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Load balancing and resource management in distributed B5G networks 16

Load balancing and resource management in distributed B5G ne...

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16th IEEE international conference on Mobility, Sensing and networking (MSN)

作者： Ma, Lu Chang, Weiling Li, Chao Ni, Shanjin Cui, Jia Liu, Ming Natl Comp Network Emergency Response Tech Team Coordinat Ctr China Beijing 100029 Peoples R China

ISBN: (纸本)9781728199160

The traffic explosion and the rising of diverse requirements lead to many challenges for the mobile networks on flexibility, scalability, and deployability. To meet the challenges, end-to-end network slicing and mobile edge computing (MEC) are introduced into beyond fifth-generation (B5G) mobile communication networks. However, the edge servers are limited by computing and data processing capabilities, how to implement efficient load balancing and resource management in data center are still challenges for B5G network slices. In this paper, we first propose a model of load balancing for B5G network slices and formulate a total cost minimization problem, considering the energy cost, processing delay, backhaul bandwidth cost, and revenue loss associated with the backhaul delay. Then, a distributed algorithm based on Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) is proposed to solve the formulated problem. Simulations are conducted to show that the proposed algorithm are effective in terms of reducing network operating cost.

关键词： B5G networks load balancing recourse optimization network slicing ADMM

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Secure distributed Data Management for Fog computing in Large-Scale IoT Application: A Blockchain-based Solution

Secure Distributed Data Management for Fog Computing in Larg...

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IEEE international conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Chen, Zunming Cui, Hongyan Wu, Ensen Li, Yuanxin Xi, Yu Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Adv Informat Networks Beijing Peoples R China

ISBN: (纸本)9781728174402

Fog computing, a novel computing paradigm, migrates cloud computing to the edge of network and consequently decrease the overhead of process and movement of the large-scale big data. Fog computing is promising to lower communication delay and offload network traffic with the scale of big data increasing in large-scale IoT application, which brings micro data centers to the network edge, enabling the network edge to perceive, process, storage and calculate the massive data, reducing the amount of transferred data. However, numerous fog nodes at the network edge face an external attack resulting in serious security threats arising from the trusting relationship vulnerability because of the lack of the consideration of the properties of the fog computing system. This paper addresses the design for developing secure distributed data management platform for fog computing in large-scale IoT application, along with a blockchain-based data management implementation of the platform, which approaches the major challenges: how to realize the integration of data security and storage management for fog computing in large-scale IoT application and enrich rational interoperability for interconnected things. Experimental results show that the system works well to empower data provenance and transparency and defend against unauthorized access effectively in fog computing, scales well with loss of the performance of communication and computing maintaining in acceptable range, verifying the efficiency and feasibility of our design to provide privacy, integrity and fine-grained data management for fog computing in large-scale IoT application by introducing the data management platform based upon the blockchain network and smart contracts therein.

关键词： Secure Data Management Blockchain IoT Application Fog computing

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Scaling distributed Deep Learning Workloads beyond the Memory Capacity with KARMA

Scaling Distributed Deep Learning Workloads beyond the Memor...

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international conference on High Performance computing, networking, Storage and Analysis (SC)

作者： Wahib, Mohamed Zhang, Haoyu Truong Thao Nguyen Drozd, Aleksandr Domke, Jenes Zhang, Lingqi Takano, Ryousei Matsuoka, Satoshi Natl Inst Adv Ind Sci & Technol Tokyo Japan MiHoYo Inc Shanghai Peoples R China Tokyo Inst Technol Tokyo Japan RIKEN Ctr Computat Sci Kobe Hyogo Japan

ISBN: (纸本)9781728199986

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： Deep Neural Networks Out-of-core GPUs

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NetSheriff: Sheltering Software-Defined Networks from Rogue Switches 1

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8th international conference on Networked Systems (NETYS)

作者： Laffranchini, Paolo Miranda, Joao Machado, Nuno Rodrigues, Luis Riviere, Etienne Sadre, Ramin ULisboa INESC ID IST Lisbon Portugal UCLouvain ICTEAM Louvain La Neuve Belgium Teradata San Diego CA USA

ISBN: (数字)9783030670870

ISBN: (纸本)9783030670863;9783030670870

We present NetSheriff - a system to automatically isolate faulty switches in Software-Defined Networks. To pinpoint the devices responsible for network misbehaviors, NetSheriff performs a differential analysis between expected paths of packets (obtained from a formal model of the network forwarding specification) and the corresponding observed paths taken by flows (obtained through network monitoring). We have built a prototype of NetSheriff supporting both OpenFlow and P4 Programmable devices and evaluated it on different network topologies, simulating real traffic behavior following recent data center studies. Our results show that NetSheriff is able to accurately identify the switch(es) responsible for different types of errors.

