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Advances in Intelligent Systems and Computing 2018年 542卷 483-492页

作者： Dastagiraiah, C. Krishna Reddy, V. Pandurangarao, K.V. Department of CSE K L University Vijayawada India

ISBN: (纸本)9789811032226

A novel framework to upgrade the execution of adaptable applications and extra battery use. This structure offloads only the concentrated systems. The offloading strategy depends on upon the module asserted component off-loader. This module picks at runtime whether the application’s schedules will run locally on the adaptable or offload to the cloud. Green Cloud processing (GCC) has drawn basic examination thought as the unmistakable quality and capacities of mobile phones have been improved starting late. In this paper, we demonstrate a structure that uses virtualization advancement to apportion server farm assets persistently checking application requests and fortify green enhancing to enlist the measure of servers being used. We demonstrate the likelihood of "skewness" to gage the unevenness in the multi-dimensional asset usage of a server. By minimizing skewness, we can join different sorts of workloads charmingly and enhance the general utilization of server assets. We build up an arrangement of heuristics that imagine over-weight in the structure plausibly while sparing centrality utilized. Take after driven reenactment and examination comes about demonstrate that our calculation accomplishes great execution. © Springer Nature Singapore Pte Ltd. 2018.

关键词： computation offloading

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Software-defined Network Based Resource Allocation in Distributed Servers for Unmanned Aerial Vehicles 8

Software-defined Network Based Resource Allocation in Distri...

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8th IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Shukla, Raj Mani Sengupta, Shamik Patra, Amar Nath Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA

ISBN: (纸本)9781538646496

Unmanned Aerial Vehicles (UAVs) are gaining a lot of popularity among research communities as well as various service providers. The UAVs deployed in mission-critical applications need to perform tasks with very less delay. They also need to conserve their on-board battery power so that their flight time can be increased. To accomplish both of these purposes UAVs may offload their computation and energy intensive jobs to some remote place like cloud servers, edge servers or another UAV node having abundant computing and energy resources. This paper proposes a Software Defined Networking (SDN) architecture to provision hybrid computing resources in an efficient manner. The proposed SDN architecture uses a greedy algorithm to satisfy Quality of Service (QoS) requirements of UAV's applications. The proposed method further leverages a simulated annealing based approach to minimize average latency of the applications and average energy consumption of UAV nodes. Simulation results illustrate a substantial improvement in average latency and energy consumption using proposed mechanism in contrast to processing applications on UAV node itself or offloading it only to the cloud.

关键词： Unmanned aerial vehicles Software defined network computation offloading cloud computing Edge computing

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Harnessing Bandit Online Learning to Low-Latency Fog Computing

Harnessing Bandit Online Learning to Low-Latency Fog Computi...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Tianyi Chen Georgios B. Giannakis Dept. of ECE and DTC University of Minnesota USA

ISBN: (纸本)9781538646595

This paper focuses on the online fog computing tasks in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddle-point (BanSP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret;and, ii) fit that captures the accumulated amount of constraint violations. Specifically, BanSP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests on fog computing tasks corroborate that BanSP offers desired performance under such limited information.

关键词： computation offloading fog computing Internet of Things online learning bandit convex optimization e-learning LOSS FUNCTION Internet of things online operation Regret

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Multi-Objective Resource Allocation for Mobile Edge Computing Systems 28

Multi-Objective Resource Allocation for Mobile Edge Computin...

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28th Annual IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

作者： Zhang, Xinyi Mao, Yuyi Zhang, Jun Letaief, Khaled B. Hong Kong Univ Sci & Technol Dept ECE Hong Kong Hong Kong Peoples R China Hamad Bin Khalifa Univ Doha Qatar

ISBN: (纸本)9781538635315

to enhance the computation capability of mobile devices by offloading computation-demanding tasks to the nearby edge servers. In order to minimize the task latency and the device energy consumption, in this paper, we investigate the multi-objective resource allocation for multi-user MEC systems by adopting the system utility as the performance metric, which is a normalized weighted combination of the time and energy saving achieved by computation offloading. To provide an efficient solution, a low-complexity ranking-based algorithm is proposed based on the modified Newton method and the concept of computation offloading priority. Simulation results show that our proposed algorithm achieves a near-optimal performance and greatly outperforms a baseline algorithm with random spectrum allocation. In addition, it is demonstrated that jointly optimizing the spectrum and computational resource management policy is more critical when the number of MEC users is large.

关键词： Mobile edge computing multi-objective optimization computation offloading modified Newton method

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Towards Context Aware computations offloading in 5G 17

Towards Context Aware Computations Offloading in 5G

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11th European Conference on Software Architecture (ECSA)

作者： Reale, Anna Toth, Melinda Horvath, Zoltan Eotvos Lorand Univ ELTE Budapest Hungary

ISBN: (纸本)9781450352178

The emerging 5G network architecture, together with the concept of edge computing, seems to be the perfect solution to the requirements of Continuous Interactive Applications. All the components of this complex system have been mostly analysed as standalone without considering too deeply their interactions. While the mobile nature of continuous interactive scenarios like real time speech recognition or augmented reality, requires adaptive algorithms and context aware architectures. In this paper we investigate both dynamic and hybrid profiling, and adaptive partitioning, for a demanding augmented reality use case, identifying a possible offloading architecture.

关键词： 5G MEC Context Awareness computation offloading Application Partitioning

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Joint computation and Radio Resource Management for Cellular Networks with Mobile Edge Computing

Joint Computation and Radio Resource Management for Cellular...

