283 research outputs found
Allocation of Computing and Communication Resources for Mobile Edge Computing with Parallel Processing
Mobilní sítě páté generace (5G) přináší množství nových užití a aplikací s přísnými požadavky na latence. "Mobile Edge Computing" (MEC) jakožto nový koncept, který podporuje přenos výpočetně náročných úloh na okraj mobilní sítě, je považován za řešení pro snížení latencí. Paralelní zpracování úloh v MEC systému má za úkol dále snížit celkový čas výpočtu. Přestože problému paralelního zpracování v MEC systémech se dostalo mezi vědci mnoho pozornosti, existující řešení se zaměřují na scénáře s jedním uživatelem, případně na dělení výpočetních prostředků na samotném okraji mobilní sítě. Tato diplomová práce předpokládá systém s více uživateli, kteří sekvenčně odesílají rozdělené úlohy přímo na klastr vybraných základnových stanic s výpočetními prostředky. Je navržen algoritmus pro optimální dělení úloh a alokaci prostředků. Efektivita navrženého algoritmu je pomocí simulací porována s existujícími řešeními. Navržený algoritmus snižuje celkový čas výpočtu až o 48% při porovnání s další metodou využívající paralelního zpracování a až o 78% ve srovnání s metodou bez paralelního zpracování.In the fifth generation (5G) mobile networks, new use cases and applications with strict requirements for latency emerge. Mobile Edge Computing (MEC) is a novel concept, which supports the offloading of computationally demanding tasks to the edge of the mobile network, and is considered a promising solution to reduce the latencies. The parallel processing of the task in the MEC system aims to further minimize the task's completion delay. Although the problem of parallel processing in the MEC has received attention among researchers, the existing works either assume a single-user scenarios, or focus on partitioning of the computation resources at the edge. In this thesis, a multi-user scenario is considered, with users offloading the partitioned tasks sequentially to the selected clusters of computing eNBs. An algorithm is proposed for the optimal task partitioning and resource allocation. The efficiency of the proposed algorithm is then simulated and compared to other existing approaches. The proposed algorithm decreases the task completion delay by up to 48% when compared to another method exploiting parallel processing and by up to 78% in comparison with a non-partitioning methods
Divide-and-Conquer Distributed Learning: Privacy-Preserving Offloading of Neural Network Computations
Machine learning has become a highly utilized technology to perform decision making on high dimensional data. As dataset sizes have become increasingly large so too have the neural networks to learn the complex patterns hidden within. This expansion has continued to the degree that it may be infeasible to train a model from a singular device due to computational or memory limitations of underlying hardware. Purpose built computing clusters for training large models are commonplace while access to networks of heterogeneous devices is still typically more accessible. In addition, with the rise of 5G networks, computation at the edge becoming more commonplace, and inspired by the successes of the folding@home project utilizing crowdsourced computation, we consider the scenario of the crowdsourcing the computation required for training of a neural network particularly appealing. Distributed learning promises to bridge the widening gap between singular device performance and large-scale model computational requirements, but unfortunately, current distributed learning techniques do not maintain privacy of both the model and input with- out an accuracy or computational tradeoff. In response, we present Divide and Conquer Learning (DCL), an innovative approach that enables quantifiable privacy guarantees while offloading the computational burden of training to a network of devices. A user can divide the training computation of its neural network into neuron-sized computation tasks and dis- tribute them to devices based on their available resources. The results will be returned to the user and aggregated in an iterative process to obtain the final neural network model. To protect the privacy of the user’s data and model, shuffling is done to both the data and the neural network model before the computation task is distributed to devices. Our strict adherence to the order of operations allows a user to verify the correctness of performed computations through assigning a task to multiple devices and cross-validating their results. This can protect against network churns and detect faulty or misbehaving devices
Deep Learning for Edge Computing Applications: A State-of-the-Art Survey
With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field
SoK: Distributed Computing in ICN
Information-Centric Networking (ICN), with its data-oriented operation and
generally more powerful forwarding layer, provides an attractive platform for
distributed computing. This paper provides a systematic overview and
categorization of different distributed computing approaches in ICN
encompassing fundamental design principles, frameworks and orchestration,
protocols, enablers, and applications. We discuss current pain points in legacy
distributed computing, attractive ICN features, and how different systems use
them. This paper also provides a discussion of potential future work for
distributed computing in ICN.Comment: 10 pages, 3 figures, 1 table. Accepted by ACM ICN 202
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobile
applications, uniformly sustaining high-performance inference on mobile has
been elusive due to the excessive computational demands of modern CNNs and the
increasing diversity of deployed devices. A popular alternative comprises
offloading CNN processing to powerful cloud-based servers. Nevertheless, by
relying on the cloud to produce outputs, emerging mission-critical and
high-mobility applications, such as drone obstacle avoidance or interactive
applications, can suffer from the dynamic connectivity conditions and the
uncertain availability of the cloud. In this paper, we propose SPINN, a
distributed inference system that employs synergistic device-cloud computation
together with a progressive inference method to deliver fast and robust CNN
inference across diverse settings. The proposed system introduces a novel
scheduler that co-optimises the early-exit policy and the CNN splitting at run
time, in order to adapt to dynamic conditions and meet user-defined
service-level requirements. Quantitative evaluation illustrates that SPINN
outperforms its state-of-the-art collaborative inference counterparts by up to
2x in achieved throughput under varying network conditions, reduces the server
cost by up to 6.8x and improves accuracy by 20.7% under latency constraints,
while providing robust operation under uncertain connectivity conditions and
significant energy savings compared to cloud-centric execution.Comment: Accepted at the 26th Annual International Conference on Mobile
Computing and Networking (MobiCom), 202
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
Edge Intelligence : Empowering Intelligence to the Edge of Network
Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe
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