关键词： Software defined networking

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A Blockchain-Based IoT Data Secure Vickery Auction System 8th

A Blockchain-Based IoT Data Secure Vickery Auction System

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8th international conference on Artificial Intelligence and Security , ICAIS 2022

作者： Wang, Haohui Chen, Xiubo Ahmad, Haseeb Xu, Gang Yang, Yixian Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad37610 Pakistan School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9783031067631

Traditional centralized architectures for IoT data suffer from single points of failure in terms of data storage and access control, and IoT data does not fully realize its value as a digital asset. In response to the above challenges, we propose a decentralized storage and lightweight access control method for IoT data based on blockchain technology, and put forward an auction system to commercialize the IoT data. Firstly, a blockchain-based data storage method proposed using Inter Planetary File System (IPFS), which ensures the data is immutable and traceable. Secondly, in this paper, we propose a distributed lightweight Capability-Based Access Control (CapBAC) based on blockchain, in which a time dimension is added so that no additional token revocation operation is required. Thus, lightweight access control is achieved under IoT devices with limited computing power. Finally, we come up with a blockchain-based Vickery auction system to sell the data to the highest bidder in demand. This auction effectively protects the privacy of bidders and does not require third-party intermediary fees. And it can ensure fairness and non-repudiation of the auction. By analyzing the entire system process, we guarantee secure storage of IoT data. Besides, we can achieve lightweight access control and realize the value of the data based on the blockchain technology. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Access control

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Proceedings of MLHPC 2019: 5th Workshop on Machine Learning in HPC Environments - Held in conjunction with SC 2019: The international conference for High Performance computing, networking, Storage and Analysis

Proceedings of MLHPC 2019: 5th Workshop on Machine Learning ...

引用

5th IEEE/ACM Workshop on Machine Learning in HPC Environments, MLHPC 2019

ISBN: (纸本)9781728159850

The proceedings contain 8 papers. The topics discussed include: scalable hyperparameter optimization with lazy Gaussian processes;understanding scalability and fine-grain parallelism of synchronous data parallel training;DisCo: physics-based unsupervised discovery of coherent structures in spatiotemporal systems;GradVis: visualization and second order analysis of optimization surfaces during the training of deep neural networks;metaoptimization on a distributed system for deep reinforcement learning;scheduling optimization of parallel linear algebra algorithms using supervised learning;parallel data-local training for optimizing Word2Vec embeddings for word and graph embeddings;and fine-grained exploitation of mixed precision for faster CNN training.

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distributed ADMM with Synergetic Communication and Computation

Distributed ADMM with Synergetic Communication and Computati...

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2020 international conference on computing, networking and Communications, ICNC 2020

作者： Tian, Zhuojun Zhang, Zhaoyang Yan, Jiamei Wang, Jue College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Hangzhou China

ISBN: (数字)9781728149059

ISBN: (纸本)9781728149059

distributed alternating direction method of multipliers (ADMM) is a widely used algorithm for large-scale optimization problems due to its fast convergence rate. However, most studies implement the distributed ADMM by communicating with all neighbor nodes, which assumably ignores the communication cost. In this paper, we propose a novel distributed ADMM algorithm with synergetic communication and computation, called SCCD-ADMM, as an attempt to reduce the total cost of the system. Specifically, each node searches the number of communication nodes considering both the communication and computation cost, chooses a subset of its neighbor nodes to receive messages from and then updates the local variables using a newly designed update rule, to attain proper trade-off between the costs of communication and computation. Numerical experiments validate that total cost can be greatly saved in SCCD-ADMM compared with the traditional distributed ADMM, especially in the case where the communication is costly. © 2020 IEEE.

关键词： Economic and social effects

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NetANNS: A High-Performance distributed Search Framework Based On In-Network computing

NetANNS: A High-Performance Distributed Search Framework Bas...

