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Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

With the increasing large number of cloud users, the number of tasks is growing exponentially. Scheduling and balancing these tasks amongst different heterogeneous virtual machines (VMs) under constraints such as, low makespan, high resource utilization rate, low execution cost and low scheduling time, become NP-hard optimization problem. So, due to the inefficiency of heuristic algorithms, many meta-heuristic algorithms, such as particle swarm optimization (PSO) have been introduced to solve the said problem. However, these algorithms do not guarantee that the optimal solution can be found, if they are not combined with other heuristic or meta-heuristic algorithms. Further, these algorithms have high time complexity, making them less useful in realistic scenarios. To solve the said NP-problem effectively, we propose an efficient binary version of PSO algorithm with low time complexity and low cost for scheduling and balancing tasks in cloud computing. Specifically, we define an objective function which calculates the maximum completion time difference among heterogeneous VMs subject to updating and optimization constraints introduced in this paper. Then, we devise a particle position updating with respect to load balancing strategy. The experimental results show that the proposed algorithm achieves task scheduling and load balancing better than existing meta-heuristic and heuristic algorithms.

Cloud computing is very popular because of its unique features such as scalability, elasticity, on-demand service, and security. A large number of tasks are performed simultaneously in a cloud system, and an effective task scheduler is needed to achieve better efficiency of the cloud system. Task scheduling algorithm should determine a sequence of execution of tasks to meet the requirements of the user in terms of Quality of Service (QoS) factors (e.g., execution time and cost). The key issue in recent task scheduling is energy efficiency since it reduces cost and satisfies the standard parameter in green computing. The most important aim of this paper is a comparative analysis of 67 scheduling methods in the cloud system to minimize energy consumption during task scheduling. This work allows the reader to choose the right scheduling algorithm that optimizes energy properly, given the existing problems and limitations. In addition, we have divided the algorithms into three categories: heuristic-based task scheduling, meta-heuristic-based task scheduling, and other task scheduling algorithms. The advantages and disadvantages of the proposed algorithms are also described, and finally, future research areas and further developments in this field are presented.

Task scheduling in cloud paradigm brought attention of all researchers as it is a challenging issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size, processing capacity and number of tasks to be scheduled. Therefore, ineffective scheduling technique may lead to increase of energy consumption SLA violations and makespan. Many of authors proposed heuristic approaches to solve task scheduling problem in cloud paradigm but it is fall behind to achieve goal effectively and need improvement especially while scheduling multimedia tasks as they consists of more heterogeneity, processing capacity. Therefore, to handle this dynamic nature of tasks in cloud paradigm, a scheduling mechanism, which automatically takes the decision based on the upcoming tasks onto cloud console and already running tasks in the underlying virtual resources. In this paper, we have used a Deep Q-learning network model to addressed the mentioned scheduling problem that search the optimal resource for the tasks. The entire extensive simulationsare performed usingCloudsim toolkit. It was carried out in two phases. Initially random generated workload is used for simulation. After that, HPC2N and NASA workload are used to measure performance of proposed algorithm. DRLBTSA is compared over baseline algorithms such as FCFS, RR, Earliest Deadline first approaches. From simulation results it is evident that our proposed scheduler DRLBTSA minimizes makespan over RR,FCFS, EDF, RATS-HM, MOABCQ by 29.76%, 41.03%, 27.4%, 33.97%, 33.57% respectively. SLA violation percentage for DRLBTSA minimized overRR,FCFS, EDF, RATS-HM, MOABCQ by48.12%, 41.57%, 37.57%, 36.36%, 30.59% respectively and energy consumption for DRLBTSA over RR,FCFS, EDF, RATS-HM, MOABCQ by36.58%,43.2%, 38.22%, 38.52%, 33.82%existing approaches.

Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms.

