A comprehensive survey of energy-efficient computing to enable sustainable massive IoT networks

Alexandria Engineering Journal, Год журнала: 2024, Номер 91, С. 12 - 29

Опубликована: Фев. 6, 2024

Energy efficiency is a key area of research aimed at achieving sustainable and environmentally friendly networks. With the rise in data traffic network congestion, IoT devices with limited computational power energy resources face challenges analyzing, processing, storing data. To address this issue, computing technology has emerged as an effective means conserving for by providing high-performance capabilities efficient storage to support collection processing. As such, energy-efficient computing, or "green computing," become focal point researchers seeking deploy large-scale This study provides comprehensive Survey recent efforts green best our knowledge, none studies literature have discussed all types (edge, fog, cloud) their role enabling massive networks terms efficiency. The article starts overview technologies then goes discussion empowering energy-saving techniques environments including, energy-aware architecture, aggregation compression, low-power hardware, scheduling, task offloading, switching on/off unused resources, virtualization, harvesting, cooling optimization. outline roadmap toward realizing vision environment networks; addition, open door interested follow continue Energy-Efficient Computing.

Язык: Английский

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SPP: stochastic process-based placement for VM consolidation in cloud environments

Computing, Год журнала: 2025, Номер 107(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Integrated publish/subscribe and push-pull method for cloud based IoT framework for real time data processing

Measurement Sensors, Год журнала: 2023, Номер 27, С. 100699 - 100699

Опубликована: Фев. 8, 2023

Cloud-based IoT is a platform that connects smart devices to the cloud for real-time data analysis. Any device on can connect through messaging. The integrated publish/subscribe and push/pull methods cloud-based framework scalable connecting processing are proposed. proposed uses messaging broker push-pull method transmitting from cloud. publish via broker, which service providers subscribe. This publishing transferring of implemented with help mechanism. In this mechanism, makes computations required select provider. Hence, overhead reduced. All go in parallel, reduces latency system. system flexible any number devices, brokers, providers, shows scalable. results demonstrated effectiveness model, was developed using focus scalability latency.

Язык: Английский

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Secured VM Deployment in the Cloud: Benchmarking the Enhanced Simulation Model

Applied Sciences, Год журнала: 2024, Номер 14(2), С. 540 - 540

Опубликована: Янв. 8, 2024

Cloud computing has gained widespread recognition for facilitating myriad online services and applications. However, the current stages of commercial cloud employ a moderate design, wherein computational resources like storage servers are housed in few sizable worldwide data centers. System reliability, efficiency, low latency all goals virtual machine (VM) placement. Load balancing emerged as crucial challenge attaining energy efficiency fictitious grid architecture where variety users’ workloads distributed across several machines. We propose more effective optimization technique known twin fold moth flame algorithm. This algorithm considers multiple constraints, including computation time, stability, placement cost. The proposed model’s effectiveness will be evaluated based on relocation costs, reaction times, stability assessments. most significant gains presented work 4.24%, 9.73%, 11.10%, 28.83%, 7.63%, 10.62% 20 count nodes artificial bee colony–bat algorithm, ant colony optimization, crow search krill herd, whale genetic improved Lévy-based respectively.

Язык: Английский

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A Modified Bat Mechanism for Virtual Machine Migration in a Cloud Environment

SN Computer Science, Год журнала: 2025, Номер 6(1)

Опубликована: Янв. 14, 2025

Язык: Английский

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A systematic review of various load balancing approaches in cloud computing utilizing machine learning and deep learning

International Journal of Data Science and Analytics, Год журнала: 2025, Номер unknown

Опубликована: Янв. 27, 2025

Язык: Английский

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Optimizing data privacy and security measures for critical infrastructures via IoT based ADP2S technique

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Март 21, 2025

The sensitive nature of the data processed by critical infrastructures a shared platform like internet things (IoT) makes it vulnerable to wide range security risks. These must have robust measures protect privacy user transmitted processing systems that utilize them. However, loss and complexities are significant issues when handling enormous in IoT applications. This paper uses reptile search optimization algorithm offer attuned protection with scheme (ADP2S). study follows reptiles' hunting behaviours find vulnerability our service's security. system activates swarm after successfully gaining access explode ice. An attack authentication explodes at breach location. number densities extent which they explore new area both functions severity breach. Service response related prevention time verify fitness according service-level value. service provider contribute authentication, is carried out via elliptic curve cryptography two-factor authentication. reptile's exploration exploitation stages merged sharing similar location across initialized candidates. proposed leverages detection recommendations 11.37% 8.04%, respectively. It reduces loss, estimation time, complexity 6.58%, 10.9%, 11.21%,

Язык: Английский

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EESF: Energy-Efficient Scheduling Framework for Deadline-Constrained Workflows with Computation Speed Estimation Method in Cloud

Parallel Computing, Год журнала: 2025, Номер unknown, С. 103139 - 103139

Опубликована: Май 1, 2025

Язык: Английский

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Blockchain Based Delay‐Tolerant Resource Optimization in Fog and Cloud Layers Utilizing NNGOA and LS²BiOLSTM

Transactions on Emerging Telecommunications Technologies, Год журнала: 2025, Номер 36(6)

Опубликована: Май 26, 2025

ABSTRACT Resource Optimization (RO) in fog and cloud layers enhances performance, minimizes costs, ensures seamless integration of distributed systems. However, prevailing works failed to perform resource optimization both due their complex disparate architectures. Therefore, the proposed work performs efficiently by predicting network traffic congestion using Neuron Northern Goshawk Algorithm (NNGOA) Log Sigmoid Softplus Bidirectional Orthogonal Long Short‐Term Memory (LS 2 BiOLSTM). At first, Cloud Users are registered logged for task assignments. Meanwhile, Smart Contract (SC) based Service Level Management (SLM) is created tasks. After that, signature SLA verified during assignment. For tasks, LS BiOLSTM utilized. Then, predicted tasks clustered mapped into a layer. Simultaneously, from Server (CS), data center prioritized SoftSign Bell‐Fuzzy (SSB‐Fuzzy). Finally, resources optimized with high accuracy 98.1259% NNGOA, which outperforms existing methodologies.

Язык: Английский

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Predictive Resource Allocation Strategies for Cloud Computing Environments Using Machine Learning

Deleted Journal, Год журнала: 2024, Номер 19(2), С. 68 - 77

Опубликована: Янв. 25, 2024

Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, cost-efficiency. IT operations service delivery have changed due widespread access. efficiently allocates resources in cloud environments, making it crucial this transformation. Resource allocation impacts efficiency, cost, performance, SLAs. Users providers allocate based on workloads using elasticity, on-demand provisioning. economics effectiveness rapid flexible allocation. Proactive versus reactive key understanding management challenges opportunities. Reactive strategies only when shortages or surpluses occur at demand. This responsive strategy often leads inefficiencies like over- under-allocation, which raises costs lowers performance. Predictive analysis workload forecasting predict proactive Optimize avoid over-provisioning. Attention has been drawn predictive These methods historical data, machine learning, analytics. optimize by considering future decisions. Reduced bottlenecks boost user satisfaction lower costs. Matching distribution optimizes management. prediction improves deep learning. CNN, LSTM, Transformer algorithms are promising. New tools for accurate predictions come from their ability spot intricate patterns data. paper compares learning forecasting. study determines the best accuracy ada[1]ptability algorithm Google Cluster Data (GCD). The evaluates upgrading model. advances strategies, help organizations improve utilization, cost-effectiveness, performance face of technological change.

Язык: Английский

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