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

作者： Penghao Zhang Heng Pan Zhenyu Li Gaogang Xie Penglai Cui Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Purple Mountain Laboratories China Computer Network Information Center Chinese Academy of Sciences China

Approximate nearest neighbor search (ANNS) is the most basic and important algorithm in Database, Machine Learning and other applications. With the expansion of cloud computing, the academia focuses on the study of how to optimize distributed frameworks based on approximate nearest neighbor search such as MapReduce, and Memcached. We implement a new distributed ANNS search framework (NetANNS). The main contributions of NetANNS are to accelerate the data preprocessing with programmable switch, and integrate a variety of efficient ANNS algorithms so that it can choose the most suitable algorithm for each datasets. The experiments show that the search efficiency of NetANNS is about 2x than the common distributed ANNS frameworks which are implemented based on the framework of MapReduce.

关键词： Cloud computing Machine learning algorithms Data preprocessing Switches Nearest neighbor methods Approximation algorithms Database machines

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New Architecture Design of Cloud computing Using Software Defined networking and Network Function Virtualization Technology 4th

New Architecture Design of Cloud Computing Using Software De...

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4th international conference of Reliable Information and Communication Technology (IRICT)

作者： Bahashwan, Abdullah Ahmed Anbar, Mohammed Abdullah, Nibras Univ Sains Malaysia USM Natl Adv IPv6 Ctr Nav6 Gelugor 11800 Penang Malaysia

ISBN: (纸本)9783030335823;9783030335816

Cloud computing is a new technology model that consists of a pool of configurable resource and multiple data centers in distributed areas. It offers a service over the internet for users using virtual data, and who share resources for storing and accessing data from anywhere. Cloud computing supports big-scale platforms that consist of thousands of servers and hundreds of switches. Currently, the cloud is suffering from many issues in term of management, security and other essential aspects. This paper discusses integrating Software Defined networking (SDN), with Network Function Virtualization (NFV) based on cloud computing, to eliminate some of the existing security issues faced by cloud computing. This research paper proposes a new architecture design by taking advantage of the NFV technology to build a virtual security mechanism such as Network Intrusion Detection System (NIDS).

关键词： Software-defined networking (SDN) Network Function Virtualization (NFV) Based in Cloud computing

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IMpart: A Partitioning-based Parallel Approach to Accelerate Influence Maximization

IMpart: A Partitioning-based Parallel Approach to Accelerate...

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international conference on High Performance computing

作者： Reet Barik Marco Minutoli Mahantesh Halappanavar Ananth Kalyanaraman Washington State University Pullman WA Pacific Northwest National Laboratory Richland WA

Influence maximization (IM) is a fundamental operation among graph problems that involve simulating a stochastic diffusion process on real-world networks. Given a graph G(V, E), the objective is to identify a small set of key influential "seeds"— i.e., a fixed-size set of k nodes, which when influenced is likely to lead to the maximum number of nodes in the network getting influenced. The problem has numerous applications including (but not limited to) viral marketing in social networks, epidemic control in contact networks, and in finding influential proteins in molecular networks. Despite its importance, application of influence maximization at scale continues to pose significant challenges. While the problem is NP-hard, efficient approximation algorithms that use greedy hill climbing are used in practice. However those algorithms consume hours of multithreaded execution time even on modest-sized inputs with hundreds of thousands of nodes. In this paper, we present IMpart, a partitioning-based approach to accelerate greedy hill climbing based IM approaches on both shared and distributed memory computers. In particular, we present two parallel algorithms— one that uses graph partitioning (IMpart-metis) and another that uses community-aware partitioning (IMpart-gratis)— with provable guarantees on the quality of approximation. Experimental results show that our approaches are able to deliver two to three orders of magnitude speedup over a state-of-the-art multithreaded hill climbing implementation with negligible loss in quality. For instance, on one of the modest-sized inputs (Slashdot: 73K nodes; 905K edges), our partitioning-based shared memory implementation yields 4610× speedup, reducing the runtime from 9h 36m to 7 seconds on 128 threads. Furthermore, our distributed memory implementation enhances problem size reach to graph inputs with ×10 6 nodes and ×10 8 edges and enables sub-minute computation of IM solutions.

关键词： Proteins Privacy Runtime Social networking (online) Instruction sets Pipelines Memory management

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