An Efficient Load Balancing Method by Using Machine Learning-Based VM Distribution and Dynamic Resource Mapping

2020 ◽  
Vol 17 (6) ◽  
pp. 2545-2551
Author(s):  
Umesh Kumar Lilhore ◽  
Sarita Simaiya ◽  
Kalpna Guleria ◽  
Devendra Prasad
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Research Area ◽  
Clustering Method ◽  
Major Research ◽  
Svm Classification ◽  
Ready Time ◽  
Dynamic Resource ◽  
Rejection Time

In cloud computing, balancing the load among VMs and resources is a major research area, which still needs attention. The primary aim of this research is to expand an effective cloud load balancing approach, enhance the reaction time, lessen the ready time, premiere utilization of sources as well lessen the activity rejection time. The proposed MLBL method is primarily based on the SVM and Ksuggest clustering method. In the proposed MLBL technique SVM classification method used to create activity businesses based totally at the size. Later Kmethod clustering technique is used to create the institution of Virtual machines based totally on their usage of CPU and number one memory (RAM). We are providing an MLBL method based totally on system learning-based VM distribution and dynamic aid mapping. In cloud computing, VMs are scheduled to hosts as in keeping with their utilization (a host which has better availability of memory) without thinking about common utilizations. The proposed technique divides the assets into various organizations as well as VMs and then follow dynamic aid mapping. The proposed MLBL technique creates VMs clusters to execute comparable task agencies that enhance the QoS and ideal utilization of sources in addition to lessen the process rejection time.

scholarly journals An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 2490-2494
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Cloud Services ◽  
Research Trend ◽  
Schedule Length ◽  
Research Gap ◽  
Load Balancing Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.

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 ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

2017 ◽  
Vol 10 (13) ◽  
pp. 162
Author(s):  
Amey Rivankar ◽  
Anusooya G
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Energy Efficient ◽  
Large Scale ◽  
Virtual Machines ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

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.

Receiver Initiated Deadline Aware Load Balancing Strategy (RDLBS) for Cloud Environment

2017 ◽  
Vol 8 (3) ◽  
pp. 53-73
Author(s):  
Raza Abbas Haidri ◽  
Chittaranjan Padmanabh Katti ◽  
Prem Chandra Saxena
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Load Balancing ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Environment ◽  
Computing Technology ◽  
Trade Off ◽  
The Cost ◽  
State Of Art

The emerging cloud computing technology is the attention of both commercial and academic spheres. Generally, the cost of the faster resource is more than the slower ones, therefore, there is a trade-off between deadline and cost. In this paper, the authors propose a receiver initiated deadline aware load balancing strategy (RDLBS) which tries to meet the deadline of the requests and optimizes the rate of revenue. RDLBS balances the load among the virtual machines (VMs) by migrating the request from the overloaded VMs to underloaded VMs. Turnaround time is also computed for the performance evaluation. The experiments are conducted by using CloudSim simulator and results are compared with existing state of art algorithms with similar objectives.

Cloud Load Balancing Algorithms: A Comparative Assessment

2019 ◽  
Vol 16 (9) ◽  
pp. 3989-3994
Author(s):  
Jaspreet Singh ◽  
Deepali Gupta ◽  
Neha Sharma
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Data Centers ◽  
Research Area ◽  
Comparative Assessment ◽  
Cloud Services ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Cloud Users

Nowadays, Cloud computing is developing quickly and customers are requesting more administrations and superior outcomes. In the cloud domain, load balancing has turned into an extremely intriguing and crucial research area. Numbers of algorithms were recommended to give proficient mechanism for distributing the cloud user’s requests for accessing pool cloud resources. Also load balancing in cloud should provide notable functional benefits to cloud users and at the same time should prove out to be eminent for cloud services providers. In this paper, the pre-existing load balancing techniques are explored. The paper intends to provide landscape for classification of distinct load balancing algorithms based upon the several parameters and also address performance assessment bound to various load balancing algorithms. The comparative assessment of various load balancing algorithms will helps in proposing a competent load balancing technique for intensify the performance of cloud data centers.

Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

2020 ◽  
Vol 17 (6) ◽  
pp. 2430-2434
Author(s):  
R. S. Rajput ◽  
Dinesh Goyal ◽  
Rashid Hussain ◽  
Pratham Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Auto Scaling ◽  
Adequate Resource ◽  
Cloud Resources

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

Sign in / Sign up

Export Citation Format

Share Document