A Task Scheduling Approach for Cloud Environments Employing Evolutionary Algorithms

2021 ◽  
Vol 13 (2) ◽  
pp. 423-438
Author(s):  
B. Lakhani ◽  
A. Agrawal
Keyword(s):  
Evolutionary Algorithms ◽  
Response Time ◽  
Task Scheduling ◽  
Gradient Descent ◽  
Virtual Machines ◽  
Sufficient Information ◽  
Swarm Optimization ◽  
Cloud Environments ◽  
Time Critical ◽  
Cloud Resources

One of the key challenges in the domain of cloud computing is task scheduling and estimation of cloud workloads for time critical applications pertaining to constrained cloud resources. While effective task scheduling is necessary for balancing the load, workload forecasting is necessary to plan in advance the requirements of cloud platforms based on previous data so as to effectively utilize cloud resources. Often it is challenging to gather sufficient information about the tasks and hence allocating the tasks to virtual machines (VMs) in the most optimal way is non-trivial. In this paper, a hybrid task scheduling approach is proposed based on evolutionary algorithms. The first approach is the amalgamation of bat and particle swarm optimization (PSO) techniques. The scheduling approach also combines the processing time preemption (PTP) approach to schedule the source intensive tasks which allows to reduce the response time of the proposed system.  The second approach is a machine learning based approach employing gradient descent with momentum (GDM). The evaluation of the proposed system has been done based on the response time and mean square error of the system.

A PSO Algorithm Based Task Scheduling in Cloud Computing

2019 ◽  
Vol 10 (4) ◽  
pp. 1-17 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Pso Algorithm ◽  
Emerging Technology ◽  
The Internet ◽  
Hard Problem ◽  
Swarm Optimization ◽  
Np Hard Problem ◽  
Simulation Results

Cloud computing is an emerging technology which involves the allocation and de-allocation of the computing resources using the internet. Task scheduling (TS) is one of the fundamental issues in cloud computing and effort has been made to solve this problem. An efficient task scheduling mechanism is always needed for the allocation to the available processing machines in such a manner that no machine is over or under-utilized. Scheduling tasks belongs to the category of NP-hard problem. Through this article, the authors are proposing a particle swarm optimization (PSO) based task scheduling mechanism for the efficient scheduling of tasks among the virtual machines (VMs). The proposed algorithm is compared using the CloudSim simulator with the existing greedy and genetic algorithm-based task scheduling mechanism. The simulation results clearly show that the PSO-based task scheduling mechanism clearly outperforms the others as it results in almost 30% reduction in makespan and increases the resource utilization by 20%.

scholarly journals Efficient Task Scheduling for Quality of Service in Cloud Computing Network

2020 ◽  
Vol 8 (5) ◽  
pp. 3193-3196
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Response Time ◽  
Task Scheduling ◽  
Waiting Time ◽  
Threshold Values ◽  
Specific Time ◽  
Required Quality ◽  
Cloud Resources

Task scheduling in cloud is the process of allocating a resource to a task at specific time. The allocation of limited cloud resources to large number of tasks to satisfy the required quality of service is the key challenge in cloud. Allocation of a resource with less capability to a task increases the response time, makespan of the task and waiting time of the entire tasks in the waiting queue. This problem will result to an unsatisfied Quality of Service. In this paper we proposed an efficient task scheduling that uses three threshold values to specify the resource to be allocated to a task at a given time. This method ensures that a capable resource is allocated to task such that the response time and makespan of the all task are minimized. The proposed method was simulated using CloudSim and the result shows a better response time and makespan than the well known Min-Min and Max-Min Method.

scholarly journals Improved Task Scheduling using Effective Particle Swarm Optimization in Cloud Computing Environment

2019 ◽  
Vol 9 (2) ◽  
pp. 1232-1237
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
High Performance ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Active Management ◽  
Swarm Optimization ◽  
Effective Particle ◽  
Cloudlet Scheduling

A vibrant on demand service of today’s era is cloud computing where one can utilize computer resources without indirect active management by user where one can use computing resources to achieve coherence in economic scale. Since cloud computing feel like Everything as a service so there should be highly scalable and reliable mechanisms to distribute the load evenly across the VMs evenly. Innumerable cloudlet mapping policies are presented in various research articles to achieve the high performance, better QOS and minimized task execution time but maximum are conventional approaches. No unconventional realistic scheduling algorithms is available which can schedule the tasks in heterogeneous manner. Since cloudlet scheduling is crucial metrics of cloud computing that has to be heightened by combining the different parameters. This paper tried to provide effectiveness and improvement in task scheduling using nature inspired Particle Swarm optimization (PSO) strategy. A powerful nature inspired load balancing mechanism is proposed in this paper which optimized makespan and throughput in environment of varying cloudlets and virtual machines results as compared to other conventional approaches. Proposed (EPSO) algorithm is with four scheduling policies namely FCFS, Round Robin (RR) and Shortest Job First (SJF) and get near twice good throughput percentage and minimized makespan in two different environments. Author used Cloud sim toolkit and some Open Source cloud packages to simulate the results of various scheduling components. Experimental results of various components are tested and simulated on java based CloudSim toolkit framework.

scholarly journals Novel Approach to Task Scheduling and Load Balancing Using the Dominant Sequence Clustering and Mean Shift Clustering Algorithms

