2021 IEEE International Conference on Networking, Architecture and Storage (NAS)最新文献

Deflection-Aware Routing Algorithm in Network on Chip against Soft Errors and Crosstalk Faults 针对软错误和串扰故障的片上网络偏转感知路由算法

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605392

Hadi Zamani, Z. Shirmohammadi, Ali Jahanshahi

引用次数: 0

GO: Out-Of-Core Partitioning of Large Irregular Graphs GO:大型不规则图的out - core Partitioning

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605433

Gurneet Kaur, Rajesh K. Gupta

{"title":"GO: Out-Of-Core Partitioning of Large Irregular Graphs","authors":"Gurneet Kaur, Rajesh K. Gupta","doi":"10.1109/nas51552.2021.9605433","DOIUrl":"https://doi.org/10.1109/nas51552.2021.9605433","url":null,"abstract":"Single-PC, disk-based processing of large irregular graphs has recently gained much popularity. At the core of a disk-based system is a static graph partitioning that must be created before the processing starts. By handling one partition at a time, graphs that do not fit in memory are processed on a single machine. However, the multilevel graph partitioning algorithms used by the most sophisticated partitioners cannot be run on the same machine as their memory requirements far exceed the size of the graph. The popular memory efficient Mt-Metis graph partitioner requires 4.8× to 13.8× the memory needed to hold the entire graph in memory. To overcome this problem, we present the GO out-of-core graph partitioner that can successfully partition large graphs on a single machine. GO performs just two passes over the entire input graph, partition creation pass that creates balanced partitions and partition refinement pass that reduces edgecuts. Both passes function in a memory constrained manner via disk-based processing. GO successfully partitions large graphs for which Mt-Metis runs out of memory. For graphs that can be successfully partitioned by Mt-Metis on a single machine, GO produces balanced 8-way partitions with 11.8× to 76.2× fewer edgecuts using 1.9× to 8.3× less memory in comparable runtime.","PeriodicalId":135930,"journal":{"name":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128731448","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 New PIS Accelerator for Text Searching 一个新的PIS文本搜索加速器

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605387

Yunxin Huang, Aiguo Song, Yafei Yang

引用次数: 0

Design of A Multi-Path Reconfigurable Traffic Monitoring System 一种多路径可重构交通监控系统设计

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605385

Liang-Min Wang, Timothy Miskell, J. Morgan, Edwin Verplanke

{"title":"Design of A Multi-Path Reconfigurable Traffic Monitoring System","authors":"Liang-Min Wang, Timothy Miskell, J. Morgan, Edwin Verplanke","doi":"10.1109/nas51552.2021.9605385","DOIUrl":"https://doi.org/10.1109/nas51552.2021.9605385","url":null,"abstract":"As network bandwidth consumption continues to grow exponentially, real-time traffic data analysis becomes increasingly challenging and expensive. In many cases, network traffic monitoring can only be achieved via hardware Test Access Point (TAP) devices. Due to the intrusiveness and inflexibility of deploying hardware devices, this approach is intractable within an SDN environment where dynamic network resource allocation is key to the orchestration of network services. This paper presents a novel mirror tunnel design to achieve near hardware level TAP-as-a-Service (TaaS) performance through network device mirror offloading, while retaining resource reconfigurability. Mirror tunneling is a hybrid approach whereby a software TAP transports traffic from a source device to a mirror tunnel device. Traffic is then mirrored in place and sent to the destination device. The combination of a software TAP with the mirroring capabilities of the underlying hardware empowers system administrators to create a dynamically reconfigurable multi-path traffic mirroring system. As demonstrated in the benchmark results, this approach is efficient in terms of network bandwidth consumption and computational resources. In addition, this methodology is designed to mirror traffic in high-throughput environments with minimal to no impact on the source Virtual Network Functions (VNFs).","PeriodicalId":135930,"journal":{"name":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129673568","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

Edges: Evenly Distributing Garbage-Collections for Enterprise SSDs via Stochastic Optimization 边缘:基于随机优化的企业ssd均匀分布垃圾收集

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605402

Shuyi Pei, Jing Yang, Bin Li

引用次数: 0

Locality-aware Thread Block Design in Single and Multi-GPU Graph Processing 单gpu和多gpu图形处理中的位置感知线程块设计

