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Load Balance Awared Data Sharing Systems In Heterogeneous Edge Environment

Load Balance Awared Data Sharing Systems In Heterogeneous Ed...

引用

international conference on Parallel and distributed Systems (ICPADS)

作者： Sheng Chen Zheng Chen Siyuan Gu Baochao Chen Junjie Xie Deke Guo Colledge of Intelligence and Computing Tianjin University Tianjin P.R. China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha Hunan P.R. China AMS PLA Institute of Systems Engineering Beijing P.R. China

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Edge computing has become the de facto method for delay-sensitive applications, in which the computation and storage resources are placed at the edge of network. The main responsibility of edge computing is to carry data from the Cloud downlinks and terminal uplinks, and organize these data well on the edge side. This is the basis for subsequent analysis and processing of data. Therefore, this brings about a question as to how those data should be organized on the edge and how to store and retrieve them. In response to this demand, some methods have been proposed to solve the related problems of how to build data storage and retrieval services on the edge side. Those methods propose three different solutions: structured, unstructured, and hybrid schemes. However, the data storage and retrieval services for the heterogeneous edge environment is still lack of research. It is still not considered an important design test load balancing when the data is stored on the edge side. In this paper, we design and implement w-strategy, a load balance approach that implements the appropriate load balance among the heterogeneous edge nodes by using the weighted Voronoi diagram. Our solution utilizes the software defined networking paradigm to support a virtual-space based distributed hash tables (DHTs) to distribute data. Evaluation results show that w-strategy achieves better load balancing among the heterogeneous edge nodes compared to the existing methods, GRED and Chord. And, the w-strategy improves the average underutilization of the resources by 20%.

关键词： Simulation Memory Load management Resource management Uplink Software defined networking Edge computing

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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 Workshops, ICC

作者： Zunming Chen Hongyan Cui Ensen Wu Yuanxin Li Yu Xi State Key Lab. of Networking and Switching Technology Beijing University of Posts and Telecommunications China Beijing Key Lab. of Advanced Information Networks Beijing University of Posts and Telecommunications China

ISBN: (数字)9781728174402

ISBN: (纸本)9781728174419

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： distributed databases Edge computing Authorization Authentication

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ACM international conference Proceeding Series

ACM International Conference Proceeding Series

引用

20th international conference on distributed computing and networking, ICDCN 2019

ISBN: (纸本)9781450360944

The proceedings contain 78 papers. The topics discussed include: the weakest failure detector to solve the mutual exclusion problem in an unknown dynamic environment;multi-tenant mobile offloading systems for real-time computer vision applications;distributed symmetry-breaking with improved vertex-averaged complexity;parallel algorithms for predicate detection;improving efficacy of concurrent internal binary search trees using local recovery;session guarantees with raft and hybrid logical clocks;reconfigurable dataflow graphs for processing-in-memory;on the hardness of the strongly dependent decision problem;and cache attacks on blockchain based information centric networks: an experimental evaluation.

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distributed Hierarchical File Systems strike back in the Cloud

Distributed Hierarchical File Systems strike back in the Clo...

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international conference on distributed computing Systems

作者： Mahmoud Ismail Salman Niazi Mauritz Sundell Mikael Ronström Seif Haridi Jim Dowling KTH - Royal Institute of Technology Logical Clocks AB Oracle AB

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Cloud service providers have aligned on availability zones as an important unit of failure and replication for storage systems. An availability zone (AZ) has independent power, networking, and cooling systems and consists of one or more data centers. Multiple AZs in close geographic proximity form a region that can support replicated low latency storage services that can survive the failure of one or more AZs. Recent reductions in inter-AZ latency have made synchronous replication protocols increasingly viable, instead of traditional quorum-based replication protocols. We introduce HopsFS-CL, a distributed hierarchical file system with support for high-availability (HA) across AZs, backed by AZ-aware synchronously replicated metadata and AZ-aware block replication. HopsFS-CL is a redesign of HopsFS, a version of HDFS with distributed metadata, and its design involved making replication protocols and block placement protocols AZ-aware at all layers of its stack: the metadata serving, the metadata storage, and block storage layers. In experiments on a real-world workload from Spotify, we show that HopsFS-CL, deployed in HA mode over 3 AZs, reaches 1.66 million ops/s, and has similar performance to HopsFS when deployed in a single AZ, while preserving the same semantics.