2019 ◽  
Vol 11 (5) ◽  
pp. 109 ◽  
Author(s):  
Amer Al-Rahayfeh ◽  
Saleh Atiewi ◽  
Abdullah Abuhussein ◽  
Muder Almiani
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Mean Shift ◽  
Kernel Functions ◽  
Service Reliability ◽  
Sequence Clustering ◽  
Mean Shift Clustering ◽  
Novel Approach

Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.

scholarly journals An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 200
Author(s):  
Suleiman Sa’ad ◽  
Abdullah Muhammed ◽  
Mohammed Abdullahi ◽  
Azizol Abdullah ◽  
Fahrul Hakim Ayob
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Task Scheduling ◽  
Large Scale ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Search Space ◽  
Cloud Environment ◽  
Nature Inspired Algorithm ◽  
Symbiotic Organism Search

Recently, cloud computing has begun to experience tremendous growth because government agencies and private organisations are migrating to the cloud environment. Hence, having a task scheduling strategy that is efficient is paramount for effectively improving the prospects of cloud computing. Typically, a certain number of tasks are scheduled to use diverse resources (virtual machines) to minimise the makespan and achieve the optimum utilisation of the system by reducing the response time within the cloud environment. The task scheduling problem is NP-complete; as such, obtaining a precise solution is difficult, particularly for large-scale tasks. Therefore, in this paper, we propose a metaheuristic enhanced discrete symbiotic organism search (eDSOS) algorithm for optimal task scheduling in the cloud computing setting. Our proposed algorithm is an extension of the standard symbiotic organism search (SOS), a nature-inspired algorithm that has been implemented to solve various numerical optimisation problems. This algorithm imitates the symbiotic associations (mutualism, commensalism, and parasitism stages) displayed by organisms in an ecosystem. Despite the improvements made with the discrete symbiotic organism search (DSOS) algorithm, it still becomes trapped in local optima due to the large size of the values of the makespan and response time. The local search space of the DSOS is diversified by substituting the best value with any candidate in the population at the mutualism phase of the DSOS algorithm, which makes it worthy for use in task scheduling problems in the cloud. Thus, the eDSOS strategy converges faster when the search space is larger or more prominent due to diversification. The CloudSim simulator was used to conduct the experiment, and the simulation results show that the proposed eDSOS was able to produce a solution with a good quality when compared with that of the DSOS. Lastly, we analysed the proposed strategy by using a two-sample t-test, which revealed that the performance of eDSOS was of significance compared to the benchmark strategy (DSOS), particularly for large search spaces. The percentage improvements were 26.23% for the makespan and 63.34% for the response time.

An Efficient Kernel FCM and Artificial Fish Swarm Optimization-Based Optimal Resource Allocation in Cloud

2020 ◽  
Vol 29 (16) ◽  
pp. 2050253
Author(s):  
Pravin Albert ◽  
Manikandan Nanjappan
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Optimal Resource Allocation ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Artificial Fish Swarm ◽  
Optimal Resource ◽  
And Task

Cloud computing model allows service-oriented system that fulfills the needs of the consumers. Capable resource management and task allocation are the important issues in cloud computing. Performance of the task scheduling method directly interrupts the utilization of cloud computing resources and the quality of experience of users. For that reason, reasonable virtual machine (VM) allocation and task scheduling are extremely important. In this paper, an efficient resource allocation model is proposed. Initially, the virtual machines are clustered with the help of Kernel Fuzzy [Formula: see text]-Means Clustering (KFCM) algorithm to reduce the complexity. After the clustering process, the user tasks are allocated to the particular VM using artificial fish swarm optimization (AFSO) algorithm. A multi-objective function is designed to achieve an optimal resource allocation. The performance of the suggested technique is tested in terms of different metrics.

scholarly journals Efficient Task Scheduling and Fair Load Distribution Among Federated Clouds

2021 ◽  
Vol 15 (3) ◽  
pp. 216-238
Author(s):  
Rajeshwari B S ◽  
M. Dakshayini ◽  
H.S. Guruprasad
Keyword(s):  
Response Time ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Future Generation ◽  
Service Level ◽  
Centralized Control ◽  
Cloud Providers ◽  
Fair Distribution ◽  
User Tasks

The federated cloud is the future generation of cloud computing, allowing sharing of computing and storage resources, and servicing of user tasks among cloud providers through a centralized control mechanism. However, a great challenge lies in the efficient management of such federated clouds and fair distribution of the load among heterogeneous cloud providers. In our proposed approach, called QPFS_MASG, at the federated cloud level, the incoming tasks queue are partitioned in order to achieve a fair distribution of the load among all cloud providers of the federated cloud. Then, at the cloud level, task scheduling using the Modified Activity Selection by Greedy (MASG) technique assigns the tasks to different virtual machines (VMs), considering the task deadline as the key factor in achieving good quality of service (QoS). The proposed approach takes care of servicing tasks within their deadline, reducing service level agreement (SLA) violations, improving the response time of user tasks as well as achieving fair distribution of the load among all participating cloud providers. The QPFS_MASG was implemented using CloudSim and the evaluation result revealed a guaranteed degree of fairness in service distribution among the cloud providers with reduced response time and SLA violations compared to existing approaches. Also, the evaluation results showed that the proposed approach serviced the user tasks with minimum number of VMs.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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