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605484

Quan Fan, Zizhong Chen

{"title":"Locality-aware Thread Block Design in Single and Multi-GPU Graph Processing","authors":"Quan Fan, Zizhong Chen","doi":"10.1109/nas51552.2021.9605484","DOIUrl":"https://doi.org/10.1109/nas51552.2021.9605484","url":null,"abstract":"Graphics Processing Unit (GPU) has been adopted to process graphs effectively. Recently, multi-GPU systems are also exploited for greater performance boost. To process graphs on multiple GPUs in parallel, input graphs should be partitioned into parts using partitioning schemes. The partitioning schemes can impact the communication overhead, locality of memory accesses, and further improve the overall performance. We found that both intra-GPU data sharing and inter-GPU communication can be summarized as inter-TB communication. Based on this key idea, we propose a new graph partitioning scheme by redefining the input graph as a TB Graph with calculated vertex and edge weights, and then partition it to reduce intra & inter-GPU communication overhead and improve the locality at the granularity of Thread Blocks (TB). We also propose to develop a partitioning and mapping scheme for heterogeneous architectures including physical links with different bandwidths. The experimental results on graph partitioning show that our scheme is effective to improve the overall performance of the Breadth First Search (BFS) by up to 33%.","PeriodicalId":135930,"journal":{"name":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126465430","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

Machine Learning-based Vulnerability Study of Interpose PUFs as Security Primitives for IoT Networks 基于机器学习的物联网网络安全原语干预puf漏洞研究

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605405

Bipana Thapaliya, Khalid T. Mursi, Yu Zhuang

{"title":"Machine Learning-based Vulnerability Study of Interpose PUFs as Security Primitives for IoT Networks","authors":"Bipana Thapaliya, Khalid T. Mursi, Yu Zhuang","doi":"10.1109/nas51552.2021.9605405","DOIUrl":"https://doi.org/10.1109/nas51552.2021.9605405","url":null,"abstract":"Security is of importance for communication networks, and many network nodes, like sensors and IoT devices, are resource-constrained. Physical Unclonable Functions (PUFs) leverage physical variations of the integrated circuits to produce responses unique to individual circuits and have the potential for delivering security for low-cost networks. But before a PUF can be adopted for security applications, all security vulnerabilities must be discovered. Recently, a new PUF known as Interpose PUF (IPUF) was proposed, which was tested to be secure against reliability-based modeling attacks and machine learning attacks when the attacked IPUF is of small size. A recent study showed IPUFs succumbed to a divide-and-conquer attack, and the attack method requires the position of the interpose bit known to the attacker, a condition that can be easily obfuscated by using a random interpose position. Thus, large IPUFs may still remain secure against all known modeling attacks if the interpose position is unknown to attackers. In this paper, we present a new modeling attack method of IPUFs using multilayer neural networks, and the attack method requires no knowledge of the interpose position. Our attack was tested on simulated IPUFs and silicon IPUFs implemented on FPGAs, and the results showed that many IPUFs which were resilient against existing attacks cannot withstand our new attack method, revealing a new vulnerability of IPUFs by re-defining the boundary between secure and insecure regions in the IPUF parameter space.","PeriodicalId":135930,"journal":{"name":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128812212","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

CALC: A Content-Aware Learning Cache for Storage Systems CALC:存储系统的内容感知学习缓存

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605381

Maher Kachmar, D. Kaeli

{"title":"CALC: A Content-Aware Learning Cache for Storage Systems","authors":"Maher Kachmar, D. Kaeli","doi":"10.1109/nas51552.2021.9605381","DOIUrl":"https://doi.org/10.1109/nas51552.2021.9605381","url":null,"abstract":"In today’s enterprise storage systems, supported services such as data deduplication are becoming a common feature adopted in the data center, especially as new storage technologies mature. Static partitioning of storage system resources, including CPU cores and memory caches, may lead to missing Service Level Agreement (SLAs) thresholds, such as the Data Reduction Rate (DRR) or IO latency. However, typical storage system applications exhibit a workload pattern that can be learned. By learning these pattern, we are better equipped to address several storage system resource partitioning challenges, issues that cannot be overcome with traditional manual tuning and primitive feedback mechanisms.We propose a Content-Aware Learning Cache (CALC) that uses online reinforcement learning models (Q-Learning, SARSA and Actor-Critic) to actively partition the storage system cache between a data digest cache, content cache, and address-based data cache to improve cache hit performance, while maximizing data reduction rates. Using traces from popular storage applications, we show how our machine learning approach is robust and can out-perform an iterative search method for various datasets and cache sizes. Our content-aware learning cache improves hit rates by 7.1% when compared to iterative search methods, and 18.2% when compared to traditional LRU-based data cache implementation.","PeriodicalId":135930,"journal":{"name":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115713822","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

World's #1 CRM Scale Challenges 世界排名第一的CRM规模挑战

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605424

引用次数: 0

Implementation of a High-Throughput Virtual Switch Port Monitoring System 高吞吐量虚拟交换机端口监控系统的实现

2021 IEEE International Conference on Networking, Architecture and Storage (NAS) Pub Date : 2021-10-01 DOI: 10.1109/nas51552.2021.9