A Multi-objective Optimization Scheduling Method Based on the Improved Differential Evolution Algorithm in Cloud Computing

Zheng, Zhe; Xie, Kun; He, Shiming; Deng, Jun

doi:10.1007/978-3-319-68505-2_20

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Makespan: To get the best performance from cloud computing resources, the makespan should be minimized. The term makespan is used for the finish time of all cloud tasks using the available cloud resources such as computing power, memory, and storage (Zheng et al, 2017). Makespan is represented in Equation ().

Makespan = {italicFT}_{Max [t, v]} t ϵ T, t = 1, \dots, n l ϵ VM, v = 1, \dots, m .

…”

Section: Proposed Ca‐mlbs Frameworkmentioning

confidence: 99%

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

et al. 2022

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Cloud computing is the on-demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance.Several methods have been proposed in the literature for effective load balancing, including heuristic, meta-heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta-heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI-assisted hybrid approach called Content-aware Machine Learning based Load Balancing Scheduler (CA-MLBS) is proposed. The scheduling system CA-MLBS combines machine learning and meta-heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA-MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively.

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Makespan = {italicFT}_{Max [t, v]} t ϵ T, t = 1, \dots, n l ϵ VM, v = 1, \dots, m .

…”

Section: Proposed Ca‐mlbs Frameworkmentioning

confidence: 99%

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

et al. 2022

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show abstract

“…Minimizing the makespan is essential for the load-balancing algorithms in the cloud computing environment [55]. Makespan is the completion time of all the scheduled tasks with the available computing and storage resources [56].…”

Section: Load Balancing Phasementioning

confidence: 99%

PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

Adil

Nabi

Raza

2022

cai

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Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud computing are scalability, efficiency, and cost reduction. The key challenge in cloud computing is the even distribution of workload across numerous heterogeneous servers. Several Cloud scheduling and load-balancing techniques have been proposed in the literature. These techniques include heuristic-based, meta-heuristics-based, and hybrid algorithms. However, most of the current cloud scheduling and load balancing schemes are not content-aware (i.e., they are not considering the content-type of user tasks). The literature studies show that the content type of tasks can significantly improve the balanced distribution of workload. In this paper, a novel hybrid approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) is proposed. PSO-CALBA scheduling scheme combines machine learning and meta-heuristic algorithm that performs classification utilizing file content type. The SVM classifier is used to classify users' tasks into different content types like video, audio, image, and text. Particle Swarm Optimization (PSO) based meta-heuristic algorithm is used to map user's tasks on Cloud. The proposed approach has been implemented and evaluated using a renowned Cloudsim simulation kit and compared with ACOFTF and DFTF. The proposed study shows significant improvement in terms of makespan, degree of imbalance (DI).

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“…This approach uses execution time, execution cost and service quality as indicators jointly to analyze the scheduling performance. Zheng et al [30] presented a multi-objective scheduling method based on an improved differential evolution (DE) algorithm. By setting adaptive parameters and redefining crossover and selection operators, the improved DE algorithm can address the limitation of slow convergence rate in traditional DE algorithms.…”

Section: B Task Scheduling In Cloud Computing Environmentsmentioning

confidence: 99%

Multiobjective Task Scheduling for Energy-Efficient Cloud Implementation of Hyperspectral Image Classification

Sun

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A Multi-objective Optimization Scheduling Method Based on the Improved Differential Evolution Algorithm in Cloud Computing

Cited by 5 publications

References 16 publications

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

CA‐MLBS: content‐aware machine learning based load balancing scheduler in the cloud environment

PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

Multiobjective Task Scheduling for Energy-Efficient Cloud Implementation of Hyperspectral Image Classification

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