A novel resource management framework in a cloud computing environment using hybrid cat swarm BAT (HCSBAT) algorithm

Kumar, Anshuman; Padmanaban, K.; Velmurugan, A. K.; Shiny, X. S. Asha; Anguraj, Dinesh Kumar

doi:10.1007/s10619-021-07339-w

Cited by 3 publications

(6 citation statements)

References 13 publications

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“…Rizvi et al [25] proposed an algorithm named MFGA (Modified Fuzzy Adaptive Genetic Algorithm) to minimize makespan and improve resource utilization under deadline and budget constraints, and a fuzzy logic controller is designed to control the crossover rate and the mutation rate to prevent the MFGA from getting stuck in local optima. Senthil et al [26] proposed a task allocation algorithm based on Cat Swarm Optimization and BAT algorithm. The BAT algorithm helps the CSO algorithm to get rid of the pre-convergence problem.…”

Section: Optimization Algorithms Relatedmentioning

confidence: 99%

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SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

Jiao,

Wang,

Luo

2024

Preprint

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With the rapid development of Internet technology, the cloud-edge-end computing model has gradually become an essential new computing model. Under this model, terminal task offloading is crucial to task allocation and processing. Existing terminal task offloading solutions mainly focus on optimizing the offloading strategy to minimize system delay, transmission energy consumption, and computation energy cost. However, there are some problems such as the lack of a basis for judging whether to offload or not, the lack of analysis on the attributes of the terminal devices and the edge nodes, and the lack of optimization of load balancing. To address these issues, a Service Reliability Analysis and Elite-Artificial Bee Colony Offloading model (SRA-E-ABCO) is proposed for cloud-edge-end environments. Specifically, a Service Reliability Analysis (SRA) method is proposed to assist in predicting the offloading necessity of terminal tasks and analyzing the attributes of terminal devices and edge nodes. Subsequently, a set of vectors is derived based on the analysis. An Elite Artificial Bee Colony Offloading (E-ABCO) method is proposed, which optimizes offloading decisions by combining elite populations with improved fitness formulas, position update formulas, and population initialization methods. Experiments show that the proposed model has better performance in convergence, delay and energy consumption.

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Section: Optimization Algorithms Relatedmentioning

confidence: 99%

“…Finally, the edge node matching degree is calculated, i.e., the similarity and correlation between edge nodes are weighted and fused as shown in Equation (26), where the weight coefficients are determined using the 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 function is determined as shown in equation (27).…”

Section: Service Reliability Analysis (Sra)mentioning

confidence: 99%

SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

Jiao,

Wang,

Luo

2024

Preprint

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“…In 2021, A. M. Senthil Kumar et al [8] In 2023 K.Pradeep, Sharma, and Jishnu [29] proposed an intense review on Task scheduling parameters which shed light on various strategies that make way for efficient scheduling with fault tolerant approaches in Fog computing. Their work emphasizes on tuning QoS parameters for improved results.…”

Section: Related Workmentioning

confidence: 99%

“…Tent map: Tent chaotic map is also refereed as the logistic map represents particular chaotic effects. The following equation (8) gives the definition of this map:…”

Section: Chimp Updated Cauchy Mutated Coot Algorithm (Cucmca) Optimiz...mentioning

confidence: 99%

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An AHP based Task Scheduling and Optimal Resource Allocation in Cloud Computing

Karimunnisa¹,

Yellamma²

2023

IJACSA

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Cloud systems by virtue characterize ultimate resource utilization with ever evolving user requirements facilitating adaptivity. With a scope of enhancing the QoS needs of user applications, numerous factors are considered for tunning among which Task scheduling promises to grab focus. The Task Scheduling mechanism ascertains improvement by distributing the subtasks to specific set of resources pertaining to prevailing Quality models. The work emphasizes the need for effective task scheduling and optimizing resource allocation by modelling a modified AHP (Analytical Hierarchy Process) driven approach. The proposed method guarantees the functionality in two phases pertaining to Task ranking and pipelined with Optimized scheduling algorithms resulting in maximization of resource utilization. The former phase of task ranking is aided by improved AHP with substantial usage of fuzzy clustering followed by an enhanced CUCMCA (Chimp Updated and Cauchy Mutated Coot Algorithm) algorithm for optimal resource allocation of cloud applications. The contributed model promises leveraged performance of 32% for memory usage, 33.5% for execution time, 29% for makespan and 18% for communication cost over pre-existing conventional models considered.

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Towards Quantum Computing-Inspired Evolutionary Algorithm for Optimized Cloud Resource Management

2024

IJIES

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Resource allocation plays a pivotal role in Cloud Computing (CC), significantly impacting its overall performance. In order to fairly spread workload among servers, network ports, and hard drives, task scheduling is a critical module of CC. Cloud computing experiences request overloading as a result of dynamic computing over the internet. To address this challenge, a novel Cloud based Load balancing using Quantum artificial bee colony Optimization Algorithm has been proposed for task scheduling that can significantly enhance the effectiveness of cloud computing operations. The technique focuses on two important aspects of cloud computing: resource allocation and task scheduling. By achieving load balancing between servers, the proposed CLUQOA (Cloud based Load balancing Using Quantum artificial bee colony Optimization Algorithm) improves reliability and lowers expenses, latency, and response times. To show how effective it is, key performance parameters including makespan, resource usage, task migration, task execution time, and response time are assessed. The results of comparative comparisons with other approaches, such as HUNTER (Holistic resoUrce maNagemenT technique for Energyefficient cloud computing using aRtificial intelligence), FPNSO (Flower Pollination based Non-dominated Sorting Optimization), and ProHPA (Proactive Hybrid Pod Autoscaling), demonstrate the superiority of CLUQOA in terms of improving resource utilization (35.6%, 26.4%, and 13.9%, respectively) and reducing makespan (11.02%, 9.6%, and 10.4%, respectively).

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A novel resource management framework in a cloud computing environment using hybrid cat swarm BAT (HCSBAT) algorithm

Cited by 3 publications

References 13 publications

SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

SRA-E-ABCO: Terminal Task Offloading for Cloud-Edge-End Environments

An AHP based Task Scheduling and Optimal Resource Allocation in Cloud Computing

Towards Quantum Computing-Inspired Evolutionary Algorithm for Optimized Cloud Resource Management

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