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Delay Optimization for Mobile Cloud computing Application Offloading in Smart Cities 12th

Delay Optimization for Mobile Cloud Computing Application Of...

引用

12th international conference on Innovative Mobile and Internet Services in Ubiquitous computing (IMIS)

作者： Guo, Shan Wang, Ying Meng, Sachula Ma, Nan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9783319935546;9783319935539

In smart cites, more and more smart mobile devices (SMDs) have many computation-intensive applications to be processed. Mobile cloud computing (MCC) as an effective technology can help SMDs reduce their energy consumption and processing delay by offloading the tasks on the distributed cloudlet. However, due to long transmission delay resulting from the unstable wireless environment, the SMD may be out of the serving area before the cloudlet transmits responses to the user. Thus, delay is a crucial problem for the MCC offloading. In this paper, we consider a multi-SMDs MCC system, where each SMD having an application to be offloaded asks for computation offloading to a cloudlet. In order to minimize the total delay of the SMDs in the system, we jointly take the offloading cloudlet selection, wireless access selection, and computation resource allocation into consideration. We formulate the total delay minimization problem as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. We propose an improved genetic algorithm to obtain a local optimal result. Simulation results demonstrated that our proposal could effectively reduce the system delay.

关键词： Mobile cloud computing

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AnnaBellaDB: Key-Value Store Made Cloud Native

AnnaBellaDB: Key-Value Store Made Cloud Native

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international conference on Network and Service Management

作者： Mark Szalay Peter Matray Laszlo Toka MTA-BME Network Softwarization Research Group Ericsson Research

ISBN: (数字)9783903176317

ISBN: (纸本)9781665415477

The cloud-native paradigm has become a well-known approach to ensure the elasticity and reliability of applications running in the cloud. One recurrent motif is the stateless design of applications, which aims to decouple the life-cycle of application states from the life-cycle of individual application instances. Application data is written to and read from cloud databases, deployed close to the application code to ensure low latency bounds on state access. When applying a stateless design, the performance of the cloud service is often limited by the cloud database. In order not to become a bottleneck, database instances are distributed on multiple hosts, and strive to ensure data locality for all application functions. However, the shared nature of certain states, and the inevitable dynamics of the application workload necessarily lead to inter-host data access. If the service is geographically distributed, this is even across data centers and edge servers resulting in a significant delay. To minimize the service performance loss due to the stateless design of applications, we propose a latency and access pattern aware state storage method, called state-layer, that can be easily applied in any kind of key-value store with the ability of deciding where to store replicas in the cluster and measure networking/computing delay. By adapting our solution to Anna, a key-value store from academia, we show the proposed state-layer is ideal to use as a cloud database for storing application states. To foster further research in this area, we make our proof-of-concept solution open-source.

关键词： Servers Noise measurement Monitoring Routing Cloud computing Delays distributed databases

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distributed-Memory DMRG via Sparse and Dense Parallel Tensor Contractions 20

Distributed-Memory DMRG via Sparse and Dense Parallel Tensor...

引用

Supercomputing conference

作者： Ryan Levy Edgar Solomonik Bryan K. Clark Illinois Quantum Information Science and Technology Center (IQUIST) Institute for Condensed Matter Theory University of Illinois at Urbana-Champaign IL USA

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

The density matrix renormalization group (DMRG) algorithm is a powerful tool for solving eigenvalue problems to model quantum systems. DMRG relies on tensor contractions and dense linear algebra to compute properties of condensed matter physics systems. However, its efficient parallel implementation is challenging due to limited concurrency, large memory footprint, and tensor sparsity. We mitigate these problems by implementing two new parallel approaches that handle block sparsity arising in DMRG, via Cyclops, a distributed memory tensor contraction library. We benchmark their performance on two physical systems using the Blue Waters and Stampede2 supercomputers. Our DMRG performance is improved by up to 5.9X in runtime and 99X in processing rate over ITensor, at roughly comparable computational resource use. This enables higher accuracy calculations via larger tensors for quantum state approximation. We demonstrate that despite having limited concurrency, DMRG is weakly scalable with the use of efficient parallel tensor contraction mechanisms.

