Task Scheduling Optimization in Cloud Computing by Rao Algorithm

Abstract

Cloud computing is currently dominated within the space of highperformance distributed computing and it provides resource polling and ondemand services through the web. So, task scheduling problem becomes a very important analysis space within the field of a cloud computing environment as a result of user's services demand modification dynamically. The main purpose of task scheduling is to assign tasks to available processors to produce minimum schedule length without violating precedence restrictions. In heterogeneous multiprocessor systems, task assignments and schedules have a significant impact on system operation. Within the heuristic-based task scheduling algorithm, the different processes will lead to a different task execution time (makespan) on a heterogeneous computing system. Thus, a good scheduling algorithm should be able to set precedence efficiently for every subtask depending on the resources required to reduce (makespan). In this paper, we propose a new efficient task scheduling algorithm in cloud computing systems based on RAO algorithm to solve an important task and schedule a heterogeneous multiple processing problem. The basic idea of this process is to exploit the advantages of heuristic-based algorithms to reduce space search and time to get the best solution. We evaluate our algorithm's performance by applying it to three examples with a different number of tasks and processors. The experimental results show that the proposed approach significantly succeeded in finding the optimal solutions than others in terms of the time of task implementation.

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

“…Technique Used Addressed Parameters [7] APSO Makespan, throughput [8] LJ-PSO, M-PSO makespan, total execution time, degree of imbalance [9] GAGELS makespan, resource utilization [10] MPSO Makespan, resource utilization [11] IT2FCM Data movements, data placement, makespan [12] PSO-RDAL Response time, task deadline, penalty cost [13] EPSOCHO Makespan, processing cost, resource utilization [14] GSOS Makespan, cost [15] AINN-BPSO makespan, cost, degree of imbalance [16] QPSO Scheduling efficiency [17] MVO-GA Task transfer time [18] NSGAIII runtime, cost, power consumption [19] Hybrid Lion-GA Load balancing [20] GSAGA Makespan [21] GBO Makespan, accuracy of scheduling [22] HWOA-MBA Makespan, cost [23] IWHOLF-TSC Makespan, cost [24] HWACOA Makespan, cost ELHHO Schedule length, execution cost, resource utilization [28] RATSA Failure rate [29] SOATS Cost, energy consumption [30] HunterPlus Energy consumption, job completion rate [31] IQSSA QOS parameters [32] RAO Makespan [33] HFSGA Makespan, cost [34] DRL Makespan, throughput [35] IMOMVO Execution time, throughput [36] HBSFD Task processing time, turnaround time [37] Wale Disk space [38] Docker Containers Disk space…”

A Bi-objective Game-based Task Scheduling Method in Cloud Computing Environment

“…This method minimizes user payment costs and task scheduling time. Youne et al [10] set priorities for tasks and use the proposed RAO algorithm to find execution time optimal solutions for tasks based on user demand scheduling policies. Tong et al [11] provided an efficient scheduling solution by exploiting the adaptive learning capability of the dual-depth Q-network to shorten the response time while guaranteeing task completion.…”