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Uniform Circle Formation by Mobile Robots 18

Uniform Circle Formation by Mobile Robots

引用

19th international conference on distributed computing and networking (ICDCN)

作者： Mondal, Moumita Chaudhuri, Sruti Gan Future Inst Technol Kolkata W Bengal India Jadavpur Univ Kolkata W Bengal India

This paper describes a distributed algorithm for swarm robots to form a uniform circle which can be required for guarding or covering an geographical boarder by a group of robots.

ISBN: (纸本)9781450363976

This paper describes a distributed algorithm for swarm robots to form a uniform circle which can be required for guarding or covering an geographical boarder by a group of robots.

关键词： Mobile Robots Uniform Circle Formation distributed Algorithm

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D2D-assisted computation offloading for mobile edge computing systems with energy harvesting 20

D2D-assisted computation offloading for mobile edge computin...

引用

20th international conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Li, Molin Chen, Tong Zeng, Jiaxin Zhou, Xiaobo Li, Keqiu Qi, Heng Tianjin Key Laboratory of Advanced Networking School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin300350 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781728126166

In mobile edge computing (MEC) systems with energy harvesting, the mobile devices are empowered with the energy that harvested from renewable energy sources. On the other hand, mobile devices can offload their computation-intensive tasks to the MEC server to further save energy and reduce the task execution latency. However, the energy harvested is unstable and the mobile devices have to make sure that the energy should not be run out. Moreover, the wireless channel condition between the mobile device and the MEC server is dynamically changing, leading to unstable communication delay. Considering the energy constraints and unstable communication delay, the benefit of computation offloading is limited. In this paper, we investigate D2D-assisted computation offloading for mobile edge computing systems with energy harvesting. In our method, the mobile device is allowed to offload its tasks to the MEC server with the help of its neighbor node. More Specifically, the neighbor node acts as a relay to help the mobile device to communicate with the MEC server. Our goal is to minimize the average task execution time by selecting an optimal execution strategy for each task, i.e., whether to execute the task locally, or offload it to the MEC server directly, or offload it to the MEC server with the help of the most suitable neighbor node, or just to drop it. We propose a low-complexity online algorithm, which stem from Lyapunov Optimization-based Dynamic Computation Offloading (LODCO) algorithm, to solve this problem. Extensive simulations verified the effectiveness of the proposed algorithm, where the average task execution time is reduced around 50% as compared to that of the original LODCO algorithm. © 2019 IEEE.

关键词： Energy harvesting

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Live Forensics for HPC Systems: A Case Study on distributed Storage Systems 20

Live Forensics for HPC Systems: A Case Study on Distributed ...

引用

Supercomputing conference

作者： Saurabh Jha Shengkun Cui Subho S. Banerjee Tianyin Xu Jeremy Enos Mike Showerman Zbigniew T. Kalbarczyk Ravishankar K. Iyer University of Illinois at Urbana-Champaign Urbana-Champaign IL USA National Center for Supercomputing Applications (NCSA) Urbana IL USA

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

Large-scale high-performance computing systems frequently experience a wide range of failure modes, such as reliability failures (e.g., hang or crash), and resource overload-related failures (e.g., congestion collapse), impacting systems and applications. Despite the adverse effects of these failures, current systems do not provide methodologies for proactively detecting, localizing, and diagnosing failures. We present Kaleidoscope, a near real-time failure detection and diagnosis framework, consisting of of hierarchical domain-guided machine learning models that identify the failing components, the corresponding failure mode, and point to the most likely cause indicative of the failure in near real-time (within one minute of failure occurrence). Kaleidoscope has been deployed on Blue Waters supercomputer and evaluated with more than two years of production telemetry data. Our evaluation shows that Kaleidoscope successfully localized 99.3% and pinpointed the root causes of 95.8% of 843 real-world production issues, with less than 0.01% runtime overhead.

关键词： Production systems Runtime Machine learning Real-time systems Supercomputers Reliability Telemetry

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Modeling Interprocessor Communication and Performance Scalability for distributed Deep Learning Systems

Modeling Interprocessor Communication and Performance Scalab...

