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Multi-Resource Fair Allocation for Composited Services in Edge Micro-Clouds

Multi-Resource Fair Allocation for Composited Services in Ed...

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IEEE International conference on Big Data and Cloud computing (BdCloud)

作者： Tongyu Guo Haitao Zhang Han Huang Jianli Guo Chenze He Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Science and Technology on Communication Networks Laboratory Shijiazhuang China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Edge micro-clouds can collaboratively provide computing ability to users nearby in the dynamic environments, such as disaster relief and battlefield. Generally, the applications deployed on edge micro-clouds platform may consist of a set of composited services, and different services require different amounts of multiple dimensional resources. So massive service deployment can lead to unbalanced resource utilization on the micro-clouds. On the other hand, the composited services can be placed in different nodes, and even different edge micro-clouds, and the Quality of Service (QoS) of applications cannot be guaranteed. In this paper, we first define two models of edge micro-clouds and composited services to measure the resource utilization balance degree and QoS constrains respectively. Based on the two models, we formulate the problem of maximizing the edge micro-clouds platform resource utilization balance degree while satisfying the application delay constraints. Subsequently, we propose a Deep Reinforcement Learning (DRL) based multi-resource fair allocation algorithm to obtain the resource allocation strategy by making online decision. In our algorithm, we design a shaped reward function to jointly consider the factors of resource utilization balance degree and application delay constrains, and an improved DRL model is used to improve the convergence performance and the accuracy of the obtained strategy. The experimental evaluation demonstrates the proposed algorithm can guarantee the QoS of applications while achieving a better resource utilization balance degree compared with the other representative algorithms.

关键词： Resource management Delays Quality of service Reinforcement learning Cloud computing Heuristic algorithms Servers

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Multiversion Concurrency Control on Intermittent Systems

Multiversion Concurrency Control on Intermittent Systems

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IEEE International conference on Computer-Aided Design

作者： Wei-Ming Chen Yi-Ting Chen Pi-Cheng Hsiu Tei-Wei Kuo Research Center for Information Technology Innovation Academia Sinica Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taiwan National Chi Nan University Taiwan College of Engineering City University of Hong Kong Hong Kong

Concurrency control allows multiple tasks that share data objects to be concurrently executed in a serializable order, thus significantly improving computation progress. However, to accumulate forward progress on energy-harvesting intermittent systems while achieving data consistency across power cycles, existing approaches based on the checkpointing paradigm typically require system suspension at runtime. The runtime overheads incurred by suspension will be more manifest when more tasks are suspended and resumed during checkpointing, offsetting the computation progress improved by concurrent task execution. This paper presents a multiversion concurrency control design, which enables concurrent task execution without system suspension during checkpointing, while maintaining the serializability of task execution and ensuring data consistency after system recovery. We integrated our design into FreeRTOS running on a Texas Instruments device. Experimental results show that, at the very best, our design can double computation progress by reducing the runtime overheads incurred by system checkpointing, especially when tasks are executed with high concurrency.

关键词： Task analysis Checkpointing Runtime Nonvolatile memory Concurrency control Concurrent computing Operating systems

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How Meta-heuristic Algorithms Contribute to Deep Learning in the Hype of Big Data Analytics 4th

How Meta-heuristic Algorithms Contribute to Deep Learning in...

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4th International conference on Advanced computing, networking and Informatics (ICACNI)

作者： Fong, Simon Deb, Suash Yang, Xin-she Univ Macau Dept Comp Informat Sci Macau Peoples R China INNS India Reg Chapter 7th Floor Jorar Ranchi 834010 Jharkhand India Middlesex Univ Sch Sci & Technol London NW4 4BT England

ISBN: (纸本)9789811033735;9789811033728

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.

关键词： Deep learning Meta-heuristic algorithm Neural network training Nature-inspired computing algorithms Algorithm design

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A Flexible Network and Compute-Aware Orchestrator to Enhance QoS in NFV-Based multimedia Services

A Flexible Network and Compute-Aware Orchestrator to Enhance...

