EERA: An Energy-Efficient Resource Allocation Strategy for Mobile Cloud Workflows

Li, Juan; Xu, Xing

doi:10.1109/access.2020.3040785

Cited by 6 publications

(5 citation statements)

References 37 publications

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“…Li et al (5) present an adaptive aid allocation method for value optimization in cloud computing. With the aid of dynamically adjusting resource allocation based on workload traits and price constraints, the proposed approach correctly reduces cloud costs whilst keeping performance.…”

Section: Literature Reviewmentioning

confidence: 99%

AI-powered financial operation strategy for cloud computing cost optimization for future

Mageshkumar,

Rajkumar,

Viji

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Cloud computing has revolutionized the way groupings feature by way of manner of presenting scalable and flexible infrastructure services. However, dealing with cloud cost efficiently remains a project, as cloud environments emerge as more complex. This paper proposes an AI-powered financial operation approach for optimizing cloud computing cost. The method leverages AI algorithms to research utilization patterns, forecast future calls, and advise price-saving measures. By imposing this approach, agencies can acquire massive financial savings at the same time as ensuring the nice everyday normal performance and scalability in their cloud infrastructure. Cloud computing has obtained massive prominence in commercial enterprise because of its capacities. However, the effective management of cloud cost remains a complex agency. However, incorporating automation and Machine Learning (ML) gives a possibility to manipulate and mitigate cloud charges successfully, rendering cloud computing an additional economically viable solution. This study will investigate into the transformative effect of automation and Machine learning cloud cost optimization, providing insights into how companies can harness those technologies to curtail fees on the equal time as addressing ability implementation-demanding situations. As organizations increasingly trust upon cloud computing services for their operations, optimizing the related prices performances into a crucial factor of financial management. This paper proposes an AI-powered financial operation technique for cloud computing fee optimization. The technique leverages tool-reading algorithms to investigate historic utilization patterns, forecast future desires, and perceive capability fee-saving possibilities. It integrates with cloud service providers' APIs to continuously reveal useful resource usage and adjust provisioning ranges dynamically. Additionally, the technique includes anomaly detection strategies to discover inefficiencies or sudden spikes in utilization, permitting proactive fee management. Through the implementation of this AI-powered technique, businesses can gain huge discounts in cloud computing costs even while preserving the finest overall performance and scalability.

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Section: Literature Reviewmentioning

confidence: 99%

AI-powered financial operation strategy for cloud computing cost optimization for future

Mageshkumar,

Rajkumar,

Viji

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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“…In Reference 56, an energy‐efficient resource allocation strategy is proposed for workflow applications in MCC. In the first step, the resource allocation problem is formulated into an optimization problem with the objective of minimizing the utility cost, which represents the trade‐off between energy consumption and total.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

“…According to the above explanations, the classification of workflow scheduling methods has been shown in Figure 4. In the first part, the classification of methods is based on the nature of scheduling algorithms, which includes super-heuristic 9 Meta Heuristic methods [5], [16], [21], [35], [36], [37], [50], [51], [52], [54], [55], [56], [57], [58], [61], [62], [63], [64], [65], [66], [67] 9 Heuristic methods [1], [2], [13], [17], [18], [19], [22], [25], [26], [28], [29], [30], [31], [32], [33], [34], [42], [46], [47], [48], [49] 9 F I G U R E 4 Classification of workflow scheduling methods. 12 methods and heuristic methods.…”

Section: Classification Of Workflow Scheduling Algorithmsmentioning

confidence: 99%

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Golmohammadi

Tabbakh

Ghaemi

2023

Trans Emerging Tel Tech

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The advent of distributed computing and mobile clouds made it possible to transfer and distribute the heavy processes of complex workflows to the cloud. Managing and with the help of the mobile cloud, virtualization and shared resources managing the executing large‐scale workflows is possible. Since virtual resources are used, scheduling and resource allocation are important and key research topics in public cloud environment and mobile clouds. The cloud model of mobile devices with public or virtual cloud, which consists of temporary mobile devices, is an interesting topic to be investigated. Workflow scheduling and availability of resources in mobile clouds by high mobility and Energy efficient, it is one of the challenges and problems that must be investigated one of the main challenges in this research area. The purpose of scheduling algorithms is to improve service quality criteria by observing the constraints. In this article, the main goal is to extensively review the scheduling algorithms for the complex scientific workflow in public and mobile clouds. Furthermore, a review of the use of heuristic and meta‐heuristic techniques in dynamic and static states, with various constraints for task scheduling in cloud and mobile computing has been performed. This article accomplishes a review of using existing techniques for scheduling tasks in mobile clouds. We also present a comprehensive analysis and systematic comparison between these scheduling algorithms.

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“…EERA [92], an energy-efcient resource allocation strategy for mobile cloud workfows, has been proposed by Li et al To illustrate the trade-of between energy usage and execution time, researchers developed the concept of utility cost. An optimization model is used to solve the resource allocation problem, aiming to reduce utility costs while meeting the execution time constraints of mobile cloud workfow applications.…”

Section: Energy-efcient Resource Provisioning Techniquementioning

confidence: 99%

Resource Provisioning Techniques in Multi-Access Edge Computing Environments: Outlook, Expression, and Beyond

Durga¹,

Daniel

Andrew

et al. 2022

Mobile Information Systems

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Mobile cloud computing promises a research foundation in information and communication technology (ICT). Multi-access edge computing is an intermediate solution that reduces latency by delivering cloud computing services close to IoT and mobile clients (MCs), hence addressing the performance issues of mobile cloud computing. However, the provisioning of resources is a significant and challenging process in mobile cloud-based environments as it organizes the heterogeneous sensing and processing capacities to provide the customers with an elastic pool of resources. Resource provisioning techniques must meet quality of service (QoS) considerations such as availability, responsiveness, and reliability to avoid service-level agreement (SLA) breaches. This investigation is essential because of the unpredictable change in service demands from diverse regions and the limits of MEC’s available computing resources. In this study, resource provisioning approaches for mobile cloud computing are thoroughly and comparatively studied and classified as taxonomies of previous research. The paper concludes with an insightful summary that gives recommendations for future enhancements.

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EERA: An Energy-Efficient Resource Allocation Strategy for Mobile Cloud Workflows

Cited by 6 publications

References 37 publications

AI-powered financial operation strategy for cloud computing cost optimization for future

AI-powered financial operation strategy for cloud computing cost optimization for future

A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds

Resource Provisioning Techniques in Multi-Access Edge Computing Environments: Outlook, Expression, and Beyond

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