WattEdge: A Holistic Approach for Empirical Energy Measurements in Edge Computing

Aslanpour, Mohammad Sadegh; Toosi, Adel Nadjaran; Gaire, Raj; Cheema, Muhammad Aamir

doi:10.1007/978-3-030-91431-8_33

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…It is a virtualization technology that enables applications to be deployed and securely isolated from one another in the same physical or cloud environment. It differs from traditional virtualization [41], which uses a hypervisor layer to separate multiple operating systems and applications running on the same hardware.…”

Section: Container-based Virtualizationmentioning

confidence: 99%

Optimizing Edge Computing through Virtualization: A Comprehensive Review

Saini

2024

ijarcs

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The adoption of virtualization in edge computing promotes the use of numerous applications and operating systems on a single physical server. The technological environment is increasingly efficient, economical, and hassle-free by virtue of this technology. This study explores the potential of virtualization in edge computing environments by creating virtual versions of physical resources and services. The primary objective of this study is to reduce hardware and operational expenses by consolidating multiple systems into one physical system. Various deployment strategies for virtual machines on edge nodes, including containers, hypervisors, and virtual machines, are discussed. The article also delves into a virtualization-based framework to address challenges in edge computing. Additionally, through historical research on virtualization techniques, this article provides insights into the benefits and limitations of virtualization in edge computing. Moreover, the integration of virtualization and edge computing is examined, highlighting open challenges that need to be addressed for optimal utilization of virtualization in edge computing environments. The findings contribute to a deeper understanding of the possibilities and challenges associated with virtualization in edge computing environments, laying the foundation for further research in this field.

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Section: Container-based Virtualizationmentioning

confidence: 99%

Optimizing Edge Computing through Virtualization: A Comprehensive Review

Saini

2024

ijarcs

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“…In [20], authors present a framework to analyze energy consumption on Raspberry Pi 3B+. The framework runs benchmarks stressing several components on a node: processor, memory, storage, connectivity, and network usage.…”

Section: Related Workmentioning

confidence: 99%

“…stress-ng is selected because it contains several workloads to stress specific components of a node, independently. It is popularly used in literature [18], [20]. Each stress scenario is selected to stress the node in a specific state or specific component, used in every application: idle, CPU and RAM.…”

Section: B Experimentsmentioning

confidence: 99%

“…Power and energy consumption variability metrics are relatively low, on a single node. In multiple papers [18], [20], idle power or energy is a baseline, deducted from total measurement to do a fine-grain analysis for workloads, without taking variability into account. Furthermore, power is multiplied by time in several works to get total energy consumption [15], multiplying the impact of non considered variability.…”

Section: A Idle Observationsmentioning

confidence: 99%

“…On rpi3-2, equiv∆T month for ∆M dnE(exp) is 3.499 times its value for idle scenario. In literature, energy measurements for CPU workloads, from a node, can be subtracted from total energy measurements of the node, without taking variability into account [20]. For studies aiming at high accuracy and precision, especially fine-grain analysis, understanding CPU component variability, on a single node, is needed.…”

Section: B Cpu Intensive "Matrix Product" Observationsmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying the Variability of Power and Energy Consumption for IoT Edge Nodes

Tofaily,

Raïs,

Anshus

2023

2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)

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For IoT and edge systems, measuring, predicting and optimizing energy consumption is an open field. It is important to accurately and precisely characterize power and energy consumption of edge nodes, as energy can be a scarce and key resource. However, there are no fine-grain studies that aim at understanding the potential variability of power and energy consumption of edge nodes. Existing research works give minor or no significance to this potential variability.This paper addresses this problem by quantifying the variability of power and energy consumption on a single edge node, and among multiple homogeneous edge nodes, for three scenarios: Idle, CPU intensive "matrix product" and RAM intensive "flip". These scenarios are found in edge applications. Identical controlled experiments are repeated thoroughly, for each scenario.Results show that power and energy variability exist for all studied scenarios. On a single node, power and energy variability measurements are relatively low. On multiple homogeneous nodes, the variability can be significant. For example, for CPU intensive "matrix product", the variability in energy is equivalent to an idle up-time, in a month, of 7 hours and 52 hours, when considering single and multiple homogeneous nodes, respectively.

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Measuring and Assessing the Resource and Energy Efficiency of Artificial Intelligence of Things Devices and Algorithms

Guldner

Murach²

2022

Progress in IS

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WattEdge: A Holistic Approach for Empirical Energy Measurements in Edge Computing

Cited by 12 publications

References 22 publications

Optimizing Edge Computing through Virtualization: A Comprehensive Review

Optimizing Edge Computing through Virtualization: A Comprehensive Review

Quantifying the Variability of Power and Energy Consumption for IoT Edge Nodes

Measuring and Assessing the Resource and Energy Efficiency of Artificial Intelligence of Things Devices and Algorithms

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