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Ada-copy: An Adaptive Memory Copy Strategy for Virtual Machine Live Migration 23

Ada-copy: An Adaptive Memory Copy Strategy for Virtual Machi...

引用

23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Wang, Zhong Xue, Guangtao Qian, Shiyou Liu, Gongshen Li, Minglu Cao, Jian Yu, Jiadi Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Inst Adv Commun & Data Sci Shanghai Peoples R China

ISBN: (纸本)9781538621295

In the cloud computing architecture, virtual machine (VM) live migration is a fundamental research topic which has drawn extensive attention from communities of industry and academy. It is critical to transfer the VM memory pages that contain essential state information to resume the VM on another host during virtual machine (VM) live migration. There are many memory copy methods, such as pre-copy and postcopy. However, these methods have two limitations: application generality and performance imbalance. In this paper, we propose Ada-copy (adaptive copy), an adaptive memory copy strategy for VM live migration. The basic idea of Ada-copy is that the memory copy method of a VM should be determined by its workload characteristics. Specifically, based on the variation of current dirty page rate of memory, Ada-copy can adaptively select the most appropriate migration method to copy memory pages, thus addressing the two limitations of existing memory copy methods. To evaluate the effectiveness of our proposed strategy, we experiment with the Ada-copy on a variety of migration tasks with different dirty page rate and diverse memory usage workloads. Evaluation results show, compared with traditional methods, Ada-copy can significantly reduce the total migration time by 26%, the VM downtime by 42% and the amount of pages transferred by 35% in average.

关键词： cloud computing live migration dirty page rate virtual machine memory copy strategy

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Relative Localization in Multi-Robot systems Based on Dead Reckoning and UWB Ranging 23

Relative Localization in Multi-Robot Systems Based on Dead R...

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23rd international conference on Information Fusion (FUSION)

作者： Li, Ming Chang, Zhuang Zhong, Zhen Gao, Yan Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China Cyber Real Innovat Ctr Nanjing Peoples R China Chengdu Siwei Power Elect Technol Co Ltd Chengdu Peoples R China

ISBN: (纸本)9780578647098

Combining dead reckoning and Ultra-Wideband (UWB) ranging information to achieve relative localization (RL) becomes prevalent in recent years. However, two main problems, i.e., pose initialization and distributed implementation, are rarely investigated in practical multi-robot systems. In this paper, a novel two-stage RL method is proposed to fill this gap, wherein an initialization strategy, using robot-to-robot measurements acquired at different vantage points during robot motion to determine initial pose, and a consensus-based distributed particle filter (DPF), fusing statistics from local robot and neighbors to realize RL, are designed. In our system, by computing the pairwise relative pose between all robots in a team, the initialization strategy can determine an estimate of the initial pose of all robots with respeet to a common reference frame and the corresponding covariance. The consensus-based DPF allows determining the relative pose of all robots with significantly reduced computing costs. Experiments results on a team of differentially driven mobile robots show the effectiveness of the initialization strategy and highlight the low computation cost of the proposed approach.

关键词： Multipurpose robots

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Pattern-Based Modeling of High-Performance computing Resilience 1

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23rd international conference on Parallel and distributed computing (Euro-Par)

作者： Hukerikar, Saurabh Engelmann, Christian Oak Ridge Natl Lab Comp Sci & Math Div POB 2009 Oak Ridge TN 37831 USA

ISBN: (数字)9783319751788

ISBN: (纸本)9783319751788;9783319751771

With the growing scale and complexity of high-performance computing (HPC) systems, resilience solutions that ensure continuity of service despite frequent errors and component failures must be methodically designed to balance the reliability requirements with the overheads to performance and power. Design patterns enable a structured approach to the development of resilience solutions, providing hardware and software designers with the building block elements for the rapid development of novel solutions and for adapting existing technologies for emerging, extreme-scale HPC environments. In this paper, we develop analytical models that enable designers to evaluate the reliability and performance characteristics of the design patterns. These models are particularly useful in building a unified framework that analyzes and compares various resilience solutions built using a combination of patterns.

关键词： High-performance computing Resilience Patterns Performance Reliability Modeling

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Joint Energy and Computation Workload Management for Geo-distributed Data Centers in Smart Grid 23

Joint Energy and Computation Workload Management for Geo-Dis...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Zizhen Wang, Ran Zhu, Kun Yi, Changyan Liu, Linfeng Niyato, Dusit Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Posts and Telecommunications Nanjing China Nanyang Technological University Singapore

