scholarly journals An Efficient Adaptive Load Balancing Algorithm for Cloud Computing Under Bursty Workloads

2015 ◽  
Vol 5 (3) ◽  
pp. 795-800 ◽  
Author(s):  
S. F. Issawi ◽  
A. Al Halees ◽  
M. Radi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Quality Of Services ◽  
Average Response ◽  
Average Response Time ◽  
It Industry ◽  
Request Rate ◽  
Wide Range ◽  
Load Balancing Algorithm

Cloud computing is a recent, emerging technology in the IT industry. It is an evolution of previous models such as grid computing. It enables a wide range of users to access a large sharing pool of resources over the internet. In such complex system, there is a tremendous need for an efficient load balancing scheme in order to satisfy peak user demands and provide high quality of services. One of the challenging problems that degrade the performance of a load balancing process is bursty workloads. Although there are a lot of researches proposing different load balancing algorithms, most of them neglect the problem of bursty workloads. Motivated by this problem, this paper proposes a new burstness-aware load balancing algorithm which can adapt to the variation in the request rate by adopting two load balancing algorithms: RR in burst and Random in non-burst state. Fuzzy logic is used in order to assign the received request to a balanced VM. The algorithm has been evaluated and compared with other algorithms using Cloud Analyst simulator.  Results show that the proposed algorithm improves the average response time and average processing time in comparison with other algorithms.

scholarly journals Green Metascheduler Architecture to Provide QoS in Cloud Computing

2015 ◽  
Author(s):  
Osvaldo Adilson De Carvalho Junior ◽  
Sarita Mazzini Bruschi ◽  
Regina Helena Carlucci Santana ◽  
Marcos José Santana
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Energy Consumption ◽  
Response Time ◽  
Large Scale ◽  
Quality Of Services ◽  
Average Response ◽  
Average Response Time ◽  
Private Cloud

The aim of this paper is to propose and evaluate GreenMACC (Green Metascheduler Architecture to Provide QoS in Cloud Computing), an extension of the MACC architecture (Metascheduler Architecture to provide QoS in Cloud Computing) which uses greenIT techniques to provide Quality of Service. The paper provides an evaluation of the performance of the policies in the four stages of scheduling focused on energy consumption and average response time. The results presented confirm the consistency of the proposal as it controls energy consumption and the quality of services requested by different users of a large-scale private cloud.

Analysis of Different Load Balancing Algorithms in Cloud Computing

2021 ◽  
Vol 11 (4) ◽  
pp. 100-112
Author(s):  
Poonam Nandal ◽  
Deepa Bura ◽  
Meeta Singh ◽  
Sudeep Kumar
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Exponential Growth ◽  
Average Response ◽  
Average Response Time ◽  
It Industry ◽  
Time Processing ◽  
Processing Cost ◽  
Day By Day

In today's world, the IT industry is emerging day by day; therefore, the need for storage and computing is increasing multifold. Cloud computing has transformed the IT sector to much greater heights by virtualizing the systems, thereby reducing cost of the hardware to greater extent. Cloud computing is based on the pay as per use policy. Due to the exponential growth in cloud computing, users demand supplementary services and improved results which makes load balancing a major challenge. Load balancing distributes the workload across multiple nodes to optimize the performance of the system. Various load balancing algorithms exist to provide better resource utilization. This paper gives a brief analysis of load balancing algorithms and also compared these algorithms on the basis of certain metrics like average response time, processing cost, and data servicing time.

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.

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.

Quality of Services and Optimal Management of Cloud Centers with Different Arrival Modes

2014 ◽  
pp. 185-198
Author(s):  
V. Goswami ◽  
S. S. Patra ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Service Level Agreement ◽  
Service Level ◽  
Quality Of Services ◽  
Finite Buffer ◽  
Optimal Management ◽  
It Industry ◽  
Computing Paradigm

In Cloud Computing, the virtualization of IT infrastructure enables consolidation and pooling of IT resources so they are shared over diverse applications to offset the limitation of shrinking resources and growing business needs. Cloud Computing is a way to increase the capacity or add capabilities dynamically without investing in new infrastructure, training new personnel, or licensing new software. It extends Information Technology's existing capabilities. In the last few years, cloud computing has grown from being a promising business concept to one of the fast growing segments of the IT industry. For the commercial success of this new computing paradigm, the ability to deliver guaranteed Quality of Services is crucial. Based on the Service Level Agreement, the requests are processed in the cloud centers in different modes. This chapter deals with Quality of Services and optimal management of cloud centers with different arrival modes. For this purpose, the authors consider a finite-buffer multi-server queuing system where client requests have different arrival modes. It is assumed that each arrival mode is serviced by one or more virtual machines, and different modes have equal probabilities of receiving services. Various performance measures are obtained and optimal cost policy is presented with numerical results. A genetic algorithm is employed to search optimal values of various parameters for the system.

scholarly journals Hybrid Load Balancing Approach based on the Integration of QoS and Power Consumption in Cloud Computing

2021 ◽  
Vol 10 (2) ◽  
pp. 1079-1090
Keyword(s):  
Load Balancing ◽  
Power Consumption ◽  
Task Scheduling ◽  
Hybrid Algorithm ◽  
Virtual Machines ◽  
Average Response ◽  
Average Response Time ◽  
The Past ◽  
Load Balancing Algorithm ◽  
And Task

Over the past few years, there has been keen research interest in load balancing and task scheduling in the cloud as the extensive amount of data that is stored in the server leads to significantly increased load. This can be resolved by using a hybrid algorithm in which the honeybee behavior algorithm’s advantages are integrated with fuzzy logic to conduct task scheduling and as well as balancing in the cloud. The design of this hybrid algorithm aims to enhance prior approaches. It is developed as per ABC and merges the important QoS factors along with power consumption so that the power that virtual machines (VMs) consume on the host can be precisely assessed, thereby ensuring efficient load balancing algorithm. The present study aims to evaluate the VMs’ power consumption by taking into account crucial QoS factors for selecting which host and virtual machine will be best suited for receiving the task. CloudSim was used to simulate the ILBA_HB algorithm. In terms of makespan, average response time, and degree of imbalance, the performance of the ILBA HB algorithm is compared to that of the LBA HB and HBB-LB algorithms. According to the results, the proposed algorithm outperformed LBA_HB and HBB-LB.

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