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AID: Efficient Prediction of Aggregated Intensity of Dependency in Large-scale Cloud Systems 36

AID: Efficient Prediction of Aggregated Intensity of Depende...

引用

36th IEEE/ACM international conference on Automated Software Engineering (ASE)

作者： Yang, Tianyi Shen, Jiacheng Su, Yuxin Ling, Xiao Yang, Yongqiang Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Huawei Cloud BU Comp & Networking Innovat Lab Shenzhen Peoples R China

ISBN: (纸本)9781665403375

Service reliability is one of the key challenges that cloud providers have to deal with. In cloud systems, unplanned service failures may cause severe cascading impacts on their dependent services, deteriorating customer satisfaction. Predicting the cascading impacts accurately and efficiently is critical to the operation and maintenance of cloud systems. Existing approaches identify whether one service depends on another via distributed tracing but no prior work focused on discriminating to what extent the dependency between cloud services is. In this paper, we survey the outages and the procedure for failure diagnosis in two cloud providers to motivate the definition of the intensity of dependency. We define the intensity of dependency between two services as how much the status of the callee service influences the caller service. Then we propose AID, the first approach to predict the intensity of dependencies between cloud services. AID first generates a set of candidate dependency pairs from the spans. AID then represents the status of each cloud service with a multivariate time series aggregated from the spans. With the representation of services, AID calculates the similarities between the statuses of the caller and the callee of each candidate pair. Finally, AID aggregates the similarities to produce a unified value as the intensity of the dependency. We evaluate AID on the data collected from an open-source microservice benchmark and a cloud system in production. The experimental results show that AID can efficiently and accurately predict the intensity of dependencies. We further demonstrate the usefulness of our method in a large-scale commercial cloud system.

关键词： cloud computing software reliability AIOps service dependency

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Parallel Asynchronous Stochastic Dual Coordinate Descent Algorithms for High Efficiency and Stable Convergence

Parallel Asynchronous Stochastic Dual Coordinate Descent Alg...

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Euromicro conference on Parallel, distributed and Network-Based Processing

作者： Yung-Chen Chen Pangfeng Liu Jan-Jan Wu The Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan National Taiwan University Taipei Taiwan Academia Sinica Institute of Information Science Taipei Taiwan

The following topics are dealt with: multiprocessing systems; parallel processing; learning (artificial intelligence); multi-threading; data handling; computational complexity; microprocessor chips; graph theory; reso... 详细信息

ISBN: (纸本)9781665414555;9781665447645

关键词： Support vector machines Machine learning algorithms Instruction sets Scalability Stochastic processes Training data Convergence

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Cost-aware & Fault-tolerant Geo-distributed Edge computing for Low-latency Stream Processing

Cost-aware & Fault-tolerant Geo-distributed Edge Computing f...

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international conference on Collaborative computing: networking, Applications and Worksharing (CollaborateCom)

作者： Jinlai Xu Balaji Palanisamy School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (纸本)9781665416269

The number of Internet-of-Things (IoT) devices is rapidly increasing with the growth of IoT applications in various domains. As IoT applications have a strong demand for low latency and high throughput computing, stream processing using edge computing resources is a promising approach to support low latency processing of large-scale data. Edge-based stream processing extends the capability of cloud-based stream processing by processing the data streams near the edge of the network. In this vision paper, we discuss a distributed stream processing framework that optimizes the performance of stream processing applications through a careful allocation of geo-distributed computing and network resources available in edge computing environments. The framework includes key optimizations in both the platform layer and the infrastructure layer. While the platform layer is responsible for converting the user program into a stream processing physical plan and optimizing the physical plan and operator placement, the infrastructure layer is responsible for provisioning geo-distributed resources to the platform layer. The framework optimizes the performance of stream query processing at the platform layer through its careful consideration of data locality and resource constraints during physical plan generation and operator placement and by incorporating resilience to deal with failures. The framework also includes techniques to dynamically determine the level of parallelism to adapt to changing workload conditions. At the infrastructure layer, the framework includes a novel model for allocating computing resources in edge and geo-distributed cloud computing environments by carefully considering latency and cost. End users benefit from the platform through reduced cost and improved user experience in terms of response time and latency.

关键词： Cloud computing Costs Throughput User experience Internet of Things Time factors Resource management

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Optimization of key and value storage in vector map rendering

Optimization of key and value storage in vector map renderin...

