Load Balancing Mechanism Using Fuzzy Row Penalty Method in Cloud Computing Environment

Kumar, Narander; Shukla, Diksha

doi:10.1007/978-981-10-3932-4_38

Cited by 15 publications

(9 citation statements)

References 7 publications

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“…This research focuses on task scheduling using a novel architecture with dynamic queues based on a hybrid algorithm using fuzzy logic and particle swarm optimization to optimize makespan and waiting time. Kumar and Shukla proposed a method for solving the load balancing problem in a cloud computing environment in which a fuzzy technique is used for handling the uncertain response time in cloud environments. The fuzzy row penalty method is used for solving both the balanced fuzzy load balancing problem and the unbalanced fuzzy load balancing problem in a cloud environment.…”

Section: Related Workmentioning

confidence: 99%

An efficient method for allocating resources in a cloud computing environment with a load balancing approach

Pourghaffari

Barari

Kashi

2019

Concurrency and Computation

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Cloud computing technology forms a computational ensemble of large computing services and systems. Recently, it has been the focus of research on resource management, task scheduling, and effective resource sharing among users. Given the computational and resource management challenges in cloud computing, an improved method is required to approach the optimal allocation of resources. The current research combines evolutionary algorithms, fuzzy logic and task scheduling techniques to improve computational cloud resource allocation with the aim of maintaining load balance in cloud providers. Simulation of the proposed model reveals that the response time, task execution time, and energy consumption of the proposed method are better than for those of other methods. KEYWORDS cloud computing, fuzzy logic, load balancing, resource allocation INTRODUCTIONUsers demand access to computer services according to their needs, no matter where these services are located or how they are delivered. In recent years, an increasing number of business-to-consumer and enterprise applications have been run in heterogeneous clouds. 1 The explosive growth in multimedia content is shifting the global infrastructure towards a cloud-based paradigm facilitated by enormous storage capacity and high end computational resources. 2 Cloud computing is one of the best computing frameworks that can offer such services to users.Cloud computing is a model for enabling ubiquitous, on-demand network-based access to a shared pool of configurable computing resources (eg, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing, as a model for distributed computing, provides network-based access to a shared collection of reconfigurable computing resources, which are provided and released with minimal management and service provider interaction. Nowadays, IT companies are moving from the traditional CAPEX model (buy the dedicated hardware and depreciate it over a period of time) to the OPEX model (use a shared cloud infrastructure and pay as one uses it). 3 Cloud computing providers designate their resources as different examples of virtual machine (VM), which are provided to a consumer for a specified period. 4 Storage, memory, processing, and bandwidth are often more efficiently provided through cloud computing. The growth of clouds has been driven to some degree by the use of virtualization. Distributed computing suppliers provide applications through the Internet, which are accessed from a web program, while the application programs and data are secured on servers at a remote location. 5Clouds can be thought of as a service model consisting of three layers, ie, software service provider (SaaS), platform service provider (PaaS), and infrastructure service provider (IaaS). 6 The base layer of any cloud computing system is IaaS. The IaaS layer offers physical servers, virtual machines, storage, and connectivity resources to run enter...

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Section: Related Workmentioning

confidence: 99%

An efficient method for allocating resources in a cloud computing environment with a load balancing approach

Pourghaffari

Barari

Kashi

2019

Concurrency and Computation

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“…Unlike the centralized algorithm, all allocation and scheduling decisions are performed with a single node. Load balancing algorithm in shared mode can be established to perform various activitiestogether which can be balanced using the fuzzy set approach [6]. These intersections are interconnected to achieve load balancing.…”

Section: Fig1 Cloud Computing Model [2]mentioning

confidence: 99%

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

Sharma*,

Pandey

2019

IJEAT

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 Abstract: 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. Index Terms: About four key words or phrases in alphabetical order, separated by commas.The proposed structure has been further evaluated using the SLR and EC. An efficient results has been obtained in a hostile environment, where VMs has been settled down using the start time and completion time. The proposed architecture has been further compared with the conventional approaches. The attained results has been improved in terms of energy consumption and time measurement for balancing the load.

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“…The response time of the resources is optimized by the ACO by distributing the load dynamically [5]. To distribute the load efficiently on the cloud fuzzy row penalty method is presented in [6] for cloud computing. The fuzzy approach is used to solve the problem of uncertainty in the response time in cloud resources.…”

Section: Literature Surveymentioning

confidence: 99%

Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

Khurana¹,

Singh²

2018

ICST Transactions on Scalable Information Systems

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Workflow scheduling is one of the most challenging tasks in cloud computing. It uses different workflows and quality of service requirements based on the deadline and cost of the tasks. The main goal of workflow scheduling algorithm is to optimize the time and cost by using virtual machine migration. This algorithm computes the subset problem and decision problem in NP time. It works on the decision-making process to reduce the time and cost of computation on the server side. This paper proposes hybrid optimization to optimize the virtual machine locally and globally. The PEFT algorithm is used for initialization and worked as a heuristic algorithm. This algorithm reduces the error of random initialization of optimization. The proposed algorithm based upon Flower pollination with Grey Wolf Optimization using hybrid approach shows significant end effective results than flower pollination with genetic algorithm. The proposed approach also considered the reliability parameter on different workflows.

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Load Balancing Mechanism Using Fuzzy Row Penalty Method in Cloud Computing Environment

Cited by 15 publications

References 7 publications

An efficient method for allocating resources in a cloud computing environment with a load balancing approach

An efficient method for allocating resources in a cloud computing environment with a load balancing approach

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

Workflow scheduling and reliability improvement by hybrid intelligence optimization approach with task ranking

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