2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)最新文献_第3页

Budgeted Persuasion on User Opinions via Varying Susceptibility 通过不同敏感性对用户意见进行预算说服

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391549

Wenyi Tang, Xinrui Xu, Guangchun Luo, Zaobo He, Kaiming Zhan

{"title":"Budgeted Persuasion on User Opinions via Varying Susceptibility","authors":"Wenyi Tang, Xinrui Xu, Guangchun Luo, Zaobo He, Kaiming Zhan","doi":"10.1109/IPCCC50635.2020.9391549","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391549","url":null,"abstract":"Nowadays, the social network becomes an indispensable part of people’s daily life, meanwhile offers an unprecedentedly convenient access for purposive individuals to influence the opinions of network users. Current studies present a subtle persuasion approach that finds a number of key users meanwhile varies their susceptibility extent to impact the public opinion. Such persuasion is significantly critical for public security, as it could facilitate both the spreading and dispelling of malicious rumors. However, the major body of these studies enclose impractical assumptions, such that persuaders have an unlimited budget, or the costs of varying different users’ susceptibilities are the same, thus rendering these works unsuitable for realistic scenarios. Therefore, this work originally proposes a more practical and generalized problem of persuasion, where varying the susceptibilities of different users holds different costs. The analysis of its non-convexity, non-submodularity and complexity shows that solving the proposed problem is nontrivial, thus inspiring us to provide an intuitive greedy algorithm. Furthermore, we design an accelerated algorithm based on the community property, which reduces the time consumption more than one order of magnitude. The acceleration is based on the intuition that the impact of a user within a proper community could be a good estimation of the impact in the whole network, while the computation of the former one is much more efficient. The relationship between two algorithms is fully analyzed, which shows the community-based algorithm can degenerate to the intuitive greedy algorithm under a specific setting. Finally, comprehensive evaluations on real-world datasets show the superiority of proposed algorithms on both effectiveness and efficiency.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132321736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Supporting Efficient Dynamic Update in Public Integrity Verification of Cloud Data 支持云数据公共完整性验证的高效动态更新

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391514

Jia Wan, Shijie Jia, Limin Liu, Yang Zhang

{"title":"Supporting Efficient Dynamic Update in Public Integrity Verification of Cloud Data","authors":"Jia Wan, Shijie Jia, Limin Liu, Yang Zhang","doi":"10.1109/IPCCC50635.2020.9391514","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391514","url":null,"abstract":"Cloud storage is an increasingly popular service of cloud computing, which can provide convenient on-demand data outsourcing services and release the burden of maintaining local data for both individuals and organizations. However, the cloud service providers are not fully trusted by the users. The reason is that the users lose physical control of their cloud data, and the cloud service providers may conceal the status of the data when encountering data loss accidents for reputation. Therefore, it is critical for users to efficiently verify the integrity of cloud data.In this paper, we propose a public integrity verification scheme to support efficient dynamic update of cloud data based on Merkle Hash Tree linked list (MHT-list), which is a novel two-dimensional data structure we designed. This structure utilizes multiple merkle hash trees (MHTs) and a linked list to record data information at the cloud service provider side. Meanwhile, we exploit the structural advantages of the MHT-list to make our scheme more efficient in dynamic update and integrity verification than existing works. Moreover, we formally prove the security of the proposed scheme and evaluate the performance of our scheme by concrete extensive experiments. The results demonstrate that our proposed scheme achieves dynamic update effectively in public integrity verification of cloud data, and outperforms the previous works in computation and communication overhead.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114319812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Practical Application of IoT for Large-scale Instruments and Equipment Sharing Management Platform 物联网在大型仪器设备共享管理平台中的实际应用

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391543

Guangjun Shi, Liang Liu, Fu Chen

引用次数: 1

Deploying Network Key-Value SSDs to Disaggregate Resources in Big Data Processing Frameworks 部署网络键值ssd，实现大数据处理框架下的资源分解