关键词： Cloud computing Protocols File systems Semantics Metadata Throughput Optimization

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Intelligent Eco networking (IEN) II: A Knowledge-Driven Future Internet Infrastructure for Value-Oriented Ecosystem 2

Intelligent Eco Networking (IEN) II: A Knowledge-Driven Futu...

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2nd IEEE international conference on Hot Information-Centric networking, HotICN 2019

作者： Lei, Kai Huang, Shuokang Huang, Jiyue Liu, Huafang Liu, Jin Peking University Shenzhen518055 China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781728157818

Intelligent Eco networking (IEN) is intent on evolving the IP-based network architecture into a Knowledge-Driven Future Internet Infrastructure based on hierarchical heterogeneous networks composed of virtualized and configurable devices, taking the valuable contents as first-class entity, d riven by knowledge intelligence, integrating the decentralized consensus trust preservation, to establish a value-oriented social ecosystem. To counter with the complicated but inefficient mechanisms of IP, a tokenized fine-grained allocation mechanism i s proposed, taking comprehensive consideration of storage, computing and networking, bringing about a collaborative and mutually beneficial value-interconnection. Knowledge-Driven Scheme (KDS), as the guideline of IEN's establishment, illustrates the way to extract knowledge from the network, the value consensus of Blockchain, and the feedback control of Federated Learning, making economy-based distributed commerce feasible. © 2019 IEEE.

关键词： Commerce

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A Learning Approach with Programmable Data Plane towards IoT Security

A Learning Approach with Programmable Data Plane towards IoT...

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international conference on distributed computing Systems

作者： Qiaofeng Qin Konstantinos Poularakis Leandros Tassiulas Institute for Network Science Yale University USA

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

Security threats arising in massively connected Internet of Things (IoT) devices have attracted wide attention. It is necessary to equip IoT gateways with firewalls to prevent hacked devices from infecting a larger amount of network nodes. The match-and-action mechanism of Software Defined networking (SDN) provides the means to differentiate malicious traffic flows from normal ones, which mirrors the past firewall mechanisms but with a new flexible and dynamically reconfigurable twist. However, vulnerabilities of IoT devices and heterogeneous protocols coexisting in the same network challenge the extension of SDN into the IoT domain. To overcome these challenges, we leverage the high level of data plane programmability brought by the P4 language and design a novel two-stage deep learning method for attack detection tailored to that particular language. Our method is able to generate flow rules that match a small number of header fields from arbitrary protocols while maintaining high performance of attack detection. Evaluations using network traces of different IoT protocols show significant benefits in accuracy, efficiency and universality over state-of-the-art methods.

关键词： Performance evaluation Protocols Intrusion detection Logic gates Throughput Security Software defined networking

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A simple 2(1 −1) factor distributed approximation algorithm for Steiner tree in the CONGEST model 20

A simple 2(1 −1) factor distributed approximation algorithm...

引用

20th international conference on distributed computing and networking, ICDCN 2019

作者： Saikia, Parikshit Karmakar, Sushanta Department of Computer Science and Engineering Indian Institute of Technology Guwahati781039 India

ISBN: (纸本)9781450360944

The Steiner tree problem is a classical and fundamental problem in combinatorial optimization. The best known deterministic distributed algorithm for the Steiner tree problem in the CON GEST model was proposed by Lenzen and Patt-Shamir [25] that constructs a Steiner tree whose cost is optimal upto a factor of 2 and the round complexity is Õ (S + √min{St,n}) for a graph of n nodes and t terminals, where S is the shortest path diameter of the graph. Note here that the Õ (·) notation hides polylogarithmic factors in n. In this paper we present a simple deterministic distributed algorithm for constructing a Steiner tree in the CON GEST model with an approximation factor 2(1 − 1 ) of the optimal where is the number of terminal leaf nodes in the optimal Steiner tree. The round complexity of our algorithm is O(S + √n log∗ n) and the message complexity is O(∆(n − t)S + n3/2), where ∆ is the maximum degree of a vertex in the graph. Our algorithm is based on the computation of a sub-graph called the shortest path forest for which we present a separate deterministic distributed algorithm with round and message complexities of O(S) and O(∆(n − t)S) respectively. © 2019 Association for computing Machinery.

关键词： Combinatorial optimization

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Multi-task Deep Reinforcement Learning for Scalable Parallel Task Scheduling

Multi-task Deep Reinforcement Learning for Scalable Parallel...