引用

2019 international conference on High Performance computing and Simulation, HPCS 2019

作者： Lyu, Yi-Hong Liu, Cheng-Yueh Lee, Chen-Pang Tu, Chia-Heng Hung, Shih-Hao National Taiwan University Taipei10617 Taiwan National Cheng Kung University Tainan70101 Taiwan

ISBN: (纸本)9781728144849

While deep learning applications become popular, the design of deep learning systems is a critical task to unleash the computing power of underlying systems. Aside from the computing hardware, the computer networking is also a key factor that affects the delivered performance. When considering a large and complex model, the scalability of the system highly depends on the design of the networks, as well as the software behaviors. In this paper, we propose a profile-data-guided performance prediction method to estimate the performance of the system with desired high-speed interconnects, based on the profiling data obtained in a previous run. In particular, we leverage the open-source profiling tool, SOFA, for characterizing the software activities of deep learning software running in a computer cluster, and the characterized information is used to build the performance model for the model training process. When estimating the performance, SOFA is used to capture the performance critical factors for the model to make the predictions. To evaluate the proposed method, four popular deep learning models are adopted in our experiments, ResNet50, Inception3, Alexnet, and VGG16, where a computer cluster formed by four nodes is used to profile the training of the above models on TensorFlow. We ran the scalability analysis to analyze the size of the cluster, and the suitable computer networks for the models. By comparing the predicted data and those measured on the cluster, our model achieves up to 95% accuracy in most of the cases, with the maximum error rate of 10%. © 2019 IEEE.

关键词： Scalability

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A New Architecture for distributed computing in Named Data networking 20

A New Architecture for Distributed Computing in Named Data N...

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20th IEEE international conference on High Performance computing and Communications (HPCC) / 16th IEEE international conference on Smart City (SmartCity) / 4th IEEE international conference on Data Science and Systems (DSS)

作者： Ma, Yuxiang Wu, Yulei Li, Jun Ge, Jingguo Chinese Acad Sci Comp Network Informat Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England Chinese Acad Sci Inst Informat Engn Beijing 100195 Peoples R China

ISBN: (纸本)9781538666142

With the development of new technologies such as big data, data mining and artificial intelligence, there is a great demand for distributed computing. Named Data networking (NDN) is a promising future Internet architecture and is receiving more and more research attentions. In this paper, we propose a new architecture to support distributed computing in NDN. Due to the nature of NDN, the computing nodes can be selected in proximity to consumers/ users. In addition, we propose a new privacy protection scheme that allows users to anonymously access network resources, e.g.,computing resources. The effectiveness and overhead of this architecture and the devised privacy protection scheme are evaluated. The results demonstrate the feasibility of the proposed architecture.

关键词： Computer architecture Privacy Task analysis Security Internet Delays

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Scaling distributed Deep Learning Workloads beyond the Memory Capacity with KARMA 20

Scaling Distributed Deep Learning Workloads beyond the Memor...

引用

Supercomputing conference

作者： Mohamed Wahib Haoyu Zhang Truong Thao Nguyen Aleksandr Drozd Jens Domke Lingqi Zhang Ryousei Takano Satoshi Matsuoka National Institute of Advanced Industrial Science and Technology Japan RIKEN Center for Computational Science Kobe Japan miHoYo Inc Tokyo Institute of Technology Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

The dedicated memory of hardware accelerators can be insufficient to store all weights and/or intermediate states of large deep learning models. Although model parallelism is a viable approach to reduce the memory pressure issue, significant modification of the source code and considerations for algorithms are required. An alternative solution is to use out-of-core methods instead of, or in addition to, data parallelism. We propose a performance model based on the concurrency analysis of out-of-core training behavior, and derive a strategy that combines layer swapping and redundant recomputing. We achieve an average of 1. 52x speedup in six different models over the state-of-the-art out-of-core methods. We also introduce the first method to solve the challenging problem of out-of-core multi-node training by carefully pipelining gradient exchanges and performing the parameter updates on the host. Our data parallel out-of-core solution can outperform complex hybrid model parallelism in training large models, e.g. Megatron-LM and Turning-NLG.

关键词： Training Deep learning Computational modeling High performance computing Memory management Data models Pipeline processing

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A Load Balancing Method for distributed Key-Value Store Based on Order Preserving Linear Hashing and Skip Graph

A Load Balancing Method for Distributed Key-Value Store Base...

