OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing

Mallikarjuna, Basetty; Krishna, P. Venkata

doi:10.1515/cait-2015-0060

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…Honeybee Foraging [88][89] [90] Reduced response and makespan time, enhances the complexity of the system When the number of VM's increases or decreases throughput remains unchanged.…”

Section: High Complexity and Low Scalabilitymentioning

confidence: 99%

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Load Balancing in Cloud Environment: A State-of-the-Art Review

Lohumi,

Gangodkar,

Srivastava

et al. 2023

IEEE Access

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IT services and resources on-demand through Internetwork are offered by Cloud Computing (CC), including the pay-for-you-go aspect. A lot is offered by the CC paradigm, such as Infrastructure related services, computing services, storage, and environments for deployment are also provided. The objective of this study is to survey one of the significant challenges in cloud computing, which is a multi-variant, multi-constraint issue termed Load unbalancing, resulting in the demising of the scalability, efficiency, and performance of the system. Equilibrium in the server workload distribution is still strived for by cloud service providers. The unbalancing issue is resolved by load balancing solutions in two ways: overloading and underloading. An extensive structural literature analysis of Load balancing and its constituent domains with the inclusion of various parameters, such as scalability, make-span, and throughput, are depicted in this research paper to enhance the QoS. A detailed and organized taxonomy of all the Load balancing algorithms based on nature system state, techniques, functionality, and types is also presented. The major focus of the survey is around the Static, dynamic, hybrid, and nature-inspired Load-Balancing algorithms.

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“…Honeybee Foraging [88][89] [90] Reduced response and makespan time, enhances the complexity of the system When the number of VM's increases or decreases throughput remains unchanged.…”

Section: High Complexity and Low Scalabilitymentioning

confidence: 99%

“…LB algorithm based on osmosis [18] [91] Tasks can be reallocated easily, Supports homogeneous and heterogeneous setting mode in the virtual machines.…”

Section: High Complexity and Low Scalabilitymentioning

confidence: 99%

Load Balancing in Cloud Environment: A State-of-the-Art Review

Lohumi,

Gangodkar,

Srivastava

et al. 2023

IEEE Access

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“…Ant Colony & Complex Network LB [37]  Assign tasks to the corresponding node  Maximum job schedule  Lack of response time  Lack of migration time  Lack of throughput Osmosis LB Algorithm [38]  VMs in homogenous as well as in heterogeneous settings  Helpful in reallocating the tasks between adjoining VMs  Carry out just one task at a time  Decentralized…”

Section: Suffers From Starvationmentioning

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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“…The QoS‐based algorithms are designed to utilize the data center resources in order to meet the users' requirements in an efficient manner. A nature‐inspired–based algorithm called Osmosis Load Balancing (OLB) for better performance as well as load balancing was presented by Mallikarjuna and Krishna 11 . The algorithm uses the principle of osmosis to reschedule the tasks of VMs and is based on the distributed hash table (DHT) with a chord overlay mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm uses the principle of osmosis to reschedule the tasks of VMs and is based on the distributed hash table (DHT) with a chord overlay mechanism. Similarly, Mallikarjuna and Krishna 12 designed a nature‐inspired–based algorithm for load balancing among VMs, which is called honeybee foraging (LB‐HBF). Their algorithm considered equal time allocation policy, including reducing the task migrations over VMs.…”

Section: Introductionmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA).Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well-known NPhard problem. To resolve this problem, this paper proposes an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision-making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. K E Y W O R D Sbest-worst method (BWM), cloud computing, energy consumption, multi-criteria decision making, TOPSIS method

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OLB: A Nature Inspired Approach for Load Balancing in Cloud Computing

Cited by 12 publications

References 18 publications

Load Balancing in Cloud Environment: A State-of-the-Art Review

Load Balancing in Cloud Environment: A State-of-the-Art Review

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

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

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