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IEEE international conference on Big Data (Big Data)

作者： Zhang, Lingxin Qi, Qi Wang, Jingyu Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781728108582

The rapid development of artificial intelligence in multiple scenarios, including machine learning, image recognition, and autonomous driving, has led to an explosion of computation jobs. These jobs are often divided into parallel child tasks and executed in distributed clusters with limited computing resources, making parallel task scheduling one of the most important research topics nowadays. Most studies about parallel task scheduling focused on formulating special scenarios and service requirements as optimization problems. However, the complicated and dynamic parallel computing environments are hard to model, predict and control, making those previous methods unscalable and unable to reflect the real scenarios. In this paper, a Multi-task Deep reinforcement learning approach for scalable parallel Task Scheduling (MDTS) is firstly devised. Generally, Deep Reinforcement Learning (DRL) is a modelfree optimization algorithm for long-term control by leveraging experience, but it suffers the curse of dimensionality for decision when coping with complex parallel computing environments and jobs with diverse properties. We extend the action selection in DRL to a multi-task decision, where the output branches of multitask learning are fine-matched to parallel scheduling tasks. Child tasks of a job are accordingly assigned to distributed nodes without any human knowledge while the resource competition among parallel tasks is leveraged through shared neural network layers. Extensive experiments show that the MDTS significantly reduces the job execution lime compared with least-connection scheduling and particle swarm optimization algorithm by 153% and 39.8% respectively. Moreover, MDTS outperforms the raw DRL algorithm on job execution time, load imbalance value, and total cost by 42.8%, 47.5%, and 59.0%.

关键词： parallel task scheduling deep reinforcement learning multi-task learning

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Impact of Hardware Impairments on the Performance of Millimeter-Wave Massive MU-MIMO Systems with distributed Antennas

Impact of Hardware Impairments on the Performance of Millime...

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international conference on Communications and networking, ComNet

作者： Widad Belaoura Khalida Ghanem Mohummed Zeeshan Shakir Khalid Qaraqe Ecole nationale polytechnique (ENP) Algiers Algeria System Architecture Laboratory Center of development of advanced techniques (CDTA) Algiers Algeria School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley Scotland UK Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728153209

ISBN: (纸本)9781728153216

The impact of hardware impairments play an crucial role in any practical communication systems, yet they are generally omitted when investigating the performance of distributed massive multiple input multiple output (MIMO) systems. In particularly, for millimeter-Wave (mm-Wave) bands, no study has been addressed yet. To overcome this limitation, in this paper a mm-Wave distributed massive multi-user (MU) MIMO system based-hybrid beamforming structure, in which the residual hardware impairments have been incorporated in the transmitter processing, is presented. At each base station (BS), a zero-forcing (ZF) baseband processing is designed over the effective channel to efficiently remove the multi-user (MU) interference. The results show that, relative to the co-located mm-Wave massive MIMO with the same antenna configurations, mm-Wave distributed MIMO achieves a significantly higher performance while reducing the impingement of hardware practical defects.

关键词： Array signal processing Transmitting antennas Massive MIMO Hardware Millimeter wave communication Antenna arrays Signal to noise ratio

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An Autonomous Food Wastage Control Warehouse: distributed Ledger and Machine Learning based Approach

An Autonomous Food Wastage Control Warehouse: Distributed Le...

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international conference on computing and networking Technology (ICCNT)

作者： Nikish Kumar S.V. Saiharsha Balasubramaniam Sanjay Tharagesh R.S. Priyanka Kumar B. Janavi Amrita School of Engineering Amrita Vishwa Vidyapeetham India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Food is an essential aspect of our lives. Back in those days, people consumed seasonal fruits and vegetables which were available locally and on a daily basis. But things have changed when refrigeration units were invented. People were now able to store and consume food products which were nonseasonal also. In today's world, warehouses and cold chains are of striking importance because most of the food products that we see in our supermarkets and grocery stores come from them. Due to improper management of food products these warehouses have become an epicenter for food wastage. Since most of the traditional warehouses are manual or RDBMS based, they can be easily tampered. With these considerations in this paper, we have proposed an autonomous warehouse system which will tackle food wastage by using Machine Learning and Blockchain technology.

关键词： Food products Blockchain Machine learning Computer science Chemicals Biological neural networks Ventilation

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