A Literature Review of QoS with Load Balancing in Cloud Computing Environment

Geeta, .; Prakash, Shiva

doi:10.1007/978-981-10-6620-7_64

Cited by 16 publications

(8 citation statements)

References 17 publications

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“…The key benefit of all these strategies is that they allow the tasks to transfer from an overloaded machine to an under loaded one [9]. Dynamic load balancing resulting in tolerance to faults, higher scalability, and low overhead may be used to increase CC efficiency [82]. This was used by the dynamic load balancing approach to handling unpredictable processor loads [87].…”

Section: Lb Approachesmentioning

confidence: 99%

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

et al. 2020

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The past few years have witnessed the emergence of a novel paradigm called cloud computing. CC aims to provide computation and resources over the internet via dynamic provisioning of services. There are several challenges and issues associated with implementation of CC. This research paper deliberates on one of CC main problems i.e. load balancing (LB). The goal of LB is equilibrating the computation on the cloud servers such that no host is under/ overloaded. Several LB algorithms have been implemented in literature to provide effective administration and satisfying customer requests for appropriate cloud nodes, to improve the overall efficiency of cloud services, and to provide the end user with more satisfaction. An efficient LB algorithm improves efficiency and asset's usage through effectively spreading the workload across the system's different nodes. This review research paper objective is to present critical study of existing techniques of LB, to discuss various LB parameters i.e. throughput, performance, migration time, response time, overhead, resource usage, scalability, fault tolerance, power savings, etc. The research paper also discusses the problems of LB in the CC environment and identifies the need for a novel LB algorithm that employs FT metrics. It has been found that traditional LB algorithms are not good enough and they do not consider FT efficiency metrics for their operation. Hence, the research paper identifies the need for FT efficiency metric in LB algorithms which is one of the main concerns in cloud environments. A novel algorithm that employs FT in LB is therefore proposed.

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Section: Lb Approachesmentioning

confidence: 99%

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

et al. 2020

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“…Geeta et al [13] had discussed various QoS and Load balancing Techniques and also had addressed key performance challenges and different modeling with their applications for QoS management and simulation toolkits in cloud computing.…”

Section: Related Studymentioning

confidence: 99%

“…In today's information technology driven world, cloud computing and big data adoption by businesses decide their competitive ranking. Especially, every business is expected to provide better QoS [13] than their competitors through maximum throughput with minimum response time. In a cloud environment, when a client gives a service request to the service providers, the distributed server farm on the CSP side, must ensure provisioning of appropriate services.The cloud consumers and cloud service providers are generally connected through communication backbone network.…”

Section: Introductionmentioning

confidence: 99%

“…The benefits of the cloud can be leveraged with the virtualization technology. Load balancing [13] in cloud computing indicates the process of distributing workloads and computing resources across distributed servers. Load balancing in any system is generally maintains system firmness, improves system performance and protects against system failures.…”

Section: Introductionmentioning

confidence: 99%

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Qos and Load Balancing in Cloud Computing- an Access for Performance Enhancement using Agent Based Software

2019

IJITEE

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Cloud computing, one of the advanced and emerged technologies in the field of computer science has been embraced by different organizations of various sizes. The purpose of organizations moving into cloud is manifold, out of which , performance enhancement and cost optimizer are the primary ones. Generally, when an organization moves their operations into cloud, Cloud Service Providers (CSPs) provision various machine images to different users based on their requirements within the organization. Also, CSPs, potentially offer Anything as a Service (XaaS) to organizations with the help of distributed and connected server farms available at geographically separate locations. QoS parameters in terms of service time, as specified in the Service Level Agreements(SLAs) between CSPs and organizations must be adhered strictly. As an effort towards maintaining QoS, within the cloud, the operational approach of load balancing across multiple distributed servers play a vital role. This paper presents a novel load balancing algorithmic framework with the help of software agent that runs in the gateway system between cloud consumers and cloud service providers. This software agent in the gateway system is vested with the responsibility of diverting the incoming work process to the appropriate servers, based on their current workload and resource utilization. The efficiency of this approach is tested using CLOUDSIM by creating different number of cloudlets and hosts

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“…The testing and experimental results are quite good comparing to others well-known algorithms like Throttle, Round Robin and MaxMin. But the testing data broad is not so wide, we can open more cases and more request to test.In 2018, Geeta et had focused more on QoS of LB in cloud[18], the paper reviewed all existing algorithms of LB, otherwise proposed the important QoSs for LB in cloud. The cloud QoS has some characteristics: Self Service on demand, Location independence, Broad network access, Rapid elasticity, Measuring service.…”

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confidence: 99%

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2020

IJCCSA

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Nowadays, the demand of using resources, using services via the intranet system or on the Internet is rapidly growing. The respective problem coming is how to use these resources effectively in terms of time and quality. Therefore, the network QoS and its economy are people concerns, cloud computing was born in an inevitable trend. However, managing resources and scheduling tasks in virtualized data centres on the cloud are challenging tasks. Currently, there are a lot of Load Balancing algorithms applied in clouds and proposed by many authors, scholars, and experts. These existing methods are more about natural and heuristic, but the application of AI, or modern datamining technologies, in load balancing is not too popular due to the different characteristics of cloud. In this paper, we propose an algorithm to reduce the processing time (makespan) on cloud computing, helping the load balancing work more efficiency. Here, we use the SVM algorithm to classify the coming Requests, K-Mean to cluster the VMs in cloud, then the LB will allocate the requests into the VMs in the most reasonable way. In this way, request with the least processing time will be allocated to the VMs with the lowest usage. We name this new proposal as MCCVA-Makespan Classification & Clustering VM Algorithm. We have experimented and evaluated this algorithm in CloudSim, a cloud simulation environment, we obtained better results than some other wellknown algorithms. With this MCCVA, we can see the big potential of AI and datamining in Load Balancing, we can further develop LB with AI to achieve better and better results of QoS.

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A Literature Review of QoS with Load Balancing in Cloud Computing Environment

Cited by 16 publications

References 17 publications

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach

Qos and Load Balancing in Cloud Computing- an Access for Performance Enhancement using Agent Based Software

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