Energy-aware fog and cloud interplay supported by wide area software defined networking

Boryło, Piotr; Lasoń, Artur; Rza̧sa, Jacek; Szymanski, Andrzej; Jajszczyk, Andrzej

doi:10.1109/icc.2016.7511451

Cited by 41 publications

(23 citation statements)

References 11 publications

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“…The results show that the proposed scheme has lower energy consumption that computing without offloading especially with large number of mobile devices. In [52], the authors investigate the energy aware interaction between fog and cloud. It is demonstrated that the overall energy consumption of data centers can be reduced without a significant deterioration of the network performance.…”

Section: A Objectivesmentioning

confidence: 99%

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

et al. 2017

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As the explosive growth of smart devices and the advent of many new applications, traffic volume has been growing exponentially. The traditional centralized network architecture cannot accommodate such user demands due to heavy burden on the backhaul links and long latency. Therefore, new architectures which bring network functions and contents to the network edge are proposed, i.e., mobile edge computing and caching. Mobile edge networks provide cloud computing and caching capabilities at the edge of cellular networks. In this survey, we make an exhaustive review on the state-of-the-art research efforts on mobile edge networks. We first give an overview of mobile edge networks including definition, architecture and advantages. Next, a comprehensive survey of issues on computing, caching and communication techniques at the network edge is presented respectively. The applications and use cases of mobile edge networks are discussed. Subsequently, the key enablers of mobile edge networks such as cloud technology, SDN/NFV and smart devices are discussed. Finally, open research challenges and future directions are presented as well.Index Terms-Mobile edge computing, mobile edge caching, D2D, SDN, NFV, content delivery, computational offloading.

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Section: A Objectivesmentioning

confidence: 99%

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

et al. 2017

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“…Among the multi-levels works, the most common is to use resources located both at the edge and at the cloud level. This is the case in the works by Liu et al [58], Borylo et al [65], Valancius et al [45], Yi et al [76], Wang et al [77], and Singh et al [44]. Specifically, Skarlat et al [50] and Bittencourt et al [72] favor using edge resources over cloud resources.…”

Section: Multi-levelmentioning

confidence: 95%

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Toczé

Nadjm‐Tehrani

2018

Wireless Communications and Mobile Computing

101

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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices at the edge of the current network. To achieve higher performance in this new paradigm, one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis are used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource and less extensive towards the estimation, discovery, and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of nonfunctional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.

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“…Moreover, some works focus solely on one resource type. Energy is one of them and Borylo et al [71] classify datacenters in two categories (green and brown depending on which source of energy they use) before using a latency-aware policy to choose a data center for serving a request. Another one is computation.…”

Section: Single Resource Focusmentioning

confidence: 99%

“…Ahvar [67] Aït-Salaht [85] Bittencourt [78] Borgia [86] Borylo [71] Boubin [87] Chen [81] De Maio [88] Elgazar [83] Fahs [89] Fricker [72] Gu [80] Habak [20] Liu [77] Liu [50] Mahmud [90] Penner [52] Rodrigues [73] Sardellitti [82] Singh [40] Skarlat [46] Sonmez [91] Tang [79] Tärneberg [75] Wang [45] Wang [70] Wang [92] Wang [93] Xia [94] Yi [76] Zamani [74] 3.4. Objective user.…”

Section: Objective Estimation Discovery Allocation Sharing Optimizationmentioning

confidence: 99%

Latency-aware Resource Management at the Edge

Toczé

2020

Linköping Studies in Science and Technology. Licentiate Thesis

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Energy-aware fog and cloud interplay supported by wide area software defined networking

Cited by 41 publications

References 11 publications

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Latency-aware Resource Management at the Edge

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