A Bee Colony based Multi-Objective Load Balancing Technique for Cloud Computing Environment

Soni, Ashish; Vishwakarma, Gagan; Jain, Yogendra Kumar

doi:10.5120/19967-1825

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…In [9], an algorithm is proposed that minimizes the server and request load concerning priorities. Cloud manager takes the request from the user and stores them in a stack, and then prioritizes.…”

Section: Literature Reviewmentioning

confidence: 99%

Load Balancing in Cloud Computing Empowered with Dynamic Divisible Load Scheduling Method

Ahmad

2021

Research Journal Of Computer Science And Information Technology

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The need to process and dealing with a vast amount of data is increasing with the developing technology. One of the leading promising technology is Cloud Computing, enabling one to accomplish desired goals, leading to performance enhancement. Cloud Computing comes into play with the debate on the growing requirements of data capabilities and storage capacities. Not every organization has the financial resources, infrastructure & human capital, but Cloud Computing offers an affordable infrastructure based on availability, scalability, and cost-efficiency. The Cloud can provide services to clients on-demand, making it the most adapted system for virtual storage, but still, it has some issues not adequately addressed and resolved. One of those issues is that load balancing is a primary challenge, and it is required to balance the traffic on every peer adequately rather than overloading an individual node. This paper provides an intelligent workload management algorithm, which systematically balances traffic and homogeneously allocates the load on every node & prevents overloading, and increases the response time for maximum performance enhancement.

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Section: Literature Reviewmentioning

confidence: 99%

Load Balancing in Cloud Computing Empowered with Dynamic Divisible Load Scheduling Method

Ahmad

2021

Research Journal Of Computer Science And Information Technology

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“…The MOHB algorithm is proposed by Soni et al [26]. This algorithm optimizes system performance based on multiobjective requirements, by optimizing the fitness value of task priority, load and execution error.…”

Section: Multi-objective Honey Bee Algorithmmentioning

confidence: 99%

“…• Lower degree of imbalance, • Lower makespan. MOHB algorithm A. Soni et al [26] To schedule workload and minimize the total processing cost.…”

Section: Moo Algorithmmentioning

confidence: 99%

“…behavior load balancing (HBB-LB) algorithms [35]. And the bee colony-multi-objective algorithm provides better results than the single objective bee colony algorithm [26]. From the experimental results shown in [36], the multiple ant bee colony optimization (MABC) algorithm is significantly more effective in an unstable network than the multiple ant colony optimization (MACO) algorithm.…”

Section: Moo Algorithmmentioning

confidence: 99%

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Load Balancing Based on Optimization Algorithms: An Overview

Mbarek¹,

Mosorov²

2019

JTIT

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Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.

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“…Some requests demand more computation than communication while others are more communication oriented. The parameter computation-to-communication ratio is used to determine whether workflow tasks are computation intensive or communication intensive [10].…”

Section: Introductionmentioning

confidence: 99%

Deadline Constrained Workflow Scheduling Optimization by Initial Seeding with ANT Colony Optimization

Sinha¹,

Srivastav²,

Ahmad³

2016

IJCA

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Cloud computing is a new model of service provisioning in distributed systems. It encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in workflow scheduling in cloud environment is its quality of service, which minimizes the cost of computation of workflows. In this paper, we use the Predicted Earliest finish time (PEFT) for initial seeding to Ant Colony optimization technique (ACO). As we know ACO is a very powerful technique appropriate for optimization.. The increasing complexity of the workflow applications is forcing researchers to explore hybrid approaches to solve the workflow scheduling problem. In this paper we proposed PEFT with ACO algorithm which reduces the initialization complexity and converge ACO algorithm.

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A Bee Colony based Multi-Objective Load Balancing Technique for Cloud Computing Environment

Cited by 12 publications

References 10 publications

Load Balancing in Cloud Computing Empowered with Dynamic Divisible Load Scheduling Method

Load Balancing in Cloud Computing Empowered with Dynamic Divisible Load Scheduling Method

Load Balancing Based on Optimization Algorithms: An Overview

Deadline Constrained Workflow Scheduling Optimization by Initial Seeding with ANT Colony Optimization

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