Effective resource utilization in cloud environment through a dynamic well-organized load balancing algorithm for virtual machines

2017 ◽  
Vol 57 ◽  
pp. 199-208 ◽  
Author(s):  
M. Vanitha ◽  
P. Marikkannu
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Load Balancing Algorithm

A Two-Phase Load Balancing Algorithm for Cloud Environment

2021 ◽  
Vol 13 (1) ◽  
pp. 38-55
Author(s):  
Archana Singh ◽  
Rakesh Kumar
Keyword(s):  
Load Balancing ◽  
Data Center ◽  
Virtual Machines ◽  
Second Phase ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Efficient Utilization ◽  
Two Phase ◽  
Excessive Load ◽  
Load Balancing Algorithm

Load balancing is the phenomenon of distributing workload over various computing resources efficiently. It offers enterprises to efficiently manage different application or workload demands by allocating available resources among different servers, computers, and networks. These services can be accessed and utilized either for home use or for business purposes. Due to the excessive load on the cloud, sometimes it is not feasible to offer all these services to different users efficiently. To solve this excessive load issue, an efficient load balancing technique is used to offer satisfactory services to users as per their expectations also leading to efficient utilization of resources and applications on the cloud platform. This paper presents an enhanced load balancing algorithm named as a two-phase load balancing algorithm. It uses a two-phase checking load balancing approach where the first phase is to divide all virtual machines into two different tables based on their state, that is, available or busy while in the second phase, it equally distributes the loads. The various parameters used to measure the performance of the proposed algorithm are cost, data center processing time, and response time. Cloud analyst simulation tool is used to simulate the algorithm. Simulation results demonstrate superiority of the algorithm with existing ones.

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

2020 ◽  
Vol 11 (1) ◽  
pp. 12-36
Author(s):  
Minakshi Sharma ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Qos Parameters ◽  
Load Balancing Algorithm

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

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.

MPSO

2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Subasish Mohapatra ◽  
Mitrabinda Ray
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Cloud Environment ◽  
Swarm Optimization ◽  
Load Balancing Algorithm

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better Quality of Service (QOS). It is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a load balancing algorithm using Multi Particle Swarm Optimization (MPSO) has been developed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization in a homogenous cloud environment. Performance comparisons are made with Genetic Algorithm (GA), Multi GA, PSO and other popular algorithms on different measures like makespan calculation and resource utilization.

scholarly journals Load Balancing Mechanisms in Amazon Web Services using Meta Heuristic Rules

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4071-4075
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Resource Utilization ◽  
Waiting Time ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
System Response ◽  
Load Balancing Algorithm

Cloud computing is defined as the resource that can be delivered or accessed by the local host from the remote server via the internet. Cloud providers typically use a "pay-as-you-go" model. The evolution of cloud computing has led to the evolution of modern environment due to abundance and advancement of computing and communication infrastructure. During user request, and system response generation, an amount load will be assigned in the cloud computing, where it may be over or under load. Due to heavy load, power consumption and energy management problems are created, and it makes system failure and lead data loss. Though, an efficient load balancing method is compulsory to overcome all mentioned problems. The objective of this work is to develop a metaheuristic load balancing algorithm to migrate multi-server for load balancing and machine learning techniques is used to increase the cloud resource utilization and minimize the make-span time of the task. Using an unsupervised machine learning technique, it is possible to predict the correct response time and waiting time of the servers by getting the prior knowledge about the virtual machines and its clusters. And this work involves to calculate the accuracy rate of the Round-Robin load balancing algorithm and then compared it with a proposed algorithm. By this work, the response time and waiting time can be minimized and also it increases the resource utilization and minimize the make- span time of the task.

scholarly journals A NOVEL APPROACH OF OPTIMIZING PERFORMANCE USING K-MEANS CLUSTERING IN CLOUD COMPUTING

2016 ◽  
Vol 15 (14) ◽  
pp. 7435-7443 ◽  
Author(s):  
Sheenam Kamboj ◽  
Mr. Navtej Singh Ghumman
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Environment ◽  
Single Node ◽  
Novel Approach ◽  
Load Balancing Algorithm

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results. 

scholarly journals A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT

2018 ◽  
Vol 17 (1) ◽  
pp. 7103-7110
Author(s):  
Ashima Ashima ◽  
Vikramjit Singh
Keyword(s):  
Load Balancing ◽  
Virtual Machines ◽  
Complete Solution ◽  
Research Work ◽  
Cloud Service ◽  
Threshold Value ◽  
Work Load ◽  
Cloud Environment ◽  
Multiple Parameters ◽  
Load Balancing Algorithm

Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. We propose an improved load balancing algorithm for job scheduling in the Grid environment.  Hence, in this research work, a multi-objective load balancing algorithm has been proposed to avoid deadlocks and to provide proper utilization of all the virtual machines (VMs) while processing the requests received from the users by VM classification. The capacity of virtual machine is computed based on multiple parameters like MIPS, RAM and bandwidth. Heterogeneous virtual machines of different MIPS and processing power in multiple data centers with different hosts have been created in cloud simulator. The VM’s are divided into 2 clusters using K-Means clustering mechanism in terms of processor MIPS, memory and bandwidth. The cloudlets are divided into two categories like High QOS and Low QOS based on the instruction size. The cloudlet whose task size is greater than the threshold value will enter into High QOS and cloudlet whose task size is lesser than the threshold value will enter into Low QOS. Submit the job of the user to the datacenter broker. The job of the user is submitted to the broker and it will first find the suitable VM according to the requirements of the cloudlet and will match VM depending upon its availability. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. This modified algorithm has an edge over the original approach in which each cloudlet build their own individual result set and it is later on built into a complete solution.

scholarly journals AN ENHANCED TASK ALLOCATION STRATEGY IN CLOUD ENVIRONMENT

2017 ◽  
Vol 16 (6) ◽  
pp. 6953-6961
Author(s):  
Kavita Redishettywar ◽  
Prof. Rafik Juber Thekiya
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Data Centers ◽  
Job Scheduling ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Load Balancing Algorithm

Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Interne technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon client’s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

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.

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.

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