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391532

Mahsa Bayati, Harsh Roogi, Ron Lee, N. Mi

{"title":"Deploying Network Key-Value SSDs to Disaggregate Resources in Big Data Processing Frameworks","authors":"Mahsa Bayati, Harsh Roogi, Ron Lee, N. Mi","doi":"10.1109/IPCCC50635.2020.9391532","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391532","url":null,"abstract":"The exponential data generation embraces unstructured object storage systems as an effective solution to improve performance. Key-Value (KV) SSD object storage devices are unveiled to mitigate the shortcomings of traditional Key-Value stores on block devices, including device low-bandwidth utilization and KV-store resource-draining operations on the host CPU and block devices. Samsung KV-SSDs are built on top of NVMe over Fabric hardware, which supports storage remote access protocols (i.e., RDMA). Network Key-Value (NKV) is a software eco-system developed by Samsung that enables data distribution and storage disaggregation of KV-SSDs. Most widely used big data processing platforms, such as Hadoop, Presto, deploy Hadoop Distributed File System (HDFS) to take advantage of rapid data access by co-locating storage and compute nodes. The co-allocation of compute and storage node limits the scalability and utilization resources and thus increases the total cost of ownership. In this paper, we present a new storage disaggregation model for big data processing platforms. Our new system layout leverages resource disaggregation by separating compute infrastructure from storage infrastructure and utilizes the benefits of new evolving storage technology, i.e., KV-SSD, for large-scale data access and processing. The goal of this work is to facilitate independent scaling of storage and compute resources, and shift the data retrieval load from the hosts to storage nodes. We evaluate our designed architecture using TPC-DS benchmark. Our results show that the CPU load on compute nodes is non-negligibly released with sustaining the same performance compared to the conventional Hadoop with HDFS.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115418203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance and Consistency Analysis for Distributed Deep Learning Applications 分布式深度学习应用的性能和一致性分析

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391566

Danlin Jia, M. Saha, J. Bhimani, N. Mi

{"title":"Performance and Consistency Analysis for Distributed Deep Learning Applications","authors":"Danlin Jia, M. Saha, J. Bhimani, N. Mi","doi":"10.1109/IPCCC50635.2020.9391566","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391566","url":null,"abstract":"Accelerating the training of Deep Neural Network (DNN) models is very important for successfully using deep learning techniques in fields like computer vision and speech recognition. Distributed frameworks help to speed up the training process for large DNN models and datasets. Plenty of works have been done to improve model accuracy and training efficiency, based on mathematical analysis of computations in the Con-volutional Neural Networks (CNN). However, to run distributed deep learning applications in the real world, users and developers need to consider the impacts of system resource distribution. In this work, we deploy a real distributed deep learning cluster with multiple virtual machines. We conduct an in-depth analysis to understand the impacts of system configurations, distribution typologies, and application parameters, on the latency and correctness of the distributed deep learning applications. We analyze the performance diversity under different model consistency and data parallelism by profiling run-time system utilization and tracking application activities. Based on our observations and analysis, we develop design guidelines for accelerating distributed deep-learning training on virtualized environments.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129150891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Recognition of Identification Schemes for IoT Identifiers via Sequence-to-Sequence Model 基于序列到序列模型的物联网标识符识别方案自动识别

