RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments

Rehman, Muhammad Habib ur; Jayaraman, Prem Prakash; Malik, Saif Ur Rehman; Khan, Atta ur Rehman; Gaber, Mohamed Medhat

doi:10.3390/jsan6030017

Cited by 43 publications

(24 citation statements)

References 49 publications

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“…c. Petri Net-based Techniques: In contrast to tree-based techniques, recent research highlights the use of High Level Petri Net (HLPN) referred to as RedEdge for modeling aggregation in edge-based systems [17]. Given that fog/edge computing accounts for three layers, namely the cloud, the user device, and the edge layers, techniques that support heterogeneity are required.…”

Section: A Data Flowmentioning

confidence: 99%

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Resource Management in Fog/Edge Computing

2019

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Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013-2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing.

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Section: A Data Flowmentioning

confidence: 99%

“…The algorithm achieves lower bounds of latency while maintaining constant energy. In a mobile device-based edge computing framework, RedEdge, it was observed that the energy consumption for data transfer was minimized [17]. However, there is a data processing overhead on the edge node.…”

Section: A Data Flowmentioning

confidence: 99%

Resource Management in Fog/Edge Computing

2019

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“…Discovering mobile edge devices by scanning all connected communication interfaces and enlisting all locally available mobile edge devices is a first common approach [70]. An example of this prevalent approach is Foggy [170], that relies on a centralised orchestration server and a container registry for deployment.…”

Section: Churn and Unreliabilitymentioning

confidence: 99%

“…These have simplified infrastructure configuration for data centre servers, storage, as well as core and edge networks. As a result, the infrastructure is able to [127,81,17] [ 42,140,153,100,164,163,132,131,116,170,70,156,44,95,79,107] [ 106,53,46,166,128,64] adapt to the needs of the services that run on it, making the interplay between the services and the infrastructure more dynamic and complex.…”

Section: Introductionmentioning

confidence: 99%

Research challenges in nextgen service orchestration

Vaquero

Cuadrado

Elkhatib

et al. 2019

Future Generation Computer Systems

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Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world.

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“…Due to powerful computing capability compared with edge devices, cloud computing puts all the computing tasks on the cloud to process data efficiently and provides highly vitalized storage and computing services on top of massively parallel distributed systems [1][2][3]. However, with the growing quantity of data generated at the edge, the speed is becoming the bottleneck for the cloud-based computing paradigm [4].…”

Section: Introductionmentioning

confidence: 99%

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

Song

Duan

Wan

et al. 2018

Wireless Communications and Mobile Computing

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Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless-network-based threetier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.

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RedEdge: A Novel Architecture for Big Data Processing in Mobile Edge Computing Environments

Cited by 43 publications

References 49 publications

Resource Management in Fog/Edge Computing

Resource Management in Fog/Edge Computing

Research challenges in nextgen service orchestration

Processing Optimization of Typed Resources with Synchronized Storage and Computation Adaptation in Fog Computing

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