AGCM: Active Queue Management-Based Green Cloud Model for Mobile Edge Computing

Ismail, Alshimaa H.; El‐Bahnasawy, Nirmeen A.; Hamed, Hesham F. A.

doi:10.1007/s11277-019-06119-1

Cited by 21 publications

(6 citation statements)

References 31 publications

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“…Ismail et al [93] pointed to reducing energy consumption in mobile devices as the major challenge to adapting to the demands of 5G. Therefore, the authors present a queues management model for Edge Computing.…”

Section: Models-algorithmsmentioning

confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.Electronics 2020, 9, 48 2 of 33 Ledger Technologies (such as blockchain) [15]; or distributed sensing and actuation technologies such as the Internet of Things (IoT) [3,16].Regarding the Internet of Things (IoT), its recognition as a paradigm dates back to 2011-2012, when renowned companies and institutions such as Gartner, Forbes, or Wired started using this term to refer to the emerging technology [17]. The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provid...

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Section: Models-algorithmsmentioning

confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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“…Despite the numerous benefits of cloud computing, the utilization of a centralized cloud infrastructure for IoT applications entails the transmission of all data to the cloud for additional processing, thereby increasing network traffic and service time [12,13]. Some IoT applications, including intelligent transport systems, industrial automation, augmented reality, online interactive games, and healthcare applications, require millisecond-scale real-time responses [14,15]. In many time-sensitive IoT applications, the latency triggered by cloud data transfer is intolerable.…”

Section: Introductionmentioning

confidence: 99%

A Two-tier Multi-objective Service Placement in Container-based Fog-Cloud Computing Platforms

Dogani

Yazdanpanah

Zare

et al. 2023

Preprint

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Using cloud computing for Internet of Things (IoT) applications necessitates the transmission of all data to the centralized structure of the cloud, thereby leading to an increase in network traffic and service time. Consequently, cloud computing proves impractical for latency-sensitive IoT applications. Fog computing, acting as an intermediate layer between the cloud and IoT, ensures low latency for such applications. The service placement problem, an NP-hard problem that determines which node should host each service, represents one of the major challenges in the fog computing paradigm. While lightweight containers have emerged as a highly efficient virtualization approach, prior research predominantly employed traditional VM-based architecture for service placement in fog computing. Therefore, this study introduces a multi-objective optimization approach for dynamic service placement in container-based fog computing, accounting for cost, latency, and energy consumption. Specifically, we propose a two-tier framework for resource management in fog computing based on Kubernetes. The multi-objective non-dominated sorting genetic algorithm II (NSGA-II) algorithm balances conflicting performance objectives, such as latency, cost, and energy consumption. Empirical results demonstrate that the proposed method outperforms existing state-of-the-art methods.

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“…The computation is derived for the edge information management to transmit the service with security. Security is ensured in this method by relating the existing state to the failure in the system (Ismail et al, 2019; Tolba, 2021).…”

Section: Introductionmentioning

confidence: 99%

A convex problem optimization solution for information management in edge computing platforms

Saad

Al-Makhadmeh

2022

Expert Systems

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Intelligent computing and social optimization techniques have been used to manage mobile edge (ME) environments for pervasive services. However, the service demand is the prime cause for requiring flawless information management. This article proposes a coherent information management (CIM) process for resolving the convex optimization problems by considering the flaws in raw information management. The information management problem for pervasiveness is defined as a nonlinear problem for user-to-information availability. According to the CIM, pervasive convergence is identified for the available information. Furthermore, convolutional neural learning identifies unavailable or utilized information between the service provider and the end-user layers. This identification improves the training rate for convergence improvement and information coherency. Therefore, the nonlinear pervasiveness is distributed between different service providers, improving efficiency. Therefore, the proposed optimization method achieves a 7.4% high admittance ratio, 8.28% high utilization, 25.25% less allocation time, and 24.4% less waiting time for different availability ratios for different information availability ratios.

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AGCM: Active Queue Management-Based Green Cloud Model for Mobile Edge Computing

Cited by 21 publications

References 31 publications

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

A Two-tier Multi-objective Service Placement in Container-based Fog-Cloud Computing Platforms

A convex problem optimization solution for information management in edge computing platforms

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