Cloud Computing Task Scheduling Method Based on a Coral Reefs Optimization Algorithm Task scheduling is a difficult nondeterministic polynomial problem. Optimization of the scheduling algorithm is the key to improve the efficiency of cloud computing. The traditional metaheuristic algorithm has slow convergence rate and is easy to fall into local optimal value. This paper proposes a new scheduling method based on a coral reefs algorithm. Firstly, the task scheduling model is formally described. The objective function is proposed to calculate load balancing rate, resource utilization and load balancing stability. Then the representation method of coral reef and the coding scheme of polyps are designed. Matrix random mapping method is applied to improve the variation effect of polyps. Finally, Ant Colony Optimization(ACO), the Genetic Algorithm(GA) andRound Robin(RR) algorithms are compared in terms of completion time, convergence effect and resource load. The simulation results show that the coral reef algorithm has reduced the completion time by 6.4%, 25.1%, 51.3%, and increased resource utilization by 10.0%, 15.2% and 51.3% when it is compared with the other three algorithms. It shows that the coral reef algorithm is suitable for task scheduling in the cloud environment.

Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment Abstract   Cloud computing is a new paradigm which provides subscriptionoriented services. Scheduling the tasks in cloud computing environments is a multigoal optimization problem, which is NP hard. To exaggerate task scheduling performance and reduce the overall Makespan of the task allocation in clouds, this paper proposes two scheduling algorithms named as TBTS (Threshold based Task scheduling algorithm) and SLALB (Service level agreementbased Load Balancing) algorithm. TBTS is twophase scheduling algorithm which schedules the tasks in a batch. It supports task scheduling in virtual machines with distinct configuration. Furthermore, in TBTS algorithm, threshold data generated based on the ETC (Expected Time to Complete) matrix. Virtual machines which execute tasks with the estimated execution time lesser than threshold value are allocated to the particular task. SLALB algorithm is a online model which schedules the task dynamically, based on the requirement of clients, like deadline and budget as the two criteria. Prediction based scheduling is implemented in TBTS to increase the system utilization and to improve the load balancing among the machines by allocation of the minimum configuration machine to the task, based on predicted robust threshold value. SLALB uses the level of agreement and finds the required system to reduce the Makespan and increase the cloudutilization. Simulation of proposed algorithms is performed with benchmark datasets (Braun, 2015) and synthetic datasets are generated with random functions. The proposed TBTS and SLALB final values of the proposed algorithms are analyzed with assorted scheduling models, namely SLAMCT, FCFS, EXIST, LBMM, Lbmaxmin, MINMIN and MAXMIN algorithms. Performance metrics such as Makespan, penalty, gain cost and also the VM utilization factor of proposed algorithm compared with existing algorithms. The comparison analysis among various existing algorithms with TBTS and SLALB algorithms show that the proposed methods outperform existing algorithms, even in the scalability situation of the dataset and virtual machines.