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International conference on Advanced Information networking and Applications (AINA)

作者： Rodrigo Moreira Flávio Silva Pedro Frosi Rui Aguiar Universidade Federal de Uberlandia Uberlandia MG BR Universidade de Aveiro Aveiro Aveiro PT

People and organizations around the globe are using multimedia applications to communicate. By evolving their networks, network operators are taking advantage of the convergence of voice and data. QoS metrics are usually based on parameters from the user side or network side. In this context, NFV and SDN can improve the communication experience and offers an abstraction layer to the deployment of flexible multimedia applications provided on the cloud. In this work, we propose an approach to mitigate globally manage network and computing resources for multimedia applications. To this end, we come up with a control plane entity capable of orchestrating compute and network resources for the multimedia application scenario, relying on SIP control messages. Our solution brings QoS enhancement to the user through resiliency, load balancing, packet inspection, scaling on demand and the separation of control and data planes.

关键词： Quality of service Scalability multimedia computing Measurement Streaming media Cloud computing Load management

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6th International conference on Frontier computing, FC 2017

6th International Conference on Frontier Computing, FC 2017

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6th International conference on Frontier computing, FC 2017

ISBN: (纸本)9789811073977

The proceedings contain 50 papers. The special focus in this conference is on Frontier computing. The topics include: Traffic sign classification base on latent dirichlet allocation;secure cyber physical system R&D project issue in Korea;an empirical study on the clickbait of data science articles in the wechat official accounts;object measurement system based on Surveillance video-images;human action classification in basketball: A single inertial sensor based framework;a smartphone inertial sensor based recursive zero-velocity detection approach;a Cloud platform for compatibility testing of android multimedia applications;critical task scheduling for data stream computing on heterogeneous clouds;clustering of freight vehicle driving behavior based on vehicle networking data mining;an exploratory study of multimodal perception for affective computing system design;heading judgement for indoor position based on the gait pattern;multilayer perceptron application for diabetes mellitus prediction in pregnancy care;Analysis of GLV/GLS method for elliptic curve scalar multiplication;An improved algorithm for facial feature location by multi-template ASM;correlation analysis of climate indices and precipitation using wavelet image processing approach;a study on the computer aided english translation of local legal based on parallel corpus;improvement method of full-scale euler angles attitude algorithm for tail-sitting aircraft;a research on the developing platform of interactive location based virtual learning application;the next generation of internet of things: Internet of vehicles;Performing Iris segmentation by using geodesic active contour (GAC);using data mining to study of relationship between antibiotic and influenza;sleeping customer detection using support vector machine.

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Online Academic Social networking Sites (ASNSs) Selection Through AHP for Placement of Advertisement of E-Learning Website 17th

Online Academic Social Networking Sites (ASNSs) Selection Th...

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17th International conference on Intelligent Systems Design and Applications, ISDA 2017

作者： Singh, Meenu Pant, Millie Kaul, Arshia Jha, P.C. Saharanpur Campus Roorkee India Department of Operational Research University of Delhi Delhi India

ISBN: (纸本)9783319763477

Over the years the use of Social networking sites (SNSs) has grown tremendously. This popularity has led to the incessant need for marketers to concentrate advertising through these websites. The need of the hour is therefore to develop advertising strategies which are effective and efficient. With new SNS being launched each day, the important decision is to determine which SNS is the most appropriate for advertising for a firm. There are many forms and options of SNSs available with different purposes which allow the people to interact with each other such as social connection (facebook, google+, etc.), multimedia sharing (youtube, flickr, etc.), professional (linkedln, classroom 2.0, etc.), hobbies (on my bloom, pinterest, etc.), academic (researchgate, ***, etc.), etc. Due to increase in the types of SNSs, the evaluation and selection of right SNS have become a complex problem for advertisers. In this research, the selection of Academic Social networking sites (ASNSs) is considered as a Multi Attribute Decision Making (MADM) problem for advertising of E-learning website. Analytical Hierarchy Process (AHP) methodology for the selection of ASNSs has been adopted. A real life case study is also presented to show the applicability of the proposed methodology. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Marketing

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Active Forwarding: Eliminate IOMMU Address Translation for Accelerator-rich Architectures 18

Active Forwarding: Eliminate IOMMU Address Translation for A...

