Book Chapter10.1007/978-3-319-32903-1_275-1
Edge Computing for Real-Time Video Stream Analytics
Miao Hu,Lei Zhuang,Di Wu,Zhichuang Huang,Han Hu +4 more
- 01 Jan 2019
- pp 1-5
About: The article was published on 01 Jan 2019. The article focuses on the topics: Edge computing.
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Citations
A Black-Box Fork-Join Latency Prediction Model for Data-Intensive Applications
TL;DR: A black-box Fork-Join model is proposed that covers a wide range of Fork- join structures for the prediction of tail and mean latency, called ForkTail and ForkMean, respectively, and can be used as a powerful tool to aid the design of tail-and-mean-latency guaranteed job scheduling and resource provisioning, especially at high load, for datacenter applications.
25
Heterogeneous Edge Offloading With Incomplete Information: A Minority Game Approach
TL;DR: This article formulate the problem of heterogeneous task offloading in a distributed environment as a minority game (MG) and proposes an MG based scheme, in which tasks are divided into subtasks and instructed to form into a set of groups as possible, and the left ones are scheduled to perform decision adjustment in a probabilistic manner.
References
Edge Computing: Vision and Challenges
TL;DR: The definition of edge computing is introduced, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge Computing.
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The Case for VM-Based Cloudlets in Mobile Computing
TL;DR: The results from a proof-of-concept prototype suggest that VM technology can indeed help meet the need for rapid customization of infrastructure for diverse applications, and this article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them.
MAUI: making smartphones last longer with code offload
Eduardo Cuervo,Aruna Balasubramanian,Dae-Ki Cho,Alec Wolman,Stefan Saroiu,Ranveer Chandra,Paramvir Bahl +6 more
- 15 Jun 2010
TL;DR: MAUI supports fine-grained code offload to maximize energy savings with minimal burden on the programmer, and decides at run-time which methods should be remotely executed, driven by an optimization engine that achieves the best energy savings possible under the mobile device's current connectivity constrains.
ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading
Sokol Kosta,Andrius Aucinas,Pan Hui,Richard Mortier,Xinwen Zhang +4 more
- 25 Mar 2012
TL;DR: This paper proposes ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud and enhances the power of mobile cloud computing by parallelizing method execution using multiple virtual machine (VM) images.
A survey on mobile edge computing
Arif Ahmed,Ejaz Ahmed +1 more
- 01 Jan 2016
TL;DR: In this paper, some of the promising real time Mobile Edge Computing application scenarios are discussed and a state-of-the-art research efforts on Mobile Edge computing domain is presented.
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