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检索条件"任意字段=23rd International Conference on Distributed Computing and Networking, ICDCN 2022"

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international conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS

International Conference on Architectural Support for Progra...

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23rd international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2018

ISBN: (纸本)9781450349116

The proceedings contain 56 papers. The topics discussed include: hardware multithreaded transactions;Slim NoC: a low-diameter on-chip network topology for high energy efficiency and scalability;skyway: connecting managed heaps in distributed big data systems;espresso: brewing Java for more non-volatility with non-volatile memory;enhancing cross-ISA DBT through automatically learned translation rules;Gloss: seamless live reconfiguration and reoptimization of stream programs;filtering translation bandwidth with virtual caching;automatic matching of legacy code to heterogeneous APIs: an idiomatic approach;understanding and auto-adjusting performance-sensitive configurations;CALOREE: learning control for predictable latency and low energy;Darwin: a genomics co-processor provides up to 15,000x acceleration on long read assembly;liquid silicon-Monona: a reconfigurable memory-oriented computing fabric with scalable multi-context support;exploiting dynamic thermal energy harvesting for reusing in smartphone with mobile applications;quantum computing is getting real: architecture, PL, and OS roles in closing the gap between quantum algorithms and machines;DAMN: overhead-free IOMMU protection for networking;statistical reconstruction of class hierarchies in binaries;FCatch: automatically detecting time-of-fault bugs in cloud systems;and LTRF: enabling high-capacity register files for GPUs via hardware/software cooperative register prefetching.

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High Resource Utilization Auto-Scaling Algorithms for Heterogeneous Container Configurations 23

High Resource Utilization Auto-Scaling Algorithms for Hetero...

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

作者： Cheng, Yi-Lin Lin, Ching-Chi Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Acad Sinica Inst Informat Sci Taipei Taiwan

ISBN: (纸本)9781538621295

Auto-scaling is a technique that allocates resources according to dynamic workload. This paper focuses on autoscaling with heterogeneous container configurations. The goal is to minimize the cost of container adjustments, and to reduce the resource insufficiency penalty, while maintaining high resource utilization. It is extremely difficult to achieve the minimal cost without knowing the future workloads in advance. Thus, we first propose an optimal dynamic programming algorithm that can scale optimally when given the future workload. This optimal solution is used as the baseline to evaluate other algorithms that do not have the future workload information. Then, we propose two greedy algorithms that do not need workload information in advance, and a heuristic algorithm that first predicts the workload of the next time step using Gradient Boosting Regression, then makes scaling decisions using the optimal dynamic programming algorithm. We evaluate these four algorithms with two realistic workload traces. The experiments show that when the cost to start new servers is much higher than resource insufficiency penalty, our short-term prediction approach will only increase the total cost by only 9.6%, and decrease the utilization by only 10%, when compared with the optimal dynamic programming that knows the future workload.

关键词： Auto-scaling Cloud computing Container Resource utilization

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CPU/GPU Collaboration Techniques for Transfer Learning on Mobile Devices. 23

CPU/GPU Collaboration Techniques for Transfer Learning on Mo...

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

作者： Valery, Olivier Liu, Pangfeng Wu, Jan-Jan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Taiwan Univ Grad Inst Networking & Multimedia Dept Comp Sci & Informat Engn Taipei Taiwan Acad Sinica Inst Informat Sci Res Ctr Informat Technol Innovat Taipei Taiwan

ISBN: (纸本)9781538621295

As mobile devices become more capable, the need for customization of mobile services becomes increasingly important for users. Nowadays, mobile device sensors are able to collect information from users throughout the day, which gives insight into their profiles. The advent of modern System-on-Chip architectures has enabled mobile devices to tackle machine learning-oriented problems heretofore reserved to desktop computers. The recent success of deep learning makes it a method of choice for understanding the complex user patterns on mobile devices. Unfortunately, training a deep neural network is often considered as too computationally intensive on mobile devices. To address this issue, we consider transfer learning, a technique that aims to take advantage of deep learning features that have been previously learned to improve the learning performance of another neural network. In this paper, we propose a deep learning framework TransferCL, that supports transfer learning on mobile devices. Our approach relies on the collaboration of the multicore CPU and the integrated GPU to accelerate deep learning computation on mobile devices. We consider three major issues - performance/portability tradeoff, power efficiency, and memory management, propose our approaches, and conduct experiments to evaluate them.

