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Reducing your local footprint with anyrun computing

COMPUTER COMMUNICATIONS 2016年 81卷 1-11页

作者： Ferrari, Alan Giordano, Silvia Puccinelli, Daniele Univ Appl Sci Southern Switzerland SUPS Inst Informat Syst & Networking Lugano Switzerland

computational offloading is the standard approach to running computationally intensive tasks on resource-limited smart devices, while reducing the local footprint, i.e., the local resource consumption. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy. Strategies that rely on local computing power have been proposed that enable fine-grained energy-aware code offloading from a mobile device to a nearby piece of infrastructure. Even state-of-the-art cloud-free solutions are centralized and suffer from a lack of flexibility, because computational offloading is tied to the presence of a specific piece of computing infrastructure. We propose AnyRun Computing (ARC), a system to dynamically select the most adequate piece of local computing infrastructure. With ARC, code can run anywhere and be offloaded not only to nearby dedicated devices, as in existing approaches, but also to peer devices. We present a detailed system description and a thorough evaluation of ARC under a wide variety of conditions. We show that ARC matches the performance of the state-of-the-art solution (MAUI), in reducing the local footprint with stationary network topology conditions and outperforms it by up to one order of magnitude under more realistic topological conditions. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Code offloading computation offloading Opportunistic computing Bayesian network Software profiling

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A Study to Optimize Heterogeneous Resources for Open IoT

arXiv

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arXiv 2017年

作者： Yamato, Yoji Hoshikawa, Naoto Noguchi, Hirofumi Demizu, Tatsuya Kataoka, Misao NTT Network Service Systems Laboratories NTT Corporation Musashino-shi Tokyo180-8585 Japan

Recently, IoT technologies have been progressed, and many sensors and actuators are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. We also implemented elemental technologies of Tacit Computing. In this paper, we propose three layers optimizations to reduce operation cost and improve performance of Tacit computing service, in order to make as a continuous service of discovered devices by Tacit Computing. In optimization process, appropriate function allocation or offloading specific functions are calculated on device, network and cloud layer before full-scale operation. Copyright © 2017, The Authors. All rights reserved.

关键词： computation offloading

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An offloading Decision Scheme for a Multi-Drone System

An Offloading Decision Scheme for a Multi-Drone System

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作者： Kwangsoo Jo Junhyuk An Jinman Jung Hong Min Division of Computer and Information Engineering Hoseo University Department of Information and Communication Engineering Hannam University

In the case of a single drone system, a drone should commit its task to the cloud to reduce task completion time and energy consumption. However, a multi-drone system, where several drones are connected to each other, can divide a task into small tasks and assign each small task to drones to improve responsibility. In this paper, we propose an offloading decision scheme that considers task completion time and energy consumption. The proposed scheme compares the cost of executing small tasks on the drones with the cost of committing a task to the cloud and decides offloading a task only if the cost of offloading is definitely smaller than the cost of using multiple drones. Our simulation results show that the proposed decision scheme is necessary because offloading spends more energy and time in some cases.

关键词： computation offloading multi-drone systems cloud computing task completion time energy consumption

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ENABLING EFFECTIVE MOBILE EDGE COMPUTING USING MILLIMETER WAVE LINKS

ENABLING EFFECTIVE MOBILE EDGE COMPUTING USING MILLIMETER WA...

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International Conference on Communications Workshops

作者： Sergio Barbarossa Elena Ceci Mattia Merluzzi Emilio Calvanese-Strinati Sapienza University of Rome DIET Via Eudossiana 18 00184 Italy CEA LETI Minatec Campus 17 rue des Martyrs 38054 Grenoble France

ISBN: (纸本)9781509015269

Mobile Edge Computing (MEC) plays a key role in the 5G roadmap, as a way to bring information technology (IT) services closer to the mobile users by empowering radio access points with additional functionalities, like caching or computation offloading capabilities. At the physical layer, some of the key technologies enabling very low latency mobile services are dense deployment of radio access points, massive MIMO and millimeter-wave (mmW) communications for radio access as well as for radio fronthaul/backhaul. In this paper, we merge computation offloading techniques for mobile edge computing with mmW communications and show how the joint optimization of computation/communication resources is crucial to design an energy efficient mobile edge computing system. In particular, we tackle the intermittency of mmW links by considering multi-link communications and show how to take advantage of preliminary estimation of blocking probabilities.

