2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)最新文献_第4页

Design Alternatives for Performance Monitoring Counter based Malware Detection 基于恶意软件检测的性能监控计数器的设计方案

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391559

Jordan Pattee, Byeong Kil Lee

{"title":"Design Alternatives for Performance Monitoring Counter based Malware Detection","authors":"Jordan Pattee, Byeong Kil Lee","doi":"10.1109/IPCCC50635.2020.9391559","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391559","url":null,"abstract":"Hardware-based malware detection is becoming increasingly important as software-based solutions can be easily compromised by attackers. Many of the existing hardware solutions are based on statistical learning blocks with processor behavioral information, which can be captured from the PMC (performance monitoring counters). The performance of the learning techniques relies primarily on the quality of data. However, due to the limited number of PMCs in a processor, only a few behavioral events can be monitored simultaneously. In this paper, we focus on multiple steps to investigate critical issues of PMC based malware detection: (i) statistical characterization of malware; (ii) distribution-based feature selection; (iii) trade-off analysis of complexity and accuracy; and (iv) design alternatives for PMC-based malware detection. Our experimental results show that the proposed detection scheme can provide highly accurate malware detection. As architectural implications, hardware acceleration as well as additional PMC registers are discussed for more accurate malware detection in real-time.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"15 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":"116918055","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

P4SFC: Service Function Chain Offloading with Programmable Switches P4SFC:业务功能链卸载与可编程开关

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391530

Junte Ma, Sihao Xie, Jin Zhao

{"title":"P4SFC: Service Function Chain Offloading with Programmable Switches","authors":"Junte Ma, Sihao Xie, Jin Zhao","doi":"10.1109/IPCCC50635.2020.9391530","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391530","url":null,"abstract":"A Service Function Chain (SFC) is an ordered sequence of network functions (NFs). Software-based NFs in Network Function Virtualization (NFV) could introduce significant performance overhead. In this paper, we present P4SFC, a high-performance SFC system that leverages P4-capable switches to accelerate packet processing by offloading proper NFs to the switches. First, considering the current limitations of P4, we analyze the offloadability of NFs and divide them into three categories: fully offloadable, partially offloadable, and non-offloadable. Second, when deploying new SFCs, P4SFC automatically offloads proper NFs to switches based on their position and offloadability. To deploy new SFCs at runtime, we design a dynamic P4 data plane, whose execution logic can be reconfigured at runtime without interrupting the current execution logic. Finally, to maintain state consistency between the server and the switch for partially offloaded NFs, we design a state library to automatically synchronize states between servers and switches. Experimental results show that P4SFC achieves significant performance improvement for real-world SFCs.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"72 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":"117120300","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: 5

Network Resource Scheduling For Cloud/Edge Data Centers 云/边缘数据中心网络资源调度

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391529

Yuhan Zhao, Wei Zhang, Meihong Yang, Huiling Shi

{"title":"Network Resource Scheduling For Cloud/Edge Data Centers","authors":"Yuhan Zhao, Wei Zhang, Meihong Yang, Huiling Shi","doi":"10.1109/IPCCC50635.2020.9391529","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391529","url":null,"abstract":"The cloud-edge integration service model combines the advantages of computing capabilities both from cloud and edge. Therefore, the data centers with cloud-edge integrated are an irreversible trend for the evolution of future data center. Software-Defined Network (SDN), emerging as a novel network model, separates content forwarding and control, and that makes resource management across data center network more efficient. This article focuses on the network data transmission and management for future data centers. First, it reviews measurement, analysis, and monitoring methods meant for new features of global SDN network. Then it focuses on unified management of SDN resources like traffic scheduling theory for cross-domain data centers based on cloud. Specifically, we proposed a novel fault response mechanism across the network with a more precise location and less response time. With dynamic changes of cloud computing and edge computing services combined, global QoS control and QoE optimization methods are proposed correspondingly. Finally, a set of SDN control platforms supporting the functions mentioned above are formulated. We hope that our work will shed some new light and provide new theoretical support for cloud-edge-combined cross-domain data center network architecture.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"1 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":"129614626","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: 2

A Multimodal Feature Fusion-Based Method for Individual Depression Detection on Sina Weibo 基于多模态特征融合的新浪微博个体抑郁检测方法

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391501

Yiding Wang, Zhenyi Wang, Chenghao Li, Yilin Zhang, Haizhou Wang

{"title":"A Multimodal Feature Fusion-Based Method for Individual Depression Detection on Sina Weibo","authors":"Yiding Wang, Zhenyi Wang, Chenghao Li, Yilin Zhang, Haizhou Wang","doi":"10.1109/IPCCC50635.2020.9391501","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391501","url":null,"abstract":"Existing studies have shown that various types of information on the online social network (OSN) can help predict the early stage of depression. However, studies using machine learning methods to accomplish depression detection tasks still do not have high classification performance, suggesting that there is much potential for improvement in their feature engineering. In this paper, we first construct a dataset on Sina Weibo (a leading OSN with the largest number of active users in the Chinese community), namely the Weibo User Depression Detection Dataset (WU3D). It includes more than 10,000 depressed users and 20,000 normal users, both of which are manually labeled and rechecked by specialists. Then, we extract text-based word features using the popular pretrained model XLNet and summarize nine statistical features related to user text, social behavior, and pictures. Moreover, we construct a deep neural network classification model, i.e. Multimodal Feature Fusion Network (MFFN), to fuse the above-extracted features from different information sources and further accomplish the classification task. The experimental results show that our approach achieves an F1-Score of 0.9685 on the test dataset, which has a good performance improvement compared to the existing works. In addition, we verify that our multimodal detecting approach is more robust than multimodel ensemble ones. Our work could also provide new research methods for depression detection on other OSN platforms.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"146 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":"133213210","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: 13