关键词： OpenCL GPGPU mobile computing heterogeneous system System-on-Chip deep learning transfer learning

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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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D-Ary Cuckoo Filter: A Space Efficient Data Structure for Set Membership Lookup 23

<i>D</i>-<i>Ary</i> Cuckoo Filter: A Space Efficient Data St...

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

作者： Xie, Zhuohan Ding, Wencheng Wang, Hongya Xiao, Yingyuan Liu, Zhenyu Donghua Univ Sch Comp Sci & Technol Shanghai Peoples R China Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China Shanghai Key Lab Comp Software Testing & Evaluat Shanghai Peoples R China

ISBN: (纸本)9781538621295

Many networking and distributed systems use Bloom filters and their variants for high speed set membership tests. Such probabilistic techniques provide very good space efficiency at the cost of a small fraction of false positive answers. The original Bloom filters do not permit deletion of items from the set and most attempts to extend Bloom filters to support deletions suffer from either space or time performance degradation. Recently, inspired by Cuckoo Hashing, Fan et. al. proposed a data structure called Cuckoo filter that achieves even better space performance than Bloom filters while supporting dynamic insertion and deletion of items. By allowing that each element has more than two candidate buckets, d-ary Cuckoo Hashing is capable of providing much higher space utilization. Motivated by this study, we generalize Cuckoo filter to d-ary Cuckoo filter for further reduction in space cost. The main difficulty here is that increasing the number of candidate buckets is not as easy as it appears because only fingerprints are available for the calculation of candidate locations. To solve this problem, we introduce the base-d digitwise xor operations as the foundation for computing the d candidate buckets of each element in a cyclic fashion. Theoretical analysis and experiment study show that d-ary Cuckoo filters can save up to one bit for each element at the cost of increased lookup and insertion performance.

关键词： Data Structures Bloom Filters Cuckoo Filters Membership Tests

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Multi-level Virtualization and Its Impact on System Performance in Cloud computing 23rd

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23rd international conference on Computer Networks (CN)

作者： Lubomski, Pawel Kalinowski, Andrzej Krawczyk, Henryk Gdansk Univ Technol IT Serv Ctr Gdansk Poland Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland

ISBN: (纸本)9783319392073;9783319392066

The results of benchmarking tests of multi-level virtualized environments are presented. There is analysed the performance impact of hardware virtualization, container-type isolation and programming level abstraction. The comparison is made on the basis of a proposed score metric that allows you to compare different aspects of performance. There is general performance (CPU and memory), networking, disk operations and application-like load taken into account. The tested technologies are, inter alia, VMware ESXi, Docker and Oracle JVM.

关键词： Virtualization Performance Benchmark

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Towards Cloud-based distributed Scaleable Processing over Large-scale Temporal Graphs 23

Towards Cloud-based Distributed Scaleable Processing over La...

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23rd IEEE international conference on Enabling Technologies, Infrastructure for Collaborative Enterprises (WETICE)

作者： Steinbauer, Matthias Kotsis, Gabriele Johannes Kepler Univ Linz Dept Telecooperat A-4040 Linz Austria

ISBN: (纸本)9781479942497

Large-scale temporal graphs can serve as a model in many application scenarios. Recently, due to the popularity of online social networks and increased research interest in reality mining i.e. gathering and analyzing data about human behavior and interaction in the real world, temporal graphs gain traction in social network analysis and more specifically in the analysis of dynamic processes in social networks. However, current methods for social network analysis either require data to be processed offline, lack support for temporal graphs, or support datasets of limited size only. In this work we present a cloud-based distributed processing framework designed for large-scale temporal graphs. By using computing resources in the cloud this system is scaleable and already constructed for the massive datasets that occur in social network analysis.