引用

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

作者： Ken Higuchi Jin Murayama Tatsuo Tsuji Graduate School of Engineering University of Fukui Fukui Japan University of Fukui Fukui Japan

In this paper, a load balancing method for the distributed key-value store based on order preserving linear hashing and Skip Graph is proposed. In this system, data are divided by order preserving linear hashing and Skip Graph is used for overlay network. The routing table of this system is very uniform. Then, short detours can exist in the route of forwarding. By using these detours, the number of hops for the query forwarding is reduced. But since data is partitioned by linear hash, load balancing among nodes is difficult. In the proposed method, by dividing a physical node and a Skip Graph node, load balancing can be done. Furthermore, the maximum number of hops for message forwarding become smaller by this load balancing method.

关键词： Routing Load management distributed databases Overlay networks Hash functions Arrays Limiting

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Towards Executing Computer Vision Functionality on Programmable Network Devices 1

Towards Executing Computer Vision Functionality on Programma...

引用

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： Glebke, Rene Krude, Johannes Kunze, Ike Ruth, Jan Senger, Felix Wehrle, Klaus Rhein Westfal TH Aachen Chair Commun & Distributed Syst Aachen Germany

ISBN: (纸本)9781450370004

By offering the possibility to already perform processing as packets traverse the network, programmable data planes open up new perspectives for applications suffering from strict latency and high bandwidth requirements. Real-time Computer Vision (CV), with its high data rates and often mission- and safety-critical roles in the control of autonomous vehicles and industrial machinery, could particularly benefit from executing parts of its logic within network elements. In this paper, we thus explore what it takes to bring CV to the network. We present our work-in-progress efforts of implementing a line-following algorithm based on convolution filters on a P4-programmable NIC. We find that by appropriately identifying regions of interest in the image data and by diligently distributing the necessary calculations among the various match/action stages of the ingress- and egress pipelines of the NIC, our prototypical implementation can achieve over 19 decisions per second on 640x480 px grayscale images with filters large enough to guide a small autonomous car through various courses.

关键词： In-network processing P4 computer vision convolution filters

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Data Center Clustering for Geographically distributed Cloud Deployments 33rd

Data Center Clustering for Geographically Distributed Cloud ...

引用

33rd international conference on Advanced Information networking and Applications, AINA 2019

作者： Pradhan, Dipesh Zahid, Feroz Simula Research Laboratory Martin Linges vei 25 Fornebu1364 Norway

ISBN: (纸本)9783030150341

Geographically-distributed application deployments are critical for a variety of cloud applications, such as those employed in the Internet-of-Things (IoT), edge computing, and multimedia. However, selecting appropriate cloud data centers for the applications, from a large number available locations, is a difficult task. The users need to consider several different aspects in the data center selection, such as inter-data center network performance, data transfer costs, and the application requirements with respect to the network performance. This paper proposes a data center clustering mechanism to group befitting cloud data centers together in order to automate data center selection task as governed by the application needs. Employing our clustering mechanism, we present four different types of clustering schemes, with different importance given to available bandwidth, latency, and cloud costs between pair of data centers. The proposed clustering schemes are evaluated using a large number of data centers from two major public clouds, Amazon Web Services, and Google Cloud Platform. The results, based on a comprehensive empirical evaluation of the quality of obtained clusters, show that the proposed clustering schemes are very effective in optimizing data center selection as per the application requirements. © 2019, Springer Nature Switzerland AG.

关键词： Network performance

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Julunga: A new Large-Scale distributed Read-Write File Storage System for Cloud computing Environments 32

Julunga: A new Large-Scale Distributed Read-Write File Stora...

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32nd IEEE international conference on Advanced Information networking and Applications (AINA)

作者： Dewan, Hrishikesh Hansdah, R. C. Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India

ISBN: (纸本)9781538621950

We present in this paper the design of a large distributed file storage system called Julunga for cloud computing environments. Julunga is designed for federated data center environments where multiple numbers of data centers across the globe interconnect to each other and present a coherent single system view to end-users. In Julunga, the metadata, the namespace and the data blocks of the files are completely distributed with no hard limits on the number of files that can be accommodated in a single directory. Also, there is no physical limit on the size of a file. Julunga supports file of exabytes size with ease along with the number of concurrent users updating, reading and writing to the same file or a directory. The location of the data blocks of a file is determined by using functions, thus expunging the need for file allocation tables. Many new data structures and algorithms were designed earlier where locality and preferences of users are considered to provide optimal storage locations for files and metadata. We elaborate, in this paper, the design of fundamental building blocks of the distributed storage system and compare it with the designs of the earlier file storage systems.

关键词： distributed file storage system distributed metadata cloud computing fault-tolerance load balancing distributed consensus agreement

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