关键词： Concurrent computing Tensors Runtime Benchmark testing Tools Supercomputers Water resources

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Online distributed Job Dispatching with Outdated and Partially-Observable Information

Online Distributed Job Dispatching with Outdated and Partial...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yuncong Hong Bojie Lv Rui Wang Haisheng Tan Zhenhua Han Hao Zhou Francis C.M. Lau LINKE Lab University of Science and Technology of China Hefei China Southern University of Science and Technology Shenzhen China The University of Hong Kong Hong Kong China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen Chinaa

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due to huge time complexity. We propose a novel low-complexity solution framework for distributed job dispatching, based on which the optimization of job dispatching policy can be decoupled via an alternative policy iteration algorithm, so that the distributed policy iteration of each AP can be made according to partial and outdated observation. A theoretical performance lower bound is proved for our approximate MDP solution. Furthermore, we conduct extensive simulations based on the Google Cluster trace. The evaluation results show that our policy can achieve as high as 20.67% reduction in average job response time compared with heuristic baselines, and our algorithm consistently performs well under various parameter settings.

关键词： Metropolitan area networks Markov processes Dispatching Servers Time complexity Optimization Edge computing

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Internet-QoE 2019: 4th Internet-QoE Workshop on QoE-based Analysis and Management of Data Communication Networks 19

Internet-QoE 2019: 4th Internet-QoE Workshop on QoE-based An...

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25th Annual international conference on Mobile computing and networking (MobiCom)

作者： Casas, Pedro Wamser, Florian Bustamante, Fabian Choffnes, David Austrian Inst Technol Seibersdorf Austria Univ Wurzburg Wurzburg Germany Northwestern Univ Evanston IL 60208 USA Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450361699

After three highly successful editions of the Internet-QoE workshop organized at ACM SIGCOMM 2016, ACM SIGCOMM 2017, and IEEE ICDCS 2018, the goal of the fourth edition of the Internet-QoE workshop is to scale the concepts of Quality of Experience (user satisfaction, user engagement, and behavioral analysis) out of the lab studies context and bring it to the analysis and operation of distributed systems and communication networks, giving a user-centric perspective to the research performed by the MOBICOM community. By fostering an explicit and deep integration of the end-user directly into the design, analysis and management of large-scale operational networks, we expect to reduce the gap between QoE research and its application to future network management paradigms, as well as to provide a more targeted end-user perspective to the research on distributed communication systems. The 4th edition of Internet-QoE also focuses on novel end-user services enabled by next generation technologies such as immersive media (3D, Virtual Reality and Augmented Reality), self-driving cars, intelligent manufacturing systems, Industry 4.0 and tactile Internet, 5G ultra-low-latency mobile networks, and real-time applications.

关键词： User experience Quality of Experience user engagement distributed large-scale measurements interdisciplinary workshop

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SNPD 2019 Message from conference Chairs

SNPD 2019 Message from Conference Chairs

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ACIS international conference on Software Engineering, Artificial Intelligence, networking, and Parallel/distributed computing (SNPD)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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Beyond content analysis: Detecting targeted ads via distributed counting 19

Beyond content analysis: Detecting targeted ads via distribu...

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1st ACM CoNEXT Workshop on Emerging in-Network computing Paradigms (ENCP) part of 15th ACM international conference on Emerging networking EXperiments and Technologies (CoNEXT)

作者： Iordanou, Costas Kourtellis, Nicolas Carrascosa, Juan Miguel Soriente, Claudio Cuevas, Ruben Laoutaris, Nikolaos MPI Informat Saarbrucken Germany Telefon Res Madrid Spain LSTech Milton Keynes Bucks England NEC Labs Europe Heidelberg Germany Univ Carlos III Madrid Telemat Engn Dept Madrid Spain UC3M Santander Big Data Inst Madrid Spain IMDEA Networks Inst Madrid Spain

ISBN: (纸本)9781450369985

Being able to check whether an online advertisement has been targeted is essential for resolving privacy controversies and implementing in practice data protection regulations like GDPR, CCPA, and COPPA. In this paper we describe the design, implementation, and deployment of an advertisement auditing system called eyeWnder that uses crowdsourcing to reveal in real time whether a display advertisement has been targeted or not. Crowdsourcing simplifies the detection of targeted advertising, but requires reporting to a central repository the impressions seen by different users, thereby jeopardizing their privacy. We break this deadlock with a privacy preserving data sharing protocol that allows eyeWnder to compute global statistics required to detect targeting, while keeping the advertisements seen by individual users and their browsing history private. We conduct a simulation study to explore the effect of different parameters and a live validation to demonstrate the accuracy of our approach. Unlike previous solutions, eyeWnder can even detect indirect targeting, i.e., marketing campaigns that promote a product or service whose description bears no semantic overlap with its targeted audience.