REPROOF: Quantifying the Jam Resistance of REBUF refproof:量化reff的抗干扰性

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391560

Joshua Groen, P. Howell, M.D. Collins

引用次数: 0

Efficient Architecture Paradigm for Deep Learning Inference as a Service 深度学习推理即服务的高效架构范式

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391551

Jin Yu, Xiaopeng Ke, Fengyuan Xu, Hao Huang

{"title":"Efficient Architecture Paradigm for Deep Learning Inference as a Service","authors":"Jin Yu, Xiaopeng Ke, Fengyuan Xu, Hao Huang","doi":"10.1109/IPCCC50635.2020.9391551","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391551","url":null,"abstract":"Deep learning (DL) inference has been broadly used and shown excellent performance in many intelligent applications. Unfortunately, the high resource consumption and training efforts of sophisticated models present obstacles for regular users to enjoy it. Thus, Deep Learning Inference as a Service (DIaaS), offering online inference services on cloud, has earned great popularity among cloud tenants who can send their DIaaS inputs via RPCs across the internal network. However, such detached architecture paradigm is inappropriate to DIaaS because the high-dimensional inputs of DIaaS consume a lot of precious internal bandwidth and the service latency of DIaaS has to be low and stable. We therefore propose a novel architecture paradigm on cloud for DIaaS in order to address the above two problems without giving up the security and maintenance benefits. We first leverage the SGX technology, a strongly-protected user space enclave, to bring DIaaS computation to its input source as close as possible, i.e. co-locating a cloud tenant and its subscribed DIaaS in the same virtual machine. When the GPU acceleration is needed, we migrate this virtual machine to any available GPU host and transparently utilize the GPU via our backend computing stack installed on it. In this way the majority of internal bandwidth is saved compared to traditional paradigm. Furthermore, we greatly improve the efficiency of the proposed architecture paradigm, from the computation and I/O perspectives, by making the entire data flow more DL-oriented. Finally, we implement a prototype system and evaluate it in real-world scenarios. The experiments show that our locality-aware architecture achieves the average single CPU (GPU) based deep learning inference time 2.84X (4.87X) less than the traditional detached architecture on average.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"46 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":"127238031","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

Authentication Based on Blockchain 基于区块链的认证方式

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391553

Norah Alilwit

引用次数: 3

Distributed Scheduling Algorithm for Optimizing Age of Information in Wireless Networks 无线网络信息时代优化的分布式调度算法

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391518

Dongxiao Yu, Xinpeng Duan, Feng Li, Yi Liang, Huan Yang, Jiguo Yu

{"title":"Distributed Scheduling Algorithm for Optimizing Age of Information in Wireless Networks","authors":"Dongxiao Yu, Xinpeng Duan, Feng Li, Yi Liang, Huan Yang, Jiguo Yu","doi":"10.1109/IPCCC50635.2020.9391518","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391518","url":null,"abstract":"Age of Information (AoI) is an emerging concept to model information freshness from the perspective of destinations of information deliveries. Serving as a metric to characterize the data delivery timeliness, the peak age indicates the maximum value of the AoI prior to a data packet reception. In this paper, we present a distributed scheduling algorithm for peak age optimization in a wireless network where a number of sensor nodes attempt to deliver their sensed data to a data collector over a wireless channel. In particular, each sensor node accesses the channel for data delivery independently according to an adaptively tuned transmission probability. The beauty of our algorithm lies in that, even with neither centralized infrastructure nor coordinations among the sensor nodes, our algorithm asymptotically approximates the optimal solution by only a constant factor. We perform solid theoretical analysis and extensive simulations to verify the efficacy of our algorithm. To the best of our knowledge, it is the first fully distributed scheduling algorithm for AoI optimization in wireless networks.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"109 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":"122887852","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

Modeling Energy Consumption of Virtual Machines in DVFS-Enabled Cloud Data Centers 支持dvfs的云数据中心虚拟机能耗建模

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391552

Jianzhou Mao, T. Bhattacharya, Xiaopu Peng, T. Cao, X. Qin

引用次数: 1

Research on Security Estimation and Control of Cyber-Physical System 网络物理系统安全评估与控制研究

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391573

Xiaobo Cai, Ke Han, Yan Li, Huihui Wang, Jiajin Zhang, Yue Zhang

引用次数: 2