Future data center energy-conservation and emission-reduction technologies in the context of smart and low-carbon city construction

Sustainable Cities and Society, Год журнала: 2022, Номер 89, С. 104322 - 104322

Опубликована: Ноя. 25, 2022

Язык: Английский

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A decentralized adaptation of model-free Q-learning for thermal-aware energy-efficient virtual machine placement in cloud data centers

Computer Networks, Год журнала: 2023, Номер 224, С. 109624 - 109624

Опубликована: Фев. 15, 2023

Язык: Английский

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Environmental regulation and carbon emission efficiency: Evidence from pollution levy standards adjustment in China

PLoS ONE, Год журнала: 2024, Номер 19(2), С. e0296642 - e0296642

Опубликована: Фев. 1, 2024

China’s economy experienced great growth, which also induces large carbon emission. Facing the target of “Carbon peak, Carbon neutrality” in China, it is vital to improve emission efficiency. Employing spatial Difference-in-Differences model, this paper investigates impact environmental regulation on efficiency with a quasi-natural experiment Pollution Levy Standards Adjustment China. Our empirical results show that can significantly moreover, two channels are explored: green innovation and industrial upgrading. More specifically, increases regulation, increased improves The industry upgrading Finally, terms city heterogeneity, we find will be more pronounced for larger cities resource-based cities. findings suggest must enhanced both smaller non-resource-based Moreover, promote firms, since risky costly, governments should provide subsidies or grants corporate technologies, thus firms motivated invest technologies reduce

Язык: Английский

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AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing

Computer Communications, Год журнала: 2023, Номер 200, С. 86 - 94

Опубликована: Янв. 9, 2023

Язык: Английский

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Optimizing Cloud Resource Management with an IoT-enabled Optimized Virtual Machine Migration Scheme for Improved Efficiency

Journal of Network and Computer Applications, Год журнала: 2025, Номер unknown, С. 104137 - 104137

Опубликована: Фев. 1, 2025

Язык: Английский

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Energy-efficient task scheduling with binary random faults in cloud computing environments

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 94, С. 101877 - 101877

Опубликована: Фев. 26, 2025

Язык: Английский

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An Efficient Deadline Based Priority Job Scheduling in Mobile Cloud Computing

IET Communications, Год журнала: 2025, Номер 19(1)

Опубликована: Янв. 1, 2025

ABSTRACT Mobile cloud computing (MCC) combines the portability of mobile devices with data centers to provide advanced services. MCC serves us in various ways our daily lives, including multimedia streaming, gaming, corporate apps, and data‐intensive applications such as augmented reality virtual reality. Among several challenges involved achieving best performance for this service, job scheduling emerges a particularly critical one. User satisfaction, service provider requirements, user priority, provider's resource limitation, deadline, energy consumption, etc., are main constraints while maintaining computing. To improve quality (QoS) achieve effectiveness scheduling, we have proposed multi‐objective model balance situation between gratification demand. optimize cost efficiency machine, two types jobs represent unconstrained constrained center. The shortest execution first (SEFS) algorithm is applied job, efficient deadline priority (EDPS) job. Our improves existing state‐of‐the‐art algorithms. Reducing time minimizing consumption providers improvements algorithm.

Язык: Английский

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Multi‐Criteria Optimization of Scientific Workflow Schedules for Improved Energy Efficiency in Cloud Infrastructures

Concurrency and Computation Practice and Experience, Год журнала: 2025, Номер 37(9-11)

Опубликована: Апрель 9, 2025

ABSTRACT Rising global dependence on cloud services has become crucial for enterprises, aiming to guarantee continuous data accessibility while pursuing enhanced energy efficiency and minimized carbon emissions from centers. However, the persistent challenge of high‐energy consumption in these facilities necessitates a concentrated approach toward reduction. This paper introduces an innovative multi‐objective scheduling strategy scientific workflows, tailored heterogeneous computing environments. Our method employs hybrid genetic algorithm, incorporating Hill Climbing generate initial population chromosomes. Subsequently, algorithm optimizes task assignments most suitable virtual machines, utilizing meticulously designed fitness function evaluate each chromosome's suitability solving problem. Through extensive experimentation, we demonstrate that our proposed outperforms other techniques terms solution quality, contributing reduced consumption, processing duration, cost. We contend this holds substantial potential mitigating footprint associated with centers, offering sustainable environmentally conscious workflow scheduling.

Язык: Английский

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Dependency-Aware Vehicular Task Scheduling Policy for Tracking Service VEC Networks

IEEE Transactions on Intelligent Vehicles, Год журнала: 2022, Номер 8(3), С. 2400 - 2414

Опубликована: Ноя. 24, 2022

In this paper, we study a tracking service vehicular edge computing (VEC) network that provides computation offloading for Intelligent vehicles, where computational tasks with different urgency and dependency are required to be completed efficiently within strict time constraints. We consider the actual scenario environmental parameters fluctuate randomly their distributions unknown, thus, long-term scheduling policy optimization problem needs solved. For motivation, first define queueing criterion sort subtasks into queue, then model specific Markov decision process (MDP) according queue. Furthermore, propose our task optimizing (VTSPO) algorithm based on most advanced policy-based deep reinforcement learning (DRL). The experimental results compared known value-based DRL algorithms verify advantages of proposed VTSPO algorithm.

Язык: Английский

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Decentralized and scalable hybrid scheduling-clustering method for real-time applications in volatile and dynamic Fog-Cloud Environments

Journal of Cloud Computing Advances Systems and Applications, Год журнала: 2023, Номер 12(1)

Опубликована: Апрель 28, 2023

Abstract A major challenge in Cloud-Fog settings is the scheduling of workflow applications with time constraints as environment highly volatile and dynamic. Furthermore, adding complexities handling IoT nodes, owners requests, renders problem space even harder to address. This paper presents a hybrid scheduling-clustering method for addressing this challenge. The proposed lightweight, decentralized, dynamic clustering algorithm based on fuzzy inference intrinsic support mobility form stable well-sized clusters nodes while avoiding global recurrent re-clustering. distributed uses Cloud resources along mobile inert Fog schedule time-constrained considering proper balance between contradicting criteria promoting scalability adaptability. Velociraptor simulator (version 0.6.7) has been used throughtly examine compare real workloads two contemporary noteworthy methods. evaluation results show superiority resource utilization about 20% better success rate almost 21% compared other Also, parameters such throughput energy consumption have studied reported.

Язык: Английский

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