ISBN: (纸本)9781665494571

With the increasing demands of data computation and storage for cloud-based services, the energy demand of data centers (DCs) is rising rapidly and becoming a noticeable challenge for current power networks. The smart grid (SG) is deemed as the future power system paradigm enabling more affordable and sustainable energy supply, which can effectively relieve the power load pressure of DCs. Moreover, with growing concerns regarding harmful emissions due to combustion of fossil fuels, the exploitation of renewable energy sources (RES) has attracted extensive attention, benefiting both SGs and DCs at large. However, the geo-distributed property of DCs and SGs, as well as the uncertain nature of RES production pose severe challenges to the optimal management of computation and energy resources in such coupling system. Focusing on these issues, a joint energy and computation workload management framework is proposed for enabling a sustainable DC paradigm with distributed RES. Specifically, to model the interactions between data center operator (DCO) and SGs, a Stackelberg game is formulated. The SG offers the DCO an electricity selling price to maximize the benefit. The DCO allocates the computation workload to geo-distributed DCs, aiming to minimize the costs of energy consumption and carbon emissions. We obtain the equilibrium state of the game and prove its uniqueness and optimality. Simulation experiments are conducted to evaluate the performance of the proposed scheme, showing that it can achieve considerable cost savings over counterparts. The proposed approach and obtained results provide useful insights for helping the DCO developing rational management strategies. © 2021 IEEE.

关键词： Data centers Costs Smart cities Computational modeling Games Carbon dioxide Data models

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PSeer: Performance Prediction for Partially Co-located distributed Deep Learning 23

PSeer: Performance Prediction for Partially Co-located Distr...

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作者： Ding, Weijia Ding, Ziwei Zhao, Laiping Qiu, Tie College of Intelligence and Computing Tianjin University Tianjin Key Laboratory of Advanced Networking China

ISBN: (纸本)9781665494571

This paper studies the problem of predicting the deep learning (DL) jobs' training time which is the fundamental guide for training resources allocating and job scheduling. The existing prediction approaches, however, are greatly challenged for distributed deep learning (DDL) jobs due to the partial interference, namely, the resource contention that only occurs at partial time or locations of a job. We observe that the maximum difference in training time under different combinations of partial interference DDL can reach 2.2×. Thus partial interference is not a negligible factor for predicting the training time of DDL jobs. In this paper, we present PSeer, a partial interference-aware training time predictor for DDL workloads. It adopts a combined dual-submodel (the step number submodel and the step speed submodel), which is highly accurate in predicting by encoding the spatially and temporally varied partial interference into the model design. It is also lightweight: the step number submodel only requires the initial training loss value, and the step speed submodel can be constructed using the CPU and GPU resource utilization information of a few pre-trained steps. Empirical evaluation shows the prediction error generated by PSeer could achieve as low as 3.93% when employing RFR in the training speed submodel, and the prediction overhead is © 2021 IEEE.

关键词： Training Deep learning Graphics processing units Interference Predictive models Encoding Resource management

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An Architecture for Programming distributed Applications on Fog to Cloud systems 1

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23rd international conference on Parallel and distributed computing (Euro-Par)

作者： Lordan, Francesc Lezzi, Daniele Ejarque, Jorge Badia, Rosa M. Barcelona Supercomp Ctr BSC Dept Comp Sci Barcelona Spain Univ Politecn Catalunya UPC Dept Comp Architecture Barcelona Spain Artificial Intelligence Res Inst Spanish Natl Res Council CSIC Barcelona Spain

ISBN: (数字)9783319751788

ISBN: (纸本)9783319751788;9783319751771

This paper presents a framework to develop and execute applications in distributed and highly dynamic computing systems composed of cloud resources and fog devices such as mobile phones, cloudlets, and micro-clouds. The work builds on the COMPSs programming framework, which includes a programming model and a runtime already validated in HPC and cloud environments for the transparent execution of parallel applications. As part of the proposed contribution, COMPSs has been enhanced to support the execution of applications on mobile platforms that offer GPUs and CPUs. The scheduling component of COMPSs is under design to be able to offload the computation to other fog devices in the same level of the hierarchy and to cloud resources when more computational power is required. The framework has been tested executing a sample application on a mobile phone offloading task to a laptop and a private cloud.

关键词： distributed computing Mobile computing Fog computing Programming model Computation offloading Fault tolerance Security

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An agent based framework for peer to peer distributed computing

An agent based framework for peer to peer distributed comput...

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23rd international conference on Computers and Their Applications, CATA 2008

作者： Choudhury, Subhrabrata Mukherjee, Devlina Chakraborty, Paramita Debnath, Narayan Lee, Ilhyun National Institute of Technology Durgapur M. G. Avenue Durgapur-713209 India Department of Computer Science Winona State University Winona MN 55987 United States Department of Science and Mathematics University of Texas-Permian Basin Odessa TX 79762 United States

ISBN: (纸本)9781604232356

In this paper, we propose an agent based peer to peer framework for service oriented distributed computing especially suitable for mobile ad hoc environment. The agent based architecture implements distributed UDDI in a dynamic and context aware manner so that the registry entries are distributed optimally among the peers and at the same time individual objectives are also fulfilled satisfactorily. The framework also supports dynamic trust based service ranking and dynamic service composition capability to offer higher service dependability and faster adaptability. Each node in the system acts as a peer and implements the agent functionalities proposed in this paper. The performance of our system is found better in terms of efficiency and dependability compared to traditional client/server based SOA implementation under identical behavioral pattern commonly experienced in a mobile ad hoc environment.