关键词： Mobile edge computing millimeter-wave communications computation offloading

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A Time-Adaptive Heuristic for Cognitive Cloud offloading in Multi-RAT Enabled Wireless Devices

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IEEE Transactions on Cognitive Communications and Networking 2016年第2期2卷 194-207页

作者： Mahmoodi, S. Eman Subbalakshmi, K. P. Suba Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States

We introduce the concept of cognitive cloud offloading where all viable wireless interfaces of a multiple radio enabled device are used for computation offloading. We propose a time and wireless adaptive heuristic for offloading computationally intensive applications to a remote cloud with goals of reducing the energy consumption on the mobile device, execution time of the application, and efficient use of the multiple radio interfaces available at the device. The proposed algorithms simultaneously determine: 1) execution place of each application component (mobile/cloud);2) amount of the associated data to be sent via each available interface of the multiple radio access technology device;and 3) scheduling order of the application components. We define a net utility function that trades off mobile device resources (battery, CPU, and memory) with realtime communication costs, such as latency and communication energy, subject to constraints that ensure queue stability of radio interfaces. Simulations using real data from an HTC smartphone running multi-component applications with Amazon EC2 as the cloud, and two radios, LTE and WiFi, show that cognitive cloud offloading provides higher net utility in comparison to the best-interface protocol. Scalability of the proposed heuristic is further analyzed using various levels for component dependency graphs and energy-delay trade-off factors. © 2015 IEEE.

关键词： computation offloading

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Multi-agent and Reinforcement Learning Based Code offloading in Mobile Fog 30

Multi-agent and Reinforcement Learning Based Code Offloading...

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30th International Conference on Information Networking (ICOIN)

作者： Alam, Md. Golam Rabiul Tun, Yan Kyaw Hong, Choong Seon Kyung Hee Univ Dept Comp Sci & Engn Yongin South Korea

ISBN: (纸本)9781509017249

Fog computing, which performs on network edges, is a front-end distributed computing archetype of centralized cloud computing. Mobile Fog is a special purpose computing prototype, which leverages the mobile computing to deliver seamless and latency-aware mobile services. offloading computation in mobile Fog is challenging because of the spatiotemporal resource requirements of heterogeneous mobile devices. In this paper, we propose reinforcement learning based code offloading mechanism to ensure low-latency service delivery towards mobile service consumers. We use the distributed reinforcement learning algorithm to offload basic blocks in a decentralized fashion to deploy mobile codes on geographically distributed mobile Fogs. We simulate the proposed prototype using OMNeT++ considering fluctuated resources of mobile Fog and varied service demands of mobile users. The proposed method significantly reduces the execution time and latency of accessing mobile services while ensuring lower energy consumption of mobile devices.

关键词： Fog computing mobile computing computation offloading reinforcement learning

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A Survey on Mobile Edge Computing 10

A Survey on Mobile Edge Computing

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10th International Conference on Intelligent Systems and Control (ISCO)

作者： Ahmed, Arif Ahmed, Ejaz Natl Inst Technol Dept Comp Sci & Engn Silchar Silchar India Univ Malaya Dept Comp Syst & Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781467378079

Mobile Edge Computing is an emerging technology that provides cloud and IT services within the close proximity of mobile subscribers. Traditional telecom network operators perform traffic control flow (forwarding and filtering of packets), but in Mobile Edge Computing, cloud servers are also deployed in each base station. Therefore, network operator has a great responsibility in serving mobile subscribers. Mobile Edge Computing platform reduces network latency by enabling computation and storage capacity at the edge network. It also enables application developers and content providers to serve context-aware services (such as collaborative computing) by using real time radio access network information. Mobile and Internet of Things devices perform computation offloading for compute intensive applications, such as image processing, mobile gaming, to leverage the Mobile Edge Computing services. In this paper, some of the promising real time Mobile Edge Computing application scenarios are discussed. Later on, a state-of-the-art research efforts on Mobile Edge Computing domain is presented. The paper also presents taxonomy of Mobile Edge Computing, describing key attributes. Finally, open research challenges in successful deployment of Mobile Edge Computing are identified and discussed.

关键词： Mobile Edge Computing computation offloading Collaborative Computing

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Distributed QoS Management for Internet of Things under Resource Constraints 16

Distributed QoS Management for Internet of Things under Reso...

