Latency in Cloud-Based Applications

Gessert, Felix; Wingerath, Wolfram; Ritter, Norbert

doi:10.1007/978-3-030-43506-6_2

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…User-centric perspectives and quality-of-experience-based cost functions have also been considered to optimize the energy-latency trade-off [64]. The shift towards cloud computing has led to the definition of architectures susceptible to latency at different levels [65]. A delayed offloading model has been devised to harness the capabilities of Wi-Fi and mobile networks, considering energy efficiency, performance metrics, and intermittently available access links [57].…”

Section: Network Performancementioning

confidence: 99%

Edge Offloading in Smart Grid

Arcas,

Cioara,

Anghel

et al. 2024

Smart Cities

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The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications and services to improve resilience and responsiveness and ensure closer to real-time control. However, the large-scale integration of Internet of Things (IoT) devices has led to the generation of significant amounts of data at the edge of the grid, posing challenges for the traditional cloud-based smart-grid architectures to meet the stringent latency and response time requirements of emerging applications. In this paper, we delve into the energy grid and computational distribution architectures, including edge–fog–cloud models, computational orchestration, and smart-grid frameworks to support the design and offloading of grid applications across the computational continuum. Key factors influencing the offloading process, such as network performance, data and Artificial Intelligence (AI) processes, computational requirements, application-specific factors, and energy efficiency, are analyzed considering the smart-grid operational requirements. We conduct a comprehensive overview of the current research landscape to support decision-making regarding offloading strategies from cloud to fog or edge. The focus is on metaheuristics for identifying near-optimal solutions and reinforcement learning for adaptively optimizing the process. A macro perspective on determining when and what to offload in the smart grid is provided for the next-generation AI applications, offering an overview of the features and trade-offs for selecting between federated learning and edge AI solutions. Finally, the work contributes to a comprehensive understanding of edge offloading in smart grids, providing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to support cost–benefit analysis in decision-making regarding offloading strategies.

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Section: Network Performancementioning

confidence: 99%

Edge Offloading in Smart Grid

Arcas,

Cioara,

Anghel

et al. 2024

Smart Cities

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“…With the developing appropriation of cloud infrastructures to convey several IT administrations, observing organization execution has become pivotal [9]. Cloud suppliers likely disclose subjective data about system execution, which hinders productive cloud selection, and causes performance issues, vulnerabilities concerning the conduct of facilitated administrations, and imperfect deployment decisions [36]. CC service models have three types: IaaS, SaaS, and PaaS.…”

Section: Cloud Service Modelsmentioning

confidence: 99%

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

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A Study on the Application of Cloud-Based Database Construction in Language Subject

Yan,

Yang,

et al. 2024

Proceedings of the 3rd International Conference on Cognitive Based Information Processing and Applications–Volume 1

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Latency in Cloud-Based Applications

Cited by 5 publications

References 24 publications

Edge Offloading in Smart Grid

Edge Offloading in Smart Grid

A Review of Machine Learning Algorithms for Cloud Computing Security

A Study on the Application of Cloud-Based Database Construction in Language Subject

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