An Enhanced Hybrid Genetic Algorithm And Particle Swarm Optimization Based on Small Position Values for Tasks Scheduling in Cloud

Musa, Nehemiah; Gital, Abdulsalam Ya’u; Zambuk, Fatima Umar; Usman, Ali Muhammad; Almutairi, Mubarak; Chiroma, Haruna

doi:10.1109/iccis49240.2020.9257696

Cited by 6 publications

(1 citation statement)

References 20 publications

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“…For example, the FGAO algorithm minimised execution time and iterations compared to GA. Furthermore, Musa et al [11] recommended an improved GA-PSO hybrid with small position value (SPV) applications (for the initial population) to diverge from arbitrariness and enhance convergence speed. Consequently, the improved GA-PSO hybrid reflected more valuable outcomes than the conventional GA-PSO algorithm in resource usage and makespan.…”

Section: Heuristic-based Researchmentioning

confidence: 99%

Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

Chraibi

Alla

Ezzati

2021

Scientific Programming

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Despite increased cloud service providers following advanced cloud infrastructure management, substantial execution time is lost due to minimal server usage. Given the importance of reducing total execution time (makespan) for cloud service providers (as a vital metric) during sustaining Quality-of-Service (QoS), this study established an enhanced scheduling algorithm for minimal cloudlet scheduling (CS) makespan with the deep Q-network (DQN) algorithm under MCS-DQN. A novel reward function was recommended to enhance the DQN model convergence. Additionally, an open-source simulator (CloudSim) was employed to assess the suggested work performance. Resultantly, the recommended MCS-DQN scheduler revealed optimal outcomes to minimise the makespan metric and other counterparts (task waiting period, resource usage of virtual machines, and the extent of incongruence against the algorithms).

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Section: Heuristic-based Researchmentioning

confidence: 99%

Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

Chraibi

Alla

Ezzati

2021

Scientific Programming

View full text Add to dashboard Cite

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An efficient lightweight algorithm for scheduling tasks onto dynamically reconfigurable hardware using graph-oriented simulated annealing

Mollajafari

2023

Neural Comput & Applic

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A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

Simaiya,

Lilhore,

Sharma

et al. 2024

Sci Rep

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Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm (“DPSO-GA”) for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

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An Enhanced Hybrid Genetic Algorithm And Particle Swarm Optimization Based on Small Position Values for Tasks Scheduling in Cloud

Cited by 6 publications

References 20 publications

Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

Makespan Optimisation in Cloudlet Scheduling with Improved DQN Algorithm in Cloud Computing

An efficient lightweight algorithm for scheduling tasks onto dynamically reconfigurable hardware using graph-oriented simulated annealing

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques

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