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391548

Xiaotao Li, Shujuan You, Wai Chen

{"title":"Automatic Recognition of Identification Schemes for IoT Identifiers via Sequence-to-Sequence Model","authors":"Xiaotao Li, Shujuan You, Wai Chen","doi":"10.1109/IPCCC50635.2020.9391548","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391548","url":null,"abstract":"In Internet of Things (IoT), each object requires a unique identifier to identify itself and index its detailed profile to support mutual recognitions among multiple objects. However, existing IoT identifiers belonging to different identification schemes are heterogeneous from each other, which create a great challenge for the applications that need to resolve the heterogeneous identifiers. To address this challenge, we propose an algorithm to automatically recognize the heterogeneous identification schemes used by various IoT identifiers, based on a sequence-to-sequence (seq2seq) model consisting of an encoder and a decoder. The encoder uses one Long Short-Term Memory (LSTM) to map the identifier sequence to a vector of fixed dimensionality, and the decoder uses another LSTM to unfold the vector into a target sequence representing the identification scheme of this identifier. To evaluate our algorithm, we create a new dataset named ID-20 with 20 categories of IoT identifiers and conduct experiments on it. The results demonstrate the superiority of our algorithm against other state-of-the-art methods, with an identifier recognition accuracy of up to 94.57%.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122047234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experiments with Non-Cooperative Space DTN Routing 非合作空间DTN路由实验

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391546

R. Lent

{"title":"Experiments with Non-Cooperative Space DTN Routing","authors":"R. Lent","doi":"10.1109/IPCCC50635.2020.9391546","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391546","url":null,"abstract":"In this paper, results from an experimental study of the end-to-end bundle delivery performance of two concurrent flows transmitted over a delay-tolerant network are presented. The study compares the Contact Graph Routing algorithm, which is the foundation of the CCSDS standard Schedule Aware Bundle Routing and the Cognitive Space Gateway that has been recently introduced as a cognitive networking alternative to the problem of space bundle routing. Both algorithms make non-cooperative, dynamic routing decisions for bundles at each step based on a similar utility, which is formulated as the minimum expected bundle delivery time to the destination. The study aims to find how well each algorithm plays the routing game under different conditions. The experiments were carried out on a laboratory testbed with emulated Earth-Moon communication conditions and included evaluation cases with a permanently connected substrate and a substrate that is being affected by regular link disruptions. Also, the flows were evaluated with cases where their shortest path either overlap or not. The results indicate that both routing approaches play a coherent game achieving fair performance for both flows, but with the CSG achieving better performance than the CGR approach.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"74 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130866696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Website Recommendation with Side Information Aided Variational Autoencoder 网站推荐与侧信息辅助变分自编码器

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391524

Pinhao Wang, Wenzhong Li, Zepeng Yu, Baoguo Lu, Sanglu Lu

{"title":"Website Recommendation with Side Information Aided Variational Autoencoder","authors":"Pinhao Wang, Wenzhong Li, Zepeng Yu, Baoguo Lu, Sanglu Lu","doi":"10.1109/IPCCC50635.2020.9391524","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391524","url":null,"abstract":"Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer; identifying movies or music that a user will find interest, etc. However, the existing recommendation approaches mainly focus on capturing user-item interaction patterns for prediction, and ignore the user’s side information such as visit frequency and duration. In this paper, we study the side information aided website recommendation problem that using the browsing history of a set of users and their side information to predict the websites that will be of interest to a certain user. We propose a novel recommendation approach called SI-VAE that incorporates side information with the variational autoencoders (VAEs) model for top-k recommendation. The proposed method takes both user-website interaction information and side information as input, and adopts an encoder/decoder model to generate user’s interested websites from partial observations. The model of SI-VAE is implemented as a neural network, and trained with a multinomial likelihood objective function to form the ranking of user-website interaction probabilities. We conduct extensive experiments on two real-world datasets, which show that the proposed model outperforms the baselines in a number of performance metrics in website recommendation.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131745063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Efficient arithmetic expression optimization with weighted adjoint matrix 带加权伴随矩阵的高效算法表达式优化

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391519

Xianhua Liu, Chun Yang, Zixin Guan

引用次数: 0

Adaptive Video Streaming via Deep Reinforcement Learning from User Trajectory Preferences 基于用户轨迹偏好的深度强化学习自适应视频流

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391533

Qingyu Xiao, Jin Ye, Chengjie Pang, Liangdi Ma, Wenchao Jiang

引用次数: 0