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IEEE International Conference on Communications (ICC) / Wireless Communications Symposium / 3rd Workshop on Optical Wireless Communications (OWC)

作者： Wang, Chenmeng Yu, F. Richard Chen, Qianbin Tang, Lun Chongqing Univ Posts & Telecommun CQUPT Chongqing Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

ISBN: (纸本)9781467389990

Mobile edge computing (MEC) has attracted great interests as a promising approach to augment computational capabilities of mobile devices. An important issue in the MEC paradigm is computation offloading. In this paper, we propose an integrated framework for computation offloading and interference management in wireless cellular networks with mobile edge computing. In this integrated framework, the MEC server makes the offloading decision according to the local computation overhead estimated by all user equipments (UEs) and the offloading overhead estimated by the MEC server itself. Then, the MEC server performs the PRB allocation using graph coloring. The outcomes of the offloading decision and PRB allocation are then used to allocate the computation resource of the MEC server to the UEs. Simulation results are presented to show the effectiveness of the proposed scheme with different system parameters.

关键词： Mobile edge computing small cell networks computation offloading interference management resource allocation

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On efficient offloading control in cloud radio access network with mobile edge computing 37

On efficient offloading control in cloud radio access networ...

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37th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Li, Tong Magurawalage, Chathura Sarathchandra Wang, Kezhi Xu, Ke Yang, Kun Wang, Haiyang Tsinghua Univ Beijing Peoples R China Univ Essex Colchester Essex England Univ Minnesota Duluth MN 55812 USA Tsinghua Natl Lab Informat Sci & Technol Beijing Peoples R China Xidian Univ ISN Natl Key Lab Xian Shanxi Peoples R China

ISBN: (纸本)9781538617915

Cloud radio access network (C-RAN) and mobile edge computing (MEC) have emerged as promising candidates for the next generation access network techniques. Unfortunately, although MEC tries to utilize the highly distributed computing resources in close proximity to user equipments equipments (UE), IN C-RANS suggests to centralize the baseband processing units (BBU) deployed in radio access networks. To better understand and address such a conflict, this paper closely investigates the MEC task offloading control in C-RANs environments. In particular, we focus on perspective of matching problem. Our model smartly captures the unique features in both MEC and C-RAN with respect to communication and computation efficiency constraints. We divide the cross-layer optimization into the following three stages: (1) matching between remote radio heads (RRH) and UEs, (2) matching between BBUs and UEs, and (3) matching between mobile clones (MC) and UEs. By applying the Gale-Shapley Matching Theory in the duplex matching framework, we propose a multi-stage heuristic to minimize the refusal rate for user's task offloading requests. Trace-based simulation confirms that our solution can successfully achieve near-optimal performance in such a hybrid deployment.

关键词： computation offloading Cloud Radio Access Network Mobile Edge Computing offloading Control

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Work-in-Progress: Snapshot-based offloading for Machine Learning Web App 13

Work-in-Progress: Snapshot-based Offloading for Machine Lear...

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International Conference on Embedded Software (EMSOFT)

作者： Jeong, InChang Jeong, Hyuk-Jin Moon, Soo-Mook Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9781450351867

We propose a new approach to running machine learning (ML) web app on resource-constrained embedded devices by offloading ML computations to servers. We can dynamically offload computations depending on the problem size and network status. The execution state is saved in the form of another web app called snapshot which simplifies the state migration. Some issues related to ML such as how to handle the Canvas object, the ML model, and the privacy of user data are addressed. The proposed offloading works for real web apps with a performance comparable to running the app entirely on the server.

关键词： computation offloading machine learning web application

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Energy - Service Trade-Off Model for Mobile Cloud Computing 14

Energy - Service Trade-Off Model for Mobile Cloud Computing

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14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

作者： Ravi, Anuradha Peddoju, Sateesh K. Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)9781538623237

The advent of Cloud has aided mobile devices in performing computation intensive tasks with the virtue of offloading. However, this leads to communication with the Cloud, which results in high energy consumption. Moreover, communication over wireless medium has the risk of intermittent connectivity with Cloud. Hence, there is an urgent need for providing a trade-off between energy consumption and service availability ill Mobile Cloud Computing. This paper model the trade-off problem as a multi-criteria decision making optimization problem, considering different parameters such as energy consumption, waiting time, risk for offloading and deadline to compute a task. The proposed model takes intelligent offloading decisions to avail service from a remote resource such as Cloud. The validity of decision is tested with different applications and proved that the model conserves energy and also prolongs the service connection for mobile devices.

关键词： Mobile Cloud Computing Wireless Networks computation offloading Energy Efficiency Seamless Service

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Using a multi criteria decision making model for managing computational resources at mobile ad-hoc cloud computing environment

Using a multi criteria decision making model for managing co...

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International Conference on Engineering and Technology (ICET)

作者： Azar, Hossein Majma, Mohammad Reza Islamic Azad Univ Pardis Branch Dept Comp Pardis Iran

ISBN: (纸本)9781538619490

Mobile devices have become one important part of our life. These devices has changed our life style by delivering various applications. These applications impacted our life and they are becoming more advanced, day by day. New applications requires more computation compared to a few years ago. By these definitions, mobile cloud computing (MCC) is evolved to help these devices known as resource poor devices. MCC helped to offload mobile devices computations to nearby cloud service provider in order to provide a high quality services to mobile users. Cyber foraging is state of art that uses MCC fundamentals to create a local cloud for local devices for computation offloading using other devices. In this article we will define criteria's needed to consider for choosing nodes suitable for computation offloading, using a multi criteria decision making algorithm known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Through simulation we will compare the results with different scenarios and compare TOPSIS results in such environment.

关键词： Mobile ad-hoc cloud computing multi criteria decision making (MCDM) TOPSIS computation offloading

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