Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

Spider Monkey Optimization Algorithm for Load Balancing in Cloud Computing Environments

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Sawsan Alshattnawi ◽  
Mohammad AL-Marie
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Round Robin ◽  
Spider Monkey ◽  
Spider Monkeys ◽  
Cloud Computing Environment ◽  
Spider Monkey Optimization

Scheduling of tasks is one of the main concerns in the Cloud Computing environment. The whole system performance depends on the used scheduling algorithm. The scheduling objective is to distribute tasks between the Virtual Machines and balance the load to prevent any virtual machine from being overloaded while other is underloaded. The problem of scheduling is considered an NP-hard optimization problem. Therefore, many heuristics have been proposed to solve this problem up to now. In this paper, we propose a new Spider Monkeys algorithm for load balancing called Spider Monkey Optimization Inspired Load Balancing (SMO-LB) based on mimicking the foraging behavior of Spider Monkeys. It aims to balance the load among virtual machines to increase the performance by reducing makespan and response time. Experimental results show that our proposed method reduces tasks' average response time to 10.7 seconds compared to 24.6 and 30.8 seconds for Round Robin and Throttled methods respectively. Also, the makespan was reduced to 21.5 seconds compared to 35.5 and 53.0 seconds for Round Robin and Throttled methods respectively.

Cloud Computing in Three Phase Scheduling Algorithm Based on Service Quality

2014 ◽  
Vol 687-691 ◽  
pp. 2862-2866
Author(s):  
Yong Long Zhuang ◽  
Xiao Lan Weng ◽  
Xiang He Wei
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Service Quality ◽  
Network Topology ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Hierarchical Network ◽  
Three Phase ◽  
Simulation Results ◽  
Minimum Completion Time

Assurance issues for cloud computing’s service quality; propose a hierarchical network topology on the basis of the three-stage scheduling algorithm. The algorithm ensures that each work to be performed can be assigned to the appropriate resources, and can effectively improve the load of each node and reduce the waste of resources. FCFS, SJF, OLB and Min-Min (MM) scheduling algorithm’s simulation results show that the algorithm can not only make the work get the minimum completion time, but also the service node operation achieve the load balancing.

Virtual Machines Scheduling Algorithm Based on Multi-Objective Optimization in Cloud Computing

2014 ◽  
Vol 1046 ◽  
pp. 508-511
Author(s):  
Jian Rong Zhu ◽  
Yi Zhuang ◽  
Jing Li ◽  
Wei Zhu
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Machine Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Virtual Machine Scheduling ◽  
Set Up

How to reduce energy consumption while improving utility of datacenter is one of the key technologies in the cloud computing environment. In this paper, we use energy consumption and utility of data center as objective functions to set up a virtual machine scheduling model based on multi-objective optimization VMSA-MOP, and design a virtual machine scheduling algorithm based on NSGA-2 to solve the model. Experimental results show that compared with other virtual machine scheduling algorithms, our algorithm can obtain relatively optimal scheduling results.

An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

2020 ◽  
Vol 16 (3) ◽  
pp. 259-277
Author(s):  
Pooja Arora ◽  
Anurag Dixit
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
System Performance ◽  
Virtual Machines ◽  
Fitness Function ◽  
Optimization Technique ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Content Type ◽  
Computing Model

Purpose The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms. Design/methodology/approach This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions. Findings The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique. Originality/value This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

scholarly journals Load Balancing Technique with an Efficient Method

2019 ◽  
Vol 8 (6) ◽  
pp. 1257-1262
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Efficient Method ◽  
Load Balance ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Quality Of Services ◽  
Huge Number ◽  
Task Scheduling Algorithm

There are a huge number of nodes connected to web computing to offer various types of web services to provide cloud clients. Limited numbers of nodes connected to cloud computing have to execute more than a thousand or a million tasks at the same time. So it is not so simple to execute all tasks at the same particular time. Some nodes execute all tasks, so there is a need to balance all the tasks or loads at a time. Load balance minimizes the completion time and executes all the tasks in a particular way.There is no possibility to keep an equal number of servers in cloud computing to execute an equal number of tasks. Tasks that are to be performed in cloud computing would be more than the connected servers. Limited servers have to perform a great number of tasks.We propose a task scheduling algorithm where few nodes perform the jobs, where jobs are more than the nodes and balance all loads to the available nodes to make the best use of the quality of services with load balancing.

scholarly journals Proposing a load balancing algorithm with an integrative approach to reduce response time and service process time in data centers

2019 ◽  
Vol 16 (4) ◽  
pp. 627-637
Author(s):  
Sanaz Hosseinzadeh Sabeti ◽  
Maryam Mollabgher
Keyword(s):  
Load Balancing ◽  
Data Processing ◽  
Response Time ◽  
Virtual Machine ◽  
Data Center ◽  
Processing Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Overall Response ◽  
Load Balancing Algorithm