关键词： distributed computer systems

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Real-Time Dense Wired Sensor Network Based on Traffic Shaping 23

Real-Time Dense Wired Sensor Network Based on Traffic Shapin...

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23rd IEEE international conference on Embedded and Real-Time computing systems and Applications (RTCSA)

作者： Loureiro, Joao Rangarajan, Raghuraman Nikolic, Borislav Indrusiak, Leandro Tovar, Eduardo Polytech Inst Porto ISEP CISTER INESC TEC Oporto Portugal Tech Univ Carolo Wilhelmina Braunschweig Inst Comp & Network Engn Braunschweig Germany Univ York York N Yorkshire England

ISBN: (纸本)9781538618981

XDense is a novel wired 2D-mesh grid sensor network system for application scenarios that benefit from densely deployed sensing (e.g. thousands of sensors per square meter). It was conceived for closed-loop cyber-physical systems (CPS) that require real-time actuation, like active flow control (AFC) on aircraft wing surfaces. XDense communication and distributed processing capabilities are designed such that they enable to extract complex features within bounded time and in a responsive manner. In this paper we tackle the issue of deterministic behavior of XDense. We present a methodology that uses traffic shaping heuristics to guarantee bounded communication delays and the fulfillment of memory requirements. We evaluate the model for varied network configurations and workload, and demonstrate the effectiveness of running real-time applications supported on XDense.

关键词： Delays Ports (Computers) Real-time systems Sensors Aircraft Feature extraction Mathematical model

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Supervised Learning Based Algorithm Selection for Deep Neural Networks 23

Supervised Learning Based Algorithm Selection for Deep Neura...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Shi, Shaohuai Xu, Pengfei Chu, Xiamen Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China HKBU Inst Res & Continuing Educ Shenzhen Peoples R China

ISBN: (纸本)9781538621295

Many recent deep learning platforms rely on thirdparty libraries (such as cuBLAS) to utilize the computing power of modern hardware accelerators (such as GPUs). However, we observe that they may achieve suboptimal performance because the library functions are not used appropriately. In this paper, we target at optimizing the operations of multiplying a matrix with the transpose of another matrix (referred to as NT operation hereafter), which contribute half of the training time of fully connected deep neural networks. Rather than directly calling the library function, we propose a supervised learning based algorithm selection approach named MTNN, which uses a gradient boosted decision tree to select one from two alternative NT implementations intelligently: (1) calling the cuBLAS library function;(2) calling our proposed algorithm TNN that uses an efficient out-of-place matrix transpose. We evaluate the performance of MTNN on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve 96% of prediction accuracy with very low computational overhead, which results in an average of 54% performance improvement on a range of NT operations. To further evaluate the impact of MTNN on the training process of deep neural networks, we have integrated MTNN into a popular deep learning platform Caffe. Our experimental results show that the revised Caffe can outperform the original one by an average of 28%. Both MTNN and the revised Caffe are open-source.

关键词： Linear Algebra Matrix Multiplication Transpose GPU Deep Neural Networks

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Spark-based Measurement and Analysis on Offline Mobile Application Market over Device-to-Device Sharing in Mobile Social Networks 23

Spark-based Measurement and Analysis on Offline Mobile Appli...

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23rd IEEE international conference on Parallel and distributed systems (ICPADS)

作者： Zhang, Yuhua Huang, Zihan Wang, Shanjia Wang, Xiaofei Jiang, Tianpeng Tianjin Univ Dept Comp Sci & Technol Tianjin Key Lab Adv Networking Tianjin Peoples R China Beijing Anqi Zhilian Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9781538621295

Recently how to select seeding users with large impacts in social networks has gained more and more attention in many studies. It has been demonstrated in a number of researches that the seeding users, playing vital roles in social groups, could be exploited to promote the dissemination of popular contents. Nevertheless, the existed algorithms, which are performed on small-scale data sets with limited feature dimensions, mostly base on unconsolidated hypotheses and measurements of data sets. Consequently, their performance and precision cannot be well evaluated and improved. In this paper, we firstly make comprehensive and large-scale measurements on 3.56 TBytes of real data sets related to Device-to-Device (D2D) content sharing activity traces from a popular D2D sharing application (APP). The mobile social networks generated by the offline content deliveries between users are presented and analyzed. Focusing on the seeding users' selection problem in social networks, we propose algorithms of weighted SeedRanks (SRs) to select the seeding users with accuracy. The algorithms are adapted to the parallel computing platform of Apache Spark with high performance. The results of the recurrent experiment on the large-scale D2D data sets prove the efficiency of our algorithms. Finally, we make conclusions and discuss future work.

关键词： Mobile Social Networks (MSNs) Device-to-Device (D2D) Seeding Users Influence Maximization Content Dissemination

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