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

Adil, Muhammad; Nabi, Said; Raza, Summair

doi:10.31577/cai_2022_5_1157

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…In 2022, Adil, et al 26 introduced an innovative hybrid strategy called Particle Swarm Optimization‐based Content‐Aware Load Balancing Algorithm (PSO‐CALBA). PSO‐CALBA is a scheduling method that integrates ML with meta‐heuristic algorithm to conduct classification using file content type.…”

Section: Literature Surveymentioning

confidence: 99%

DL‐DC: Deep learning‐based deadline constrained load balancing technique

Champla,

Dhandapani,

Velmurugan

2023

Concurrency and Computation

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SummaryLoad balancing is a crucial feature of cloud computing that evenly distributes workload between servers, network interfaces, and hard drives. Because of dynamic computing over the internet, cloud computing suffers from request overloading. To address this challenge, this paper proposes a new deep learning‐based deadline constrained (DL‐DC) load balancing technique. The proposed DL‐DC technique will improve resource utilization, reduce cost, latency, and response time, as well as balance load between servers and improve reliability. The proposed DL‐DC technique will direct traffic to a load balancer, which will forward the load with a deadline to a deep Inception ResNet. This network considers some parameters, such as sticky session, content‐based and instance health check and efficiently predicts the schedule for the task. Finally, a predicted task schedule has been derived using the DL‐DC model which is used to distribute the task to the virtual machine. The proposed DL‐DC load balancing algorithm is compared with other existing algorithms such as QMPSO, DQTS, and ACSO in terms of cost, makespan, response time, transmission time and task migration. The proposed method achieves up to 21.08% low response time, 27.3% decrease in make span, 25.5% decrease in task migration, and 38.9% decrease in cost respectively.

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Section: Literature Surveymentioning

confidence: 99%

DL‐DC: Deep learning‐based deadline constrained load balancing technique

Champla,

Dhandapani,

Velmurugan

2023

Concurrency and Computation

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“…Training and test datasets statistics(Adil et al, 2022) Iterations and population's best statistics(Adil et al, 2022) …”

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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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