scholarly journals Optimization Load Balancing Over an Imbalance Datacenter Topologies

2019 ◽  
Vol 8 (3) ◽  
pp. 5676-5680
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Efficient Algorithm ◽  
Virtual Machines ◽  
End User ◽  
Huge Number ◽  
Number Of Clusters ◽  
Main Challenge ◽  
Cpu Utilization ◽  
Load Balancing Algorithm

The quick demand of cloud resources, responsible for design a highly dynamic and flexible Cloud, has become a main challenge in datacenter deployment.A huge number of virtual machines will be available in Datacenter. Further Datacenter will be divided into a greater number of clusters. Each cluster is grouped to same type of Virtual machines. The virtual machines inside the cluster is homogeneous and heterogeneous to other cluster. Any virtual machine can be allocated to end user. If an unhealthy and less energy virtual machine is allocated to user, it will completely degrade the performance of the machine. To overcome this issue, we use an efficient load-balancing algorithm to allocate virtual machine to end user. The Fuzzy Optimized load-balancing algorithm uses the bandwidth, memory, CPU utilization are the key metrics. An efficient algorithm increases the number of hosts allocated to each end user

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 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 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.

scholarly journals Cloud Computing based on the Load Balancing Algorithm

2018 ◽  
Vol 7 (4.7) ◽  
pp. 131
Author(s):  
NV Abhinav Chand ◽  
A Hemanth Kumar ◽  
Surya Teja Marella
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
User Satisfaction ◽  
Easy Access ◽  
Future Research ◽  
Computing Technology ◽  
Main Challenge ◽  
Load Balancing Algorithm ◽  
And Performance ◽  
Time Resource

Emerging cloud computing technology is a big step in virtual computing. Cloud computing provides services to clients through the internet. Cloud computing enables easy access to resources distributed all over the world. Increase in the number of the population has further increased the challenge. The main challenge of cloud computing technology is to achieve efficient load balancing. Load balancing is a process of assigning load to available resources in such a way that it avoids overloading of resources. If load balancing is performed efficiently, it improves QoS metric including cost, throughput, response time, resource utilization and performance. Efficient load balancing techniques also provide better user satisfaction. Various load balancing algorithms are used in different scenarios for ensuring the same. In the current research, we will study different algorithms for load balancing and benefits and limitations caused to the system due to the algorithms. In this paper, we will compare static and dynamic load balancing algorithms for various measures of efficiency. These will be useful for future research in the concerned field. 

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.

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.

scholarly journals Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

2020 ◽  
Vol 10 (7) ◽  
pp. 2323
Author(s):  
T. Renugadevi ◽  
K. Geetha ◽  
K. Muthukumar ◽  
Zong Woo Geem
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Power Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Resource Provisioning ◽  
Total Power

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

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.

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