Topic
Edge computing
About: Edge computing is a research topic. Over the lifetime, 11657 publications have been published within this topic receiving 148533 citations.
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TL;DR: Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.
Abstract: Edge computing (EC) has recently emerged as a promising paradigm that supports resource-hungry Internet of Things (IoT) applications with low latency services at the network edge. However, the limited capacity of computing resources at the edge server poses great challenges for scheduling application tasks. In this paper, a task scheduling problem is studied in the EC scenario, and multiple tasks are scheduled to virtual machines (VMs) configured at the edge server by maximizing the long-term task satisfaction degree (LTSD). The problem is formulated as a Markov decision process (MDP) for which the state, action, state transition, and reward are designed. We leverage deep reinforcement learning (DRL) to solve both time scheduling (i.e., the task execution order) and resource allocation (i.e., which VM the task is assigned to), considering the diversity of the tasks and the heterogeneity of available resources. A policy-based REINFORCE algorithm is proposed for the task scheduling problem, and a fully-connected neural network (FCN) is utilized to extract the features. Simulation results show that the proposed DRL-based task scheduling algorithm outperforms the existing methods in the literature in terms of the average task satisfaction degree and success ratio.
67 citations
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TL;DR: This paper proposes a smart-toy-edge-computing-oriented data exchange prototype using Hyperledger Fabric v1.0 to solve the issue of automatically maintaining a tamper-resistant, reliable and distributed ledger by writing smart contract in the environment where the participants are distrustful of each other.
67 citations
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01 Nov 2017TL;DR: In this article, a low-resource machine learning on fog devices kept close to wearables for smart telehealth was proposed for analyzing pathological speech data obtained from smart watches worn by patients with Parkinson's disease.
Abstract: The increasing use of wearables in smart telehealth system led to the generation of large medical big data. Cloud and fog services leverage these data for assisting clinical procedures. IoT Healthcare has been benefited from this large pool of generated data. This paper suggests the use of low-resource machine learning on Fog devices kept close to wearables for smart telehealth. For traditional telecare systems, the signal processing and machine learning modules are deployed in the cloud that processes physiological data. This paper presents a Fog architecture that relied on unsupervised machine learning big data analysis for discovering patterns in physiological data. We developed a prototype using Intel Edison and Raspberry Pi that was tested on real-world pathological speech data from telemonitoring of patients with Parkinson's disease (PD). Proposed architecture employed machine learning for analysis of pathological speech data obtained from smart watches worn by the patients with PD. Results show that proposed architecture is promising for low-resource machine learning. It could be useful for other applications within wearable IoT for smart telehealth scenarios by translating machine learning approaches from the cloud backend to edge computing devices such as Fog.
67 citations
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TL;DR: This paper proposes a novel management architecture for 5G service based core network based on NFV and SDN, which can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration ofnetwork functions and optimal workload allocation.
Abstract: The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5G era, service based architecture is introduced into mobile networks. The monolithic network elements (e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5G core network are still big challenges. In this paper, we propose a novel management architecture for 5G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.
67 citations
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TL;DR: In this paper, a distributed computation offloading strategy based on Deep Q-learning Network (DQN) is proposed to find the best offloading method to minimize the execution time of a compound task.
67 citations