PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

Chen, Zheyi; Yang, Lijian; Huang, Yinhao; Chen, Xing; Zheng, Xianghan; Rong, Chunming

doi:10.1109/access.2020.3017643

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…Adapting a specific workload sequence is typically straightforward for an estimation framework that employs only one predicting model; however, it is difficult with data from the real world, while the workload pattern varies quickly over time 30 . These situations continue despite excessive capacity and under-provisioning.…”

Section: Literature Reviewmentioning

confidence: 99%

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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Section: Literature Reviewmentioning

confidence: 99%

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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“…where c(w i , r i ) is the cost of workload w i executed by resource r i . electricity consumption is the electricity consumed in resource utilization calculated by electricity co n = electricity vm + electricity memory + electricity misc (4) Where electricity vm is the virtual machine electricity consumption, electricity memory is the electricity consumption for memory operations and electricity misc is the electricity consumption for fans and other miscellaneous parts.…”

Section: Scheduling Techniquementioning

confidence: 99%

“…The Particle Swarm Optimization and Genetic Algorithm (PSO-GA) was used to make runtime decisions for exploring the objectives of the resource allocation plan (4) . The issue of resource allocation for cloud-based services is addressed by introducing the workload time window.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Particle Swarm Optimization Based Resource Provisioning Technique in Cloud Computing

Reddy¹,

Indiramma²,

Nagarathna³

2023

IJST

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Objectives: To find the optimal allocation of resources and minimize the overall cost while meeting the performance requirements of the applications. Methods: The proposed Intelligent PSO-based resource optimization in cloud computing evaluates the quality of the solutions based on their resource allocation parameters. Cloud Sim software is used as a simulation tool for testing and evaluating new solutions and strategies in the cloud. The Closest Data Center Service Broker Policy is implemented in Cloud Simulation. Findings: The proposed technique assigns workloads effectively on available resources with an improvement of 10.46% in electricity consumption. Novelty:The algorithm can further be employed for identifying the unused VM's in the Data Center to reduce the cost.

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“…Chen et al 8 Resource allocation algorithm was developed based on current and future workloads by using PSO-GA.…”

Section: Cloud Analystmentioning

confidence: 99%

Efficient Throttled load balancing algorithm to improve the response time and processing time in data center

Indiramma²

2022

Concurrency and Computation

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Summary In today's world, cloud computing is an emerging service, and it has proved to be a profit‐oriented business model. A drastic growth was observed in cloud computing during the last decade because of its easy access to services. As the number of users are increasing dynamically, load balancing is required to handle the user's load. Load balancing algorithms minimize the data center processing time and increase the throughput. Therefore cloud service providers need the best dynamic strategies. In this work, the proposed Efficient Throttled load balancing algorithm improves the performance in the data center environment. Our experimental results indicate that the Efficient Throttled load balancing algorithm improves the average overall response time by 6.47% and average data center processing time by 20.74%, compared to the Throttled load balancing algorithm.

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PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

Cited by 13 publications

References 35 publications

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

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

Intelligent Particle Swarm Optimization Based Resource Provisioning Technique in Cloud Computing

Efficient Throttled load balancing algorithm to improve the response time and processing time in data center

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