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

作者： Feng Jing Ying Jiang Shengbin Chen Xirong Lv Jiayi He State Grid Shanxi Electric Power Company Taiyuan China Xiamen Great Power Geo Information Technology Company Ltd Xiamen China School of Computer and Information Engineering Xiamen University of Technology Xiamen China

With the continuous development of data-centric Internet applications, data management solutions for massive data have gradually become a new research direction in vector map rendering. The introduction of distributed "cloud computing" technology has continuously improved the storage and management of massive data. Supported by a large number of distributed systems, it provides normal services to the outside world, as well as fast data location services. The system has good performance in data storage efficiency, node load balancing and system scalability, and has an efficient data positioning mechanism. The basic idea of the system design draws on the hash algorithm and the consistent hash algorithm, and improves the scalability of the basic algorithm. Through the three-tier layout: front-end server layer, consistent hash layer, data node layer, to maximize the consistency, availability, efficiency and scalability of the system.

关键词： Cloud computing Scalability Layout Memory distributed databases Systems architecture Rendering (computer graphics)

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The Influence-Maximizing Node Selection Algorithm Based on Local Isolated Centrality in Large-scale networks Implemented by distributed Graph computing

The Influence-Maximizing Node Selection Algorithm Based on L...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Piaoyang Chen Rong Lei Mingsen Deng School of Information Guizhou University of Finance and Economics Guiyang China

Identifying influence-maximizing (IM) nodes is an effective strategy for designing efficient communication networks or contain the spreading of information for rumors and epidemics with limited budgets. With the growing of the sizes of the social networks, finding solutions to the problem of intfuence − maximizing vertices becomes more and more challenging. Tra-ditionally, the number of users finally affected was the focus. However, the information diffusion speed was becoming a bottle-neck of IM problems. In this paper, we proposed a locality-aware vertex selection strategy based on local isolation centrality (LIC), while the information diffusion experiments used the Independent Cascade (IC) model. The computing scheme was implemented by a distributed graph computing framework. The superiority of the LIC algorithm was confirmed by evaluated on three real-world datasets. It was indistinguishable from algorithms such as K-shell, H-index, degree centrality (DC), local centrality (LC), and VoteRank at the diffusion cascade scale. But it showed better superiority in diffusion speed. More importantly, by comparing LIC with the VoteRank algorithm, which was the closest algorithm on diffusion speed to that of LIC, the huge advantage of the LIC algorithm in terms of time cost was found. We believe that the locality-aware LIC algorithm was more suitable for the influence-maximization problem in very large-scale social networks.

关键词： Epidemics Costs Social networking (online) Computational modeling Communication networks Integrated circuit modeling

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Anonymous and Coercion-Resistant distributed Electronic Voting

Anonymous and Coercion-Resistant Distributed Electronic Voti...

引用

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

作者： Zaghloul, Ehab Li, Tongtong Ren, Jian

ISBN: (纸本)9781728149059

Electronic voting (e-voting) presents a convenient and cost-effective alternative to current paper ballot-based voting. It provides many benefits such as increased voter turnout and accuracy in the decision-making process. While presenting many improvements, e-voting still faces serious security challenges that hinder its adoption. In this paper, we propose a novel remote e-voting scheme that can enhance the integrity, efficiency, and voter turnout of the election process. Our proposed work is secure and preserves the privacy of voters through secure multi-party computations performed by parties of differing allegiances. It also leverages a blockchain running smart contracts as a publicly accessible and tamper-resistant bulletin board to permanently store votes and prevent double-voting. In our security and privacy analysis, we show that our proposed scheme is secure against potential threats and provides voter anonymity. Our performance discussion shows that the proposed scheme is practical for large-scale elections. Index Terms - Remote e-voting, blockchain, smart contract. © 2020 IEEE.

关键词： Decision making

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Application of Perceptual Features for User Authentication in distributed Systems 22nd

Application of Perceptual Features for User Authentication i...

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11th international conference on Intelligent networking and Collaborative Systems (INCoS) / 22nd international conference on Network-Based Information Systems (NBiS)

作者： Ogiela, Marek R. Ogiela, Lidia AGH Univ Sci & Technol Cryptog & Cognit Informat Res Grp 30 Mickiewicza Ave PL-30059 Krakow Poland Pedag Univ Krakow Dept Cryptog & Cognit Informat Podchorazych 2 St PL-30084 Krakow Poland

ISBN: (纸本)9783030290290;9783030290283

This paper will present selected approaches based on cognitive solutions for creation security procedures and cryptographic protocols. In particular will be described the ways of using perceptual and cognitive features in creation of secure protocols, oriented on user authentication and data management. The new areas of cognitive cryptography will be also described, which offer a new possibilities in security areas oriented on application of perceptual features and personal characteristics.

关键词： Cognitive authentication Security protocols Cryptographic protocols

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distributed Denial of Service (DDoS) Attacks Detection System for OpenStack-based Private Cloud

Distributed Denial of Service (DDoS) Attacks Detection Syste...

