Proceedings of the 2021 on Systems and Network Telemetry and Analytics最新文献

Session details: Keynote 2 会议详情:主题演讲2

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3240508.3286918

Jinoh Kim

引用次数: 0

Characterizing Resource Heterogeneity in Edge Devices for Deep Learning Inferences 基于深度学习推理的边缘设备资源异构特征

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3452411.3464446

Jianwei Hao, Piyush Subedi, I. Kim, Lakshmish Ramaswamy

{"title":"Characterizing Resource Heterogeneity in Edge Devices for Deep Learning Inferences","authors":"Jianwei Hao, Piyush Subedi, I. Kim, Lakshmish Ramaswamy","doi":"10.1145/3452411.3464446","DOIUrl":"https://doi.org/10.1145/3452411.3464446","url":null,"abstract":"Significant advances in hardware capabilities and the availability of enormous data sets have led to the rise and penetration of artificial intelligence (AI) and deep learning (DL) in various domains. Considerable efforts have been put forth in academia and industry to make these computationally demanding DL tasks work on resource-constrained edge devices. However, performing DL tasks on edge devices is still challenging due to the diversity of DNN (Deep Neural Networks) architectures and heterogeneity of edge devices. This study evaluates and characterizes the performance and resource heterogeneity in various edge devices for performing DL tasks. We benchmark various DNN models for image classification on a set of edge devices ranging from the widely popular and relatively less powerful Raspberry Pi to GPU-equipped high-performance edge devices like Jetson Xavier NX. We also compare and contrast the performance of three widely-used DL frameworks when used in these edge devices. We report DL inference throughput, CPU and memory usage, power consumption, and frameworks' initialization overhead, which are the most critical factors for characterizing DL tasks on edge devices. Additionally, we provide our insights and findings, which will provide a better idea of how compatible or feasible edge devices are for running DL applications.","PeriodicalId":339207,"journal":{"name":"Proceedings of the 2021 on Systems and Network Telemetry and Analytics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127086203","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

Session details: Technical Session 1 会议详情:技术会议1

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3470766

M. Cafaro

引用次数: 0

INODE - Intelligence Open Data Exploration 智能开放数据探索

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3452411.3464448

Kurt Stockinger

引用次数: 0

A Hybrid Virtual Network Function Placement Strategy for Maximizing the Profit of Network Service Deployment over Dynamic Workload 动态负载下网络服务部署利润最大化的混合虚拟网络功能布局策略

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3452411.3464440

Chi-Chen Yang, J. Chou

引用次数: 0

Analyzing Scientific Data Sharing Patterns for In-network Data Caching 网络内数据缓存科学数据共享模式分析

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3452411.3464441

Elizabeth Copps, Huiyi Zhang, A. Sim, Kesheng Wu, I. Monga, C. Guok, F. Würthwein, Diego Davila, E. Hernandez

{"title":"Analyzing Scientific Data Sharing Patterns for In-network Data Caching","authors":"Elizabeth Copps, Huiyi Zhang, A. Sim, Kesheng Wu, I. Monga, C. Guok, F. Würthwein, Diego Davila, E. Hernandez","doi":"10.1145/3452411.3464441","DOIUrl":"https://doi.org/10.1145/3452411.3464441","url":null,"abstract":"The volume of data moving through a network increases with new scientific experiments and simulations. Network bandwidth requirements also increase proportionally to deliver data within a certain time frame. We observe that a significant portion of the popular dataset is transferred multiple times to different users as well as to the same user for various reasons. In-network data caching for the shared data has shown to reduce the redundant data transfers and consequently save network traffic volume. In addition, overall application performance is expected to improve with in-network caching because access to the locally cached data results in lower latency. This paper shows how much data was shared over the study period, how much network traffic volume was consequently saved, and how much the temporary in-network caching increased the scientific application performance. It also analyzes data access patterns in applications and the impacts of caching nodes on the regional data repository. From the results, we observed that the network bandwidth demand was reduced by nearly a factor of 3 over the study period.","PeriodicalId":339207,"journal":{"name":"Proceedings of the 2021 on Systems and Network Telemetry and Analytics","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121438033","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: 4

Session details: Technical Session 2 会议详情:技术会议2

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3470768

Jinoh Kim

引用次数: 0

Recent Advances and Future Challenges for Network Function Virtualization Infrastructure 网络功能虚拟化基础设施的最新进展和未来挑战

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3452411.3464449

J. Chou

{"title":"Recent Advances and Future Challenges for Network Function Virtualization Infrastructure","authors":"J. Chou","doi":"10.1145/3452411.3464449","DOIUrl":"https://doi.org/10.1145/3452411.3464449","url":null,"abstract":"Today's enterprise networks has revolutionized by the emerging technology called Network function virtualization (NFV), which is a type of data center network architecture proposed by the European Telecommunications Standards Institute (ETSI). NFV uses virtualization techniques to implement various Network Functions (NFs) like firewall, load balancer etc. from dedicated network devices to virtualized instances in commodity servers. This virtualized instance is called as Virtual Network Function (VNF). The purpose of VNF is to process NFs in order to accomplish a specific task. Traditionally these NFs were implemented on dedicated network equipment called middleboxes. Although these middleboxes are capable of processing heavy workloads, they are expensive, inflexible and require experts to maintain them. Therefore, NFV has the potential to substitute these middleboxes with virtualized instances in cloud datacenters, and hence greatly reduces the Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) of networks by making it cheaper, flexible and scalable. This talk will share the recent advances and future challenges on how to build the infrastructure for hosting and managing NFVs. In particularly, we will focus on two of the most important topics in this research direction. First is the VNF placement problem, which aims to find the best mapping decision between VNF instances and physical resources. It has significant impact to the network operation cost, and service quality, but it is also known to be a NP-hard problem. So the problem has been actively studied by the research community. The second topic is Cloud-native/Container Network Function (CNF), which aims to minimize the overhead of traditional virtualization technique for network function using container-based technologies. Hence, it has drawn growing interests from industry to build the NFV infrastructure for CNF, but many new challenges remain to be addressed and studied.","PeriodicalId":339207,"journal":{"name":"Proceedings of the 2021 on Systems and Network Telemetry and Analytics","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130134925","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

GPU-based Classification for Wireless Intrusion Detection 基于gpu的无线入侵检测分类

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3452411.3464445

A. Lazar, A. Sim, Kesheng Wu

引用次数: 3

Access Patterns to Disk Cache for Large Scientific Archive 大型科学档案磁盘缓存的访问模式

Proceedings of the 2021 on Systems and Network Telemetry and Analytics Pub Date : 2020-06-21 DOI: 10.1145/3452411.3464444

Yumeng Wang, Kesheng Wu, A. Sim, Shinjae Yoo, S. Misawa

引用次数: 1