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55th ACM/ESDA/IEEE Design Automation conference (DAC)

作者： Fu, Hsueh-Chun Wang, Po-Han Yang, Chia-Lin Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781450357005

Accelerator -rich architectures employ IOMMUs to support unified virtual address, but researches show that they fail to meet the performance and energy requirements of accelerators. Instead of optimizing the speed/energy of IOMMU address translation, this work tackles the issue from a new perspective, eliminating the need for translation with an active for (AF) mechanism that forwards input data of accelerators directly from the CPU cache to the scratchpad memory of the accelerator. Results show that on average, AF can provide 8% performance improvement compared to the state-of-the-art mechanism, hostPageWaik, and reduce 22.1% accelerator power.

关键词： Heterogeneous computing Accelerator-rich Architecture Virtual Memory System

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CatAR: A Novel Stereoscopic Augmented Reality Cataract Surgery Training System with Dexterous Instrument Tracking Technology 18

CatAR: A Novel Stereoscopic Augmented Reality Cataract Surge...

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CHI conference on Human Factors in computing Systems (CHI)

作者： Huang, Yu-Hsuan Chang, Hao-Yu Yang, Wan-ling Chiu, Yu-Kai Yu, Tzu-Chieh Tsai, Pei-Hsuan Ouhyoung, Ming Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Hosp Dept Ophthalmol Taipei Taiwan

ISBN: (纸本)9781450356206

We propose CatAR, a novel stereoscopic augmented reality (AR) cataract surgery training system. It provides dexterous instrument tracking ability using a specially designed infrared optical system with 2 cameras and 1 reflective marker. The tracking accuracy on the instrument tip is 20 lam, much higher than previous simulators. Moreover, our system allows trainees to use and to see real surgical instruments while practicing. Five training modules with 31 parameters were designed and 28 participants were enrolled to conduct efficacy and validity tests. The results revealed significant differences between novice and experienced surgeons. Improvements in surgical skills after practicing with CatAR were also significant.

关键词： Augmented reality Microsurgery Surgical simulator Surgical training Cataract Instrument tracking Dexterous input

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A real-time distributed index based on topic for microblogging system

A real-time distributed index based on topic for microbloggi...

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9th International conference on Internet multimedia computing and Service, ICIMCS 2017

作者： Chen, Zhikun Wang, Lu Yang, Shuqiang Beijing China Computer Department National University of Defense Technology ChangshaHunan China

ISBN: (纸本)9789811085291

With the development of internet technology and widely used in mobile devices, the microblogging systems such as Twitter and Sina Weibo in China have become the most important platform for people to retrieve information and communicate with each other. The real-time search became a big challenge for microblogging systems because of the volume of data and users. Existing approaches build all microblogs in an index which will increase the cost of index update and query. The search results could not satisfy users’ timely and high quality requirements. In this paper, we propose a new real-time distributed index based on topic (RDIBT), which can build index for each topic. Those topical indices will be distributed to many sites, so it can improve the concurrently of queries. Extensive experiments demonstrate the effectiveness and efficiency of RDIBT on the real dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Social networking (online)

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A deep learning approach for scientific paper semantic ranking 10th

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10th KES International conference on Intelligent Interactive multimedia Systems and Services, IIMSS 2017

作者： Gargiulo, Francesco Silvestri, Stefano Fontanella, Mariarosaria Ciampi, Mario De Pietro, Giuseppe Institute for High Performance Computing and Networking ICAR-CNR Via Pietro Castellino 111 Naples80131 Italy

ISBN: (纸本)9783319594798

In this paper we proposed a novel Deep Learning approach to realize a Word Embeddings (WEs) similarity based search tool, considering the medical literature as case study. Using the compositional properties of the WEs we defined a methodology to aggregate the information coming from each word to obtain a vector corresponding to the abstracts of each PubMed article. Through this paradigm it is possible to capture the semantic content of the papers and, consequently, to evaluate and rank the similarity among them. The preliminary results with the proposed approach are obtained analysing a subset of the whole the PubMed collection. The results correctness has been verified by human domain experts, showing that the methodology is promising. © Springer International Publishing AG 2018.

关键词： Deep learning

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