A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

International Journal of Communication Systems, Год журнала: 2024, Номер 37(15)

Опубликована: Июль 15, 2024

Summary The latest developments in the industrial Internet of things (IIoT) have opened up a collection possibilities for many industries. To solve massive IIoT data security and efficiency problems, potential approach is considered to satisfy main needs IIoT, such as high throughput, security, efficiency, which named blockchain. blockchain mechanism significant boosting protection performance. In quest amplify capabilities blockchain‐based pivotal role accorded Glowworm Swarm Optimization (GSO) algorithm. Inspired by collaborative brilliance glowworms nature, GSO algorithm offers unique harmonizing these conflicting aims. This paper proposes new improve performance optimization using due blockchain's contradictory objectives. proposed system addresses scalability challenges typically associated with technology efficiently managing interactions among nodes dynamically adapting network demands. optimizes allocation resources decision‐making, reducing inefficiencies bottlenecks. method demonstrates considerable improvements through extensive simulations compared traditional algorithms, offering more scalable efficient solution applications context IIoT. simulation computational study shown that considerably improves objective function systems' algorithms. It provides secure systems industries corporations.

Язык: Английский

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Enhanced resource allocation in distributed cloud using fuzzy meta-heuristics optimization

Computer Communications, Год журнала: 2023, Номер 209, С. 14 - 25

Опубликована: Июнь 24, 2023

Язык: Английский

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Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations

Energies, Год журнала: 2023, Номер 16(15), С. 5764 - 5764

Опубликована: Авг. 2, 2023

Data Centres serve as the foundation for digital technologies in energy sector, enabling advanced analytics, optimization, and automation. However, their rapid growth can exert a substantial influence on environment due to consumption, water utilization, production of electronic waste. This research begins with an overview setup operations data centres, highlighting key components infrastructure, emphasizing crucial role managing resources driving sector’s technologies. Building upon this understanding, holistic framework is proposed tackle sustainability concerns focus energy-related aspects. The places emphasis three primary metrics, namely efficiency, waste management. It underscores significance green building design principles energy-efficient equipment constituents sustainable centre infrastructure. delineates optimal operational best practices encompassing virtualization consolidation, effective cooling tactics, management monitoring, all aimed at reducing consumption enhancing performance. Furthermore, emphasizes incorporating metrics into decision-making procedures adhering regulatory standards efficiency. Through adherence framework, centres’ environmental impact be mitigated positive contribution towards future made, particularly realm conservation optimization.

Язык: Английский

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A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms

Future Generation Computer Systems, Год журнала: 2024, Номер 155, С. 465 - 485

Опубликована: Фев. 27, 2024

Язык: Английский

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Mitigating power grid impact from proactive data center workload shifts: A coordinated scheduling strategy integrating synergistic traffic - data - power networks

Applied Energy, Год журнала: 2024, Номер 377, С. 124697 - 124697

Опубликована: Окт. 22, 2024

Язык: Английский

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Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation

Journal of Network and Computer Applications, Год журнала: 2024, Номер 226, С. 103885 - 103885

Опубликована: Апрель 20, 2024

Язык: Английский

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Power Global Multi-Source Heterogeneous Unified Metadata Query Method under Pluggable Storage Framework

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104600 - 104600

Опубликована: Март 1, 2025

Язык: Английский

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Multi-Task Learning for Electricity Price Forecasting and Resource Management in Cloud Based Industrial IoT Systems

IEEE Access, Год журнала: 2023, Номер 11, С. 54280 - 54295

Опубликована: Янв. 1, 2023

Cloud computing has gained immense popularity in the logistics industry. This innovative technology optimizes operations by eliminating requirement for physical equipment calculations. Instead, specialized companies provide cloud-based services, relying heavily on computers and servers that consume substantial amounts of energy. Hence, ensuring availability affordable dependable electricity is paramount efficient design management these services. centers, which are power-intensive, face challenge reducing their energy consumption due to escalating power costs. To address this issue, data placement node strategies commonly employed operations. An AlexNet model been designed optimize storage relocation predict prices. The outcome initiative resulted a considerable reduction at centres. uses Dwarf Mongoose Optimization Algorithm (DMOA) produce an optimal solution increase its performance with real-world dataset from IESO Ontario, Canada. 75% available was used training assure model’s precision, remaining 25% allocated testing purposes. forecasts prices MAE 2.22% MSE 6.33%, resulting average 22.21% expenses. Our proposed method accuracy 97% compared 11 benchmark algorithms, including CNN, DenseNet, SVM having 89%, 88%, 82%, respectively.

Язык: Английский

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Controller placement problem during SDN deployment in the ISP/Telco networks: A survey

Engineering Reports, Год журнала: 2023, Номер 6(2)

Опубликована: Ноя. 3, 2023

Abstract With the successful implementation of Software‐Defined Networking (SDN) in data center networking, way forward for its deployment ISP/Telco network is becoming prominent. Small and medium‐sized networks may easily adopt SDN. The research on SDN a large‐scale continuing. This paper properly presents current status Controller Placement Problem (CPP) Multi‐CPP (MCPP) over with their specific challenges provides comprehensive review major performance metrics, that is, latency, controller load balancing techniques. survey highlights use partitioning‐based CPP clustering approaches benefits context deployment. Moreover, this importance implementing security issues into networks. Finally, we provide some key areas ongoing discuss future direction regarding various SDN‐based (CP) next‐generation IP advanced networking technologies.

Язык: Английский

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Efficient task scheduling in cloud networks using ANN for green computing

International Journal of Communication Systems, Год журнала: 2024, Номер 37(5)

Опубликована: Янв. 18, 2024

Summary Recently, there has been a growing emphasis on reducingenergy consumption in cloud networks and achieving green computing practices toaddress environmental concerns optimize resource utilization. In thiscontext, efficient task scheduling minimizes energy usage enhances overallsystem performance. To tackle the challenge ofenergy‐efficient allocation, we propose novel approach that harnessesthe power of Artificial Neural Networks (ANN). Our neural network Dynamic Balancing (ANNDB) method is designed toachieve environments. ANNDB leverages feed‐forwardnetwork architecture multi‐layer perceptron, effectively allocatingrequests to higher‐power higher‐quality virtual machines, resulting inoptimized Through extensive simulations, wedemonstrate superiority over existing methods, including WPEG,IRMBBC, BEMEC, terms efficiency. Specifically, ourproposed exhibits substantial improvements 13.81%, 8.62%, and9.74% Energy criterion compared WPEG, IRMBBC, BEMEC,respectively. Additionally, Power criterion, achievesperformance enhancements 3.93%, 4.84%, 4.19% mentioned methods.The findings from this research hold significant promise for organizations seekingto their environments while reducing energyconsumption promoting sustainable practices. By adopting theANNDB scheduling, businesses institutions cancontribute efforts, reduce operational costs, make moreenvironmentally friendly choices without compromising allocationperformance.

Язык: Английский

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