Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud

Ouyang, Mingxue; Xi, Jing; Bai, Weihua; Li, Keqin

doi:10.1109/access.2022.3198971

Cited by 6 publications

(9 citation statements)

References 37 publications

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“…Here, we evaluate the proposed AF‐CSDS deployment strategy based on the above experimental data and the simulation running environment. In the experiment, it was compared with existing deployment strategies such as APSO‐TSDS, 5 MSG‐NSGA‐III, 20 ACO‐MCMS, 19 GA‐NSGA‐II, 17 and the Spread algorithm implemented in Docker Swarm 8 The APSO‐TSDS strategy is a service deployment strategy based on an accelerated particle swarm optimization algorithm.…”

Section: Experiments and Results Evaluationmentioning

confidence: 99%

“…Its goal is to improve CPU resource utilization and reduce task execution costs. In a previous paper, 5 we established an improved accelerated particle swarm optimization (APSO) algorithm, which is based on the service function chain (SFC) to gather the execution containers of services to the same physical node as much as possible to solve the deployment problem of microservices, considering the transmission overhead between services, resource utilization rate of CDC clusters, and aggregation degree between containers as optimization objectives to improve the resource utilization of the CDC.…”

Section: Related Workmentioning

confidence: 99%

“…The CDC model for container‐based service deployment is illustrated in Figure 1. The BAC as a Service (BaaS) platform is a type of Internet of Things (IoT) cloud platform proposed by us, which is composed of a group of band‐area application containers (BACs) 5 . BAC is an aggregation of tool services, documents, users, messages, and other objects, as well as relevant operation rules.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Microservice architecture (MSN) is currently an important application development mode that divides the monolithic application into a group of independent fine‐grained and componentized services 4 . A lightweight communication mode is adopted between services, and the service function chain (SFC) is formed through the call relationship to form the application 5 . Because of their high reusability, scalability, and flexibility, microservices are usually deployed in multiple computing servers or cloud data centers (CDC) in the form of VMs and containers.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A container deployment strategy for server clusters with different resource types

Ouyang

Bai

et al. 2023

Concurrency and Computation

Self Cite

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The method of deploying microservices based on container technology is widely used in cloud environments. This method can realize the rapid deployment of microservices and improve the resource utilization of cloud datacenters. However, resource allocation and deployment of container-based microservices are key issues. With the continuous growth of computing-and storage-intensive services, it is necessary to consider the deployment of microservices of different business types. This study establishes a multi-objective optimization problem model with the similarity between containers and servers, load balance of clusters, and reliability of microservice execution as the optimization objectives. An improved artificial fish swarm algorithm is proposed for the container deployment of computing-and storage-intensive microservices. The comprehensive experimental results show that, compared with the existing deployment strategies, the matching degree between the container and server, cluster load balance value, service execution reliability, and other performance parameters are improved while shortening the running time of the algorithm. In addition, under the constraint of load balancing, the resource utilization of the computing and storage server clusters is improved.

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Section: Experiments and Results Evaluationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A container deployment strategy for server clusters with different resource types

Ouyang

Bai

et al. 2023

Concurrency and Computation

Self Cite

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“…The Particle Swarm Optimization (PSO) methodology is based on mimicking the foraging behavior of flock animals like birds using an optimization tool that outperforms the group intelligence approach. Some technical optimization issues can be solved with the PSO method [54,55,56,57], which was developed after observing this class of animal foraging patterns. All particles in the PSO algorithm have an adjustable value, and their search velocity and range are controlled by the speed at which they move.…”

Section: ) Particle Swarm Optimizationmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middleincome countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

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Joint optimization of load balancing and resource allocation in cloud environment using optimal container management strategy

Muniswamy,

Vignesh

2024

Concurrency and Computation

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SummaryDue to the high performance of cloud computing‐based microservices, a wide range of industries and fields rely on them. In a containerized cloud, traditional resource management strategies are typically used to allocate and migrate virtual machines. A major problem for cloud service providers is resource allocation for containers, which directly affects system performance and resource consumption. In this paper, we propose a joint optimization of load balancing and resource allocation in the cloud using an optimal container management strategy. We aim to enhance scheduling efficiency and reduce costs by improving the container's schedule requested digitally by users. An improved backtracking search optimization (IBSO) algorithm is used to allocate resources between end‐users/IoT devices and the cloud under the consideration of service‐level agreements. Mechanic quantum recurrent neural networks (MQ‐RNNs) are designed to allocate, consolidate, and migrate containers in cloud environments. The various simulation measures used to validate the proposed strategy are energy consumption, number of active servers, number of interruptions, total cost, runtime, and statistical measures.

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Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud

Cited by 6 publications

References 37 publications

A container deployment strategy for server clusters with different resource types

A container deployment strategy for server clusters with different resource types

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Joint optimization of load balancing and resource allocation in cloud environment using optimal container management strategy

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