Improved Artificial Bee Colony Using Monarchy Butterfly Optimization Algorithm for Load Balancing (IABC-MBOA-LB) in Cloud Environments

Janakiraman, S.; Priya, M. Deva

doi:10.1007/s10922-021-09602-y

Cited by 25 publications

(11 citation statements)

References 26 publications

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“…The comparative investigation of the proposed HDWOA‐LBM is conducted with the baseline HHHPOA, 25 HABC‐MBOA 24 and MMMA‐DLB 23 schemes, since they are the recently proposed hybrid metaheuristic optimization algorithms‐based load balancing mechanisms contributed towards the objective of achieving load balancing with balanced exploration and exploitation in the search space. They were contributed as the multi‐objective constraints‐based optimization solutions which achieved load balancing using the difference between the number of submitted tasks to the number of processing unit, total energy consumption, and deviation of load between each host and mean load of the entire network.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, Janakiraman and Priya 24 proposed a hybrid ABC and MBOA‐based load balancing approach for potential resource utilization by confirming effective and efficient task assignment to suitable VMs. This HABC‐MBOA potentiality adopted ABC for global exploration, and MBOA for exploitation to allocate user tasks to VMs by estimating their conditional status of overloading and underloading.…”

Section: Related Workmentioning

confidence: 99%

“…The system is checked for balancing depending on the state of the VM set. The system is identified to be balanced when the value of SD (

{\mathrm{SD}}_{\left(\upsigma \right)}

) is less than or equal to the SD threshold (

{\mathrm{SD}}_{\left(\mathrm{Thresh}\right)}

) as defined in Reference 24. Otherwise, the system is determined to be in the state of underloaded or overloaded condition.…”

Section: Proposed Hybrid Dingo and Whale Optimization Algorithm‐based...mentioning

confidence: 99%

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Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Ramya

Ayothi

2023

Trans Emerging Tel Tech

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Cloud computing is the most rapidly growing technology that facilitates virtualized computer resources to the users through its service providers. Load balancing and task scheduling is the important concern in the cloud computing scenario from the service provider point of view. The process of load balancing is responsible for allocating the dynamic workload among the cloud systems, such that equal sharing of the resources without the virtual machines (VMs) being overloaded or underloaded. This load balancing process not only needs to allocate the tasks to suitable VMs but also need to maintain trade‐off among VMs. Thus, a potential dynamic and optimized load balancing strategy is essential for improving resource utilization, services, reliability, and throughput by maintaining equilibrium between VMs with minimized makespan. In this article, hybrid dingo and whale optimization algorithm‐based load balancing mechanism (HDWOA‐LBM) is proposed for effective load balancing those aids in maximized throughput, reliability, and resource utilization in the clouds. This HDWOA‐LBM is proposed by mimicking the hunting characteristics of dingo (equivalent to tasks) and VMs as their prey to be hunted. It inherited the process of exploration and exploitation through the chasing and approaching, encircling, and harassing inherent with dingo optimization algorithm (DOA) to determine the optimal allocation of incoming tasks to the suitable VM. In specific, it utilized the merits of whale optimization for improving the exploitation phase of DOA to balance the trade‐off between local and global search. The simulation experiments of the proposed HDWOA‐LBM conducted using CloudSim confirmed better throughput of 21.28%, maximized reliability of 25.42%, minimized makespan of 22.98%, and improved resource allocation of 20.86%, on par with the competitive intelligent load balancing schemes used for investigation.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The system is checked for balancing depending on the state of the VM set. The system is identified to be balanced when the value of SD (

{\mathrm{SD}}_{\left(\upsigma \right)}

) is less than or equal to the SD threshold (

{\mathrm{SD}}_{\left(\mathrm{Thresh}\right)}

) as defined in Reference 24. Otherwise, the system is determined to be in the state of underloaded or overloaded condition.…”

Section: Proposed Hybrid Dingo and Whale Optimization Algorithm‐based...mentioning

confidence: 99%

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Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Ramya

Ayothi

2023

Trans Emerging Tel Tech

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“…It provides support for modeling scheduling systems, cloud service brokers, VMs, physical machine hosts, and data centers. In this process of experimentation, an IaaS cloud provider comprises of 20 VMs of different configurations, two hosts and a data center 42‐44 . The hosts and data center configurations used for the experimental process are presented in Table 2.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Nithiavathy,

Janakiraman,

Deva Priya

2023

Trans Emerging Tel Tech

Self Cite

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In a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.

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“…e core idea of MEC is to decentralize part of the computing and storage capacity of the data center in MCC to the edge of the network, i.e., close to the user. Hence, the data-processing requirements generated by mobile applications can be executed by the MEC server which is located at the edge of local network, and results can be returned without going through the core network and data center [7].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Strategy Using Deep Reinforcement Learning in Cloud-Edge Collaborative Computing Environment

Jun-jie

2022

Mobile Information Systems

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With the development of technologies such as IoT and 5G, the exponential explosion in the amount of new data has put more stringent requirements on ultrareliable and low-delay communication of services. To better meet these requirements, a resource allocation strategy using deep reinforcement learning in a cloud-edge collaborative computing environment is proposed. First, a collaborative mobile edge computing (MEC) system model, which combines the core cloud center with MEC to improve the network interaction ability, is constructed. The communication model and computation model of the system are considered at the same time. Then, the goal of minimizing system delay is modeled as a Markov decision process, and it is solved by using the deep Q network (DQN) which is improved by hindsight experience replay (HER), so as to realize the resource allocation with the minimum system delay. Finally, the proposed method is analyzed based on the simulation platform. The results show that when the number of user terminals is 80, the maximum user delay is 1150 ms, which is better than other comparison strategies and can effectively reduce the system delay in complex environment.

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Improved Artificial Bee Colony Using Monarchy Butterfly Optimization Algorithm for Load Balancing (IABC-MBOA-LB) in Cloud Environments

Cited by 25 publications

References 26 publications

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Hybrid dingo and whale optimization algorithm‐based optimal load balancing for cloud computing environment

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

Resource Allocation Strategy Using Deep Reinforcement Learning in Cloud-Edge Collaborative Computing Environment

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