关键词： cloud computing data analysis graph theory social networking (online) cloud-based distributed processing framework cloud-based distributed scaleable processing computing resources data gathering large-scale temporal graph online social networks reality mining social network analysis Cloud computing Clustering algorithms Context Electronic mail Organizations Partitioning algorithms Social network services cloud-computing distributed computing scaleable temporal graph Social network analysis Cloud computing Social Networks Social network services Partitioning algorithms electronic mail Clustering algorithms Data Collection Methods distributed computing Data analysis Graph theory Scalability

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CDrdN: Content Driven Routing in Datacenter Network 23

CDRDN: Content Driven Routing in Datacenter Network

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23rd international conference on Computer Communication and Networks (ICCCN)

作者： Zhu, Ming Li, Dan Liu, Ying Wu, Jianping Tsinghua Univ Dept Comp Sci Beijing Peoples R China Tsinghua Natl Lab Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781479935727

A major challenge in data center networks is to provide enough network capacity to meet the ever increasing demand of large-scale distributed computing. While existing proposals focus on adding more switches and links, which cost extra hardware and energy, we explore another dimension in which spare disk space at servers is used for caching data, so as to increase network throughput without additional hardware. Leveraging the Named Data networking (NDN) architecture and unique characteristics of data centers, we design a novel Content Driven Routing in Datacenter Network (CDrdN) that enables universal caching in data center networks in an efficient and scalable way. First, rather than using switches for "on-path" caching like in native NDN, CDrdN uses the large storage space at servers to do "off-path" caching. Second, by taking advantage of data center's regular and hierarchical network topology, CDrdN switches are able to direct requests to nearby server caches even under high dynamics of caches. Third, given the vast amount of data in data centers, the full name-based routing table would be difficult to fit in switch's limited fast memory. CDrdN adopts a compound content and location routing to ensure packet delivery while benefiting from name-based routing as much as the switches can afford. CDrdN extends NDN's adaptive forwarding mechanism to deal with cache misses, link failures, and congestion without running routing protocols or cache exchange protocols. Our packet-level simulations show that CDrdN can almost double the network throughput compared with shortest-path routing under the same setting, and CDrdN can effectively deal with link failures using adaptive forwarding.

关键词： Datacenter Network Named Data networking Routing Algorithm distributed File System

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Greedy-based distributed algorithms for green traffic routing

Greedy-based distributed algorithms for green traffic routin...

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international conference on Computer Communications and Networks (ICCCN)

作者： Yunlong Song Min Liu University of Chinese Academy of Sciences Beijing P.R. China Institute of Computing Technology Chinese Academy of Sciences

Green networking has become a hot research area in recent years. Most works just focus on centralized algorithms, where a central controller is used to collect all the information of the network and compute the energy aware traffic routing. However, these centralized algorithms only fit to the scenario when the network is not large and assume that all the network's information can be timely collected. When the above condition is not satisfied, the centralized algorithms will not work any more. In this paper, we study the basic principles of energy aware traffic routing and use them to explore a greedy-based distributed algorithm. The evaluation shows that our algorithm can reduce more energy consumption (almost 15%) than the existing distributed algorithms.

关键词： Density measurement Power system measurements Routing Power demand distributed algorithms Green products Routing protocols

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A distributed Application-level IT System Workload Generator

A Distributed Application-level IT System Workload Generator

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23rd international conference on Advanced Information networking and Applications Workshops

作者： Mueller, Christopher Horie, Michael Neville, Stephen W. Univ Victoria Dept Elect & Comp Engn Victoria BC Canada

ISBN: (纸本)9781424439997

Developing a capacity to test distributed systems hinges on being able to generate the workloads that these systems are to process. Appropriate tools must not only generate these workloads in real-time, but must also be able to sweep through a range of possible workload characteristics to support sensitivity and robustness analyses. Currently, the majority of prior work in this area, including Harpoon, ns-2, OpNet, and tepreplay, has focused on the re-production of workload traces at the network-level. However, for many distributed systems, reproducing application-level workload characteristics is more informative from a testing perspective. This work details such an application-level workload generation tool. The tool itself is distributed and, hence, easily scales to using multiple machines to re-create complex multi-homed workloads. Furthermore, the tool supports the standard abilities to produce both statistically-described workloads, as well as re-instantiating previously-captured workload traces.

关键词： distributed computing automatic test software

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