINN-BPSO 6 which are modelled based on metaheuristic approaches but still these approaches are not focused on failure rate, resource cost, SLA based trust parameters. Ineffective scheduling of tasks leads to increase in delay of processing tasks thereby increase in makespan, resource costs, execution time, energy consumption.…”