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international conference on Computational Intelligence and Data Science (ICCIDS)

作者： Virupakshar, Karan B. Asundi, Manjunath Channal, Kishor Shettar, Pooja Patil, Somashekar Narayan, D. G. KLE Technol Univ Hubballi Karnataka India

Cloud computing technique is commonly used by fast growing Internet-based applications. Moving to cloud computing results in reducing the cost of managing and maintaining IT infrastructure. Different organizations controls the cloud resources over internet using networking protocols and standards. This makes IT infrastructure distributed in nature but controlled centrally, which opens the door to the attackers for intrusions. Attacks as distributed Denial of Service (DDoS) is one of the most popular intrusions in private cloud which cause a degradation of services being reduced or denial of services. In this work the sole focus is on DDoS attack which specifically targeted towards detection of bandwidth flooding and connection flooding. Such attacks target the network layer of the cloud set up with invalid requests and make it denial to legitimate requests. Thus, the entire cloud set up becomes vulnerable and can be disrupted by these DDoS attacks. To overcome this a cloud operating system which has an integrated firewall with DDoS detection system is required. Here a system with OpenStack integrated firewall and raw socket programming for monitoring the network traffic is proposed. Based on the dataset generated in controlled DDoS attack environment, algorithms such as Decision tree, K nearest neighbor (KNN), Naive Bayes and Deep Neural Network (DNN) algorithms are compared against the trained model. Eventually, DDoS attacks are detected, and administrator of the private cloud is notified. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Cloud distributed Denial of Service (DDoS) OpenStack Raw Sockets K nearest neighbor Deep Neural Network

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MetaWBC: POSIX-compliant metadata write-back caching for distributed file systems 22

MetaWBC: POSIX-compliant metadata write-back caching for dis...

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Proceedings of the international conference on High Performance computing, networking, Storage and Analysis

作者： Yingjin Qian Wen Cheng Lingfang Zeng Marc-André Vef Oleg Drokin Andreas Dilger Shuichi Ihara Wusheng Zhang Yang Wang André Brinkmann Data Direct Networks (DDN) China Huazhong University of Science and Technology (HUST) China Zhejiang Lab China Johannes Gutenberg University Mainz Germany Whamcloud Inc Whamcloud Inc Canada Data Direct Networks (DDN) Japan Tsinghua University China Chinese Academy of Sciences China

In parallel and distributed file systems, caching can improve data performance and metadata operations. Currently, most distributed file systems adopt a write-back data cache for performance and a write-through metadata cache for simplifying consistency. However, with modern file systems scales and workloads, write-through metadata caching can impact overall file system performance, e.g., through lock contention and heavy RPC loads required for namespace synchronization and transaction *** paper proposes a novel metadata write-back caching (MetaWBC) mechanism to improve the performance of metadata operations in distributed environments. To achieve extreme metadata performance, we developed a fast, lightweight, and POSIX-compatible memory file system as a metadata cache. Further, we designed a file caching state machine and included other performance optimizations. We coupled MetaWBC with Lustre and evaluated that MetaWBC can outperform the native parallel file system by up to 8x for metadata-intensive benchmarks, and up to 7x for realistic workloads in throughput.

关键词： file system data-intensive application write-back caching lustre metadata

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Trade less Accuracy for Fairness and Trade-off Explanation for GNN

Trade less Accuracy for Fairness and Trade-off Explanation f...

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IEEE international conference on Big Data

作者： Yazheng Liu Xi Zhang Sihong Xie Key Laboratory of Trustworthy Distributed Computing and Service (MoE) BUPT Computer Science and Engineering Dept Lehigh University

ISBN: (纸本)9781665480468

Graphs are widely found in social network analysis and e-commerce, where Graph Neural Networks (GNNs) are the state-of the-art model. GNNs can be biased due to sensitive attributes and network topology. With existing work that learns a fair node representation or adjacency matrix, achieving a strong guarantee of group fairness while preserving prediction accuracy is still challenging, with the fairness-accuracy trade-off remaining obscure to human decision-makers. We first define and analyze a novel upper bound of group fairness to optimize the adjacency matrix for fairness without significantly h arming prediction accuracy. To understand the nuance of fairness-accuracy tradeoff, we further propose macroscopic and microscopic explanation methods to reveal the trade-offs and the space that one can exploit. The macroscopic explanation method is based on stratified sampling and linear programming to deterministically explain the dynamics of the group fairness and prediction accuracy. Driving down to the microscopic level, we propose a path-based explanation that reveals how network topology leads to the tradeoff. On seven graph datasets, we demonstrate the novel upper bound can achieve more efficient fairness-accuracy trade-offs and the intuitiveness of the explanation methods can clearly pinpoint where the trade-off is improved.

关键词： Training Upper bound Network topology Social networking (online) Microscopy Big Data Linear programming

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