Goal: Load balancing policies often map workloads on virtual machines, and are being sought to achieve their goals by creating an almost equal level of workload on any virtual machine. In this research, a hybrid load balancing algorithm is proposed with the aim of reducing response time and processing time. Design / Methodology / Approach: The proposed algorithm performs load balancing using a table including the status indicators of virtual machines and the task list allocated to each virtual machine. The evaluation results of response time and processing time in data centers from four algorithms, ESCE, Throttled, Round Robin and the proposed algorithm is done. Results: The overall response time and data processing time in the proposed algorithm data center are shorter than other algorithms and improve the response time and data processing time in the data center. The results of the overall response time for all algorithms show that the response time of the proposed algorithm is 12.28%, compared to the Round Robin algorithm, 9.1% compared to the Throttled algorithm, and 4.86% of the ESCE algorithm. Limitations of the investigation: Due to time and technical limitations, load balancing has not been achieved with more goals, such as lowering costs and increasing productivity. Practical implications: The implementation of a hybrid load factor policy can improve the response time and processing time. The use of load balancing will cause the traffic load between virtual machines to be properly distributed and prevent bottlenecks. This will be effective in increasing customer responsiveness. And finally, improving response time increases the satisfaction of cloud users and increases the productivity of computing resources. Originality/Value: This research can be effective in optimizing the existing algorithms and will take a step towards further research in this regard.

scholarly journals Resource Allocation in Cloud Computing using Heuristic Load Balancing Algorithm

2020 ◽  
Author(s):  
Anup Shrestha ◽  
Suriayati Chuprat ◽  
Nandini Mukherjee
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Completion Time ◽  
Virtual Machines ◽  
It Services ◽  
Computing Environment ◽  
Load Balancing Algorithm ◽  
Better Than

Cloud computing is becoming more popular, unlike conventional computing, due to its added advantages. This is because it offers utility-based services to its subscribers upon their demand. Furthermore, this computing environment provides IT services to its users where they pay for every use. However, the increasing number of tasks requires virtual machines for them to be accomplished quickly. Load balancing a critical concern in cloud computing due to the massive increase in users' numbers. This paper proposes the best heuristic load balancing algorithm that will schedule a strategy for resource allocation that will minimize make span (completion time) in any technology that involves use cloud computing. The proposed algorithm performs better than other load balancing algorithms.

scholarly journals LOAD BALANCING IN CLOUD ENVIRONMENT: A REVIEW

2016 ◽  
Vol 15 (8) ◽  
pp. 6986-6990
Author(s):  
Sheenam Kamboj ◽  
Mr. Navtej Singh Ghumman
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Computing Platform ◽  
Side Load ◽  
Virtualization Technology ◽  
Cloud Computing Platform ◽  
Demand Changes

An essential role of cloud computing platform is to dynamically balance the load among the different servers in order to improve resource utilization and to avoid hotspots. Load balancing (LB) is done on both sides i.e. on provider as well as on consumer side. On provider side, load balancing is the problem of allocating virtual machines to servers at runtime. Virtual Machine need to be reassigned so that servers do not get overloaded as demand changes. On consumer side application load can be balanced which provides efficiency to the consumers. On cloud computing platform, load balancing of the entire system can be dynamically handled by  using virtualization technology through which itÂbecomes possible to remap virtual machine and physical resources according to  the change in load. However, in order to improve performance, the virtual machines have to fully utilize its resources and services by adapting to computing environment dynamically. The load balancing with proper allocation of resources must be guaranteed in order to improve resource utility.

scholarly journals Load Balancing of Unbalanced Matrix Problem of Maximum Machines with Min Min Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 627-629
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Execution Time ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Elastic Behavior ◽  
Computing Technology ◽  
Matrix Problem ◽  
Web Based ◽  
Stay Behind

Most eminent computing technology related to the cloud is an exclusively web-based approach where resources are hosted on a cloud; it prospers the resources. Cloud computing is the most promising technologies that offer a standard for bulky sized computing. That is a structure for enabling applications to execution on virtualized resources and accessed by a network protocol. It provides resource and services in a very elastic behavior that can be scaled according to the required of the clients. Limited numbers of devices execute less number of tasks at that time. So it is more complex to perform each task at once. Several devices execute each task, so it has required balancing total loads that reduce the completion time and executes each task in a definite way. We have said earlier that there are not feasible to stay behind an equal server to execute similar tasks. The tasks that are to be executed by machine in the cloud system must be less than the united VM for a time. Overloaded servers have to perform a less number of jobs. Here in our approach, we want to show a scheduling algorithm for balancing of loads and presentation with minimum execution time and makespan.

scholarly journals Resource-Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 1863-1870 ◽  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Cloud Resource ◽  
Tasks Scheduling ◽  
Minimum Completion Time ◽  
Resource Aware ◽  
Time Resource

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms.

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