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International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS)

作者： Samie, Farzad Tsoutsouras, Vasileios Xydis, Sotirios Bauer, Lars Soudris, Dimitrios Henkel, Joerg Karlsruhe Inst Technol CES D-76021 Karlsruhe Germany Natl Tech Univ Athens ECE Microprocessors & Digital Syst Lab GR-10682 Athens Greece

ISBN: (纸本)9781450344838

Internet-of-Things (IoT) envisions an infrastructure of ubiquitous networked smart devices offering advanced monitoring and control services. Current art in IoT architectures utilizes gateways to enable application-specific connectivity to IoT devices. In typical configurations, an IoT gateway is shared among several IoT devices. However, given the limited available bandwidth and processing capabilities of an IoT gateway, the quality of service (QoS) of IoT devices must be adjusted over time not only to fulfill the needs of individual IoT device users, but also to tolerate the QoS needs of the other IoT devices sharing the same gateway. In this paper, we address the problem of QoS management for IoT devices under bandwidth, battery, and processing constraints. We first formulate the problem of resourceaware QoS tailored to the IoT paradigm and then propose an efficient problem decomposition that enables the adoption of a recurrent dynamic programming approach with reduced execution time overhead. We evaluate the efficiency of the proposed approach with a case study and through extensive experimentation over different IoT system configurations regarding to the number and type of the employed IoTdevices. Experiments show that our solution improves the overall QoS by 50% compared to an unsupervised system while both meet the constraints.

关键词： Internet of Things IoT Resource Management computation offloading ECG

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Code offloading using Support Vector Machine 6

Code Offloading using Support Vector Machine

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6th International Conference on Innovative Computing Technology (INTECH)

作者： Majeed, Ayesha Abdul Khan, Atta Ur Rehman UlAmin, Riaz Muhammad, Jan Ayub, Sara Balochistan Univ Informat Technol & Management Sc Quetta Pakistan King Saud Univ Riyadh Saudi Arabia King Saud Univ Riyadh Saudi Arabia

ISBN: (纸本)9781509020003

Due to enormous growth in mobile device technology, user's preferences have been shifted from traditional mobile phones and laptops to the other handheld devices such as smartphones. Significant efforts have been made to make smartphones rich in terms of processing capabilities and reduction on energy consumption. Despite the improvements in provision of computational, memory and energy resources with smart phones, Smart phones are still characterized as resource constrained devices. It is believed that increasing resource capabilities in smart phones cannot handle exponential increase in smart phone applications and the resultant network traffic. Cloud computing has emerged as viable solution to address the user's increasing resource requirements. To achieve computational efficiency in terms of speed, recent researches have recommended that programming codes that require intensive computational resourcescan be offloaded to the cloud servers. However, the accuracy of decision to offload code to cloud server can largely impact the performance of the overall system. In this paper, we propose an accurate decision making system for adaptive and dynamic nature of mobile systems by using Support Vector machine learning technique for making offloading decision locally or remotely. Proposed system is evaluated with Androidbased prototype component for experiments considering different internal and external conditions (network characteristics). Our proposed system achieves approximately 92% accuracy, leading to accurate decision, thus improving performance and reducing energy consumption.

关键词： Adaptive Scheduler computation offloading Context-Awareness Machine Learning

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A Hybrid Framework using Markov Decision Process for Mobile Code offloading 19

A Hybrid Framework using Markov Decision Process for Mobile ...

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19th International Conference on Computer and Information Technology (ICCIT)

作者： Zannat, Hafsa Hossain, Md. Shohrab Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (纸本)9781509040902

With the emergence of a promising technology, Cloud Computing is spreading its usage everywhere. Mobile Cloud Computing (MCC) integrates cloud computing with mobile devices which holds a promise to run powerful and resource-intensive applications on mobile devices irrespective of its performance limitations (e.g., battery life, memory utilization and computation). Overcoming of these limitations is realized through a prominent solution, computation offloading: bypassing heavy computation to resourceful servers and receiving the outcome from these servers. A lot of research works have been done for mobile code offloading. Most of the frameworks either requires full virtual machine migration which results in coarse-grained and costly solutions, or implement fine-grained offloading requiring costly synchronization between local and offloaded codes. In both approaches, offloading of native methods are restricted only to local execution. In this paper, we have proposed a conceptual models blending the positive outcomes of both approaches using Markov Decision Process (MDP) for decision making and provides conceptual analysis with related research works.

关键词： Mobile cloud computing computation offloading Markov Decision Process (MDP) Cloud Computing Mobile Device

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