This paper proposes the combination of Swarm Intelligence algorithm of artificial bee colony with heuristic scheduling algorithm, called Heuristic Task Scheduling with Artificial Bee Colony (HABC). This algorithm is applied to improve virtual machines scheduling solution for cloud computing within homogeneous and heterogeneous environments. It was introduced to minimize makespan and balance the loads. The scheduling performance of the cloud computing system with HABC was compared to that supplemented with other swarm intelligence algorithms: Ant Colony Optimization (ACO) with standard heuristic algorithm, Particle Swarm Optimization (PSO) with standard heuristic algorithm and improved PSO (IPSO) with standard heuristic algorithm. In our experiments, CloudSim was used to simulate systems that used different supplementing algorithms for the purpose of comparing their makespan and load balancing capability. The experimental results can be concluded that virtual machine scheduling management with artificial bee colony algorithm and largest job first (HABC_LJF) outperformed those with ACO, PSO, and IPSO.

Cloud computing is an efficient technology to serve the requirement of big data applications. Minimizing the makespan of the cloud system while increasing resource utilization is important to reduce costs. In this case, task scheduling is a challenging task to meet the requirement because it requires both effectiveness and efficiency. This article proposes a task scheduler with several discrete variants of the particle swarm optimization (PSO) algorithm for task scheduling in cloud computing. In order to evaluate the performance, these approaches were compared with three well-known heuristic algorithms on task scheduling problems. Experiment results demonstrate the efficiency and effectiveness of the proposed approaches. For the proposed PSO-based scheduler, an appropriate choice is to use the logarithm decreasing strategy to provide an optimal scheduling scheme. The average makespan of the proposed PSO-based scheduler that adopts logarithm decreasing strategy is reduced by 19.12%, 21.42% and 15.14% relative to the compared gravitational search algorithm, artificial bee colony algorithm and dragonfly algorithm respectively.

Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.

The motivation of task scheduling in heterogeneous computing systems is the optimal management of heterogeneous distributed resources as well as the exploitation of system capabilities. Energy consumption is one of the most important issues in dealing with task scheduling in heterogeneous distributed systems. In addition to energy, the task completion time and the task cost have also been added to the concerns of the users. Since the nature of computing systems is heterogeneous and dynamic, task scheduling with traditional methods is inefficient. Meta-heuristic approaches for task scheduling in heterogeneous distributed systems are open problems that have attracted the attention of researchers. So far, many meta-heuristic approaches have addressed the task scheduling problem. However, most of these algorithms are developed for homogeneous systems and optimize only one of the quality-of-service parameters. With this motivation, this paper presents an optimization for energy-aware design of task scheduling in heterogeneous distributed systems using meta-heuristic approaches. We simultaneously consider several parameters such as energy, task completion time and task execution cost for task scheduling. The Harris Hawk Optimization (HHO) algorithm is considered for the optimization task due to its adaptability to large search spaces. We combine HHO with a greedy algorithm to avoid local optima and early convergence. The evaluation of the proposed method has been done through numerical simulations. Experimental results show promising performance of the proposed method in terms of energy consumption.

Scientific ensemble workflows are commonly executed in Infrastructure-as-a-Service clouds for high-performance computing. The dynamic pricing of spot instances offers a cost-effective way for users to rent cloud resources. However, these instances are subject to out-of-bid failures when their prices exceed the user’s bid, leading to task termination and disruptions in workflow execution. It is a great challenge to reduce costs while ensuring the quality of task completion. This paper addresses the problem of scheduling prioritized ensemble workflows using on-demand and spot instances, with the objective of maximizing the number of high-priority workflows completed while minimizing total cost. We propose a rules-based scheduling heuristic with hybrid provisioning, which includes task scheduling, dynamic provisioning, and spot monitoring processes. The proposed algorithm is evaluated by comparing it to existing algorithms for similar problems over two classic scientific workflow datasets, Montage and LIGO. The score for completing as many high-priority workflows as possible is calculated within the given deadline D. The results reveal that our proposed algorithm achieves an average 30% improvement in the RPD value at different deadline levels and task sizes than other baseline algorithms.