关键词： Crowdsourcing Display Advertisements Security and Privacy distributed Algorithms

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Weibo Sentiment Classification Based on Two Channels Text Convolution Neural Network with Multi-Feature

Weibo Sentiment Classification Based on Two Channels Text Co...

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international conference on Cyber-Enabled distributed computing and Knowledge Discovery, CyberC

作者： Wu Yu Zhao Zhenni Yang Jie Lin Yijun Institute of Web Intelligence and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728184487

ISBN: (纸本)9781728184494

Nowadays, Internet platforms such as Weibo have marked a new era for the public to express their attitudes, opinions and sentiments on the government's policies or various social phenomena. The analysis of those ideas and sentiments can provide practical significance to promote the governance of cyberspace, it is also very helpful to observe and warn cyber events. Taking Sina Weibo as the object, most of the research ignores the combination of sentiment prior knowledge and user information. Therefore, the accuracy of sentiment classification is affected. In order to make full use of the rich information on social networks, the two channels text convolution neural network (TC-TextCNN), which combines multi-feature, is proposed. By improving the algorithm of the word embedding, the part of sentiment information is enhanced by sentiment dictionary. To supplement traditional features, this model construct user features according to attributes and behaviors of users. TC-TextCNN can learn semantic features, sentiment features and user features at the same time. The effectiveness of sentiment word embedding and user features has been proved by experiments. Experiments proved that this method achieves better results on classifying sentiment. And the difference of sentiment distribution under different cyber events is verified by empirical analysis, which provides a reference for the early warning of cyber events.

关键词： Dictionaries Convolution Social networking (online) Blogs Neural networks Supervised learning Classification algorithms

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Efficient distributed Training in Heterogeneous Mobile Networks with Active Sampling

Efficient Distributed Training in Heterogeneous Mobile Netwo...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yunhui Guo Xiaofan Yu Kamalika Chaudhuri Tajana Rosing University of California San Diego CA

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Mobile edge computing is an emerging research topic which aims at pushing the computation from the cloud to the edge devices. Most of the current machine learning (ML) algorithms, such as federated learning, are designed for homogeneous mobile networks, that is, all the devices collect the same type of data. In this paper, we address distributed training of ML algorithms in heterogeneous mobile networks where the features, rather than the samples, are distributed across multiple heterogeneous mobile devices. Training ML models in heterogeneous mobile networks incurs a large communication cost due to the necessity to deliver the local data to a central server. Inspired by active learning, which is traditionally used to reduce the labeling cost for training ML models, we propose an active sampling method to reduce the communication cost of learning in heterogeneous mobile networks. Instead of sending all the local data, the proposed active sampling method identifies and sends only informative data from each device to the central server. Extensive experiments on four real datasets, both with numerical simulation and on a networked mobile system, show that the proposed method can reduce the communication cost by up to 53% and energy consumption by up to 67% without accuracy degradation compared with the conventional approaches.

关键词： Training Degradation Energy consumption Machine learning algorithms Sampling methods Data models Servers

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Towards distributed Edge-based Systems

Towards Distributed Edge-based Systems

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IEEE international conference on Cognitive Machine Intelligence (CogMI)

作者： Schahram Dustdar Ilir Murturi Distributed Systems Group TU Wien Vienna Austria

ISBN: (数字)9781728141442

ISBN: (纸本)9781728141459

In the past few years, researchers from academia and industry stakeholders suggest adding more computational resources (i.e., storage, networking, and processing) closer to the end-users and IoT domain, respectively, at the edge of the network. Such computation entities perceived as edge devices aim to overcome high-latency issues between the cloud and the IoT domain. Thus, processing IoT data streams closer to the end-users and IoT domain can solve several operational challenges. Since then, a plethora of application-specific IoT systems are introduced, mainly hard-coded, inflexible, and limited extensibility for future changes. Additionally, most IoT systems maintain a centralized design to operate without considering the dynamic nature of edge networks. In this paper, we discuss some of the research issues, challenges, and potential solutions to enable: i) deploying edge functions on edge resources in a distributed manner and ii) deploying and scaling edge applications on-premises of Edge-Cloud infrastructure. Additionally, we discuss in detail the three-tier Edge-Cloud architecture. Finally, we introduce a conceptual framework that aims to enable easy configuration and deployment of edge-based systems on top of heterogeneous edge infrastructure and present our vision within a smart city scenario.

关键词： Internet of Things Cloud computing Computer architecture Servers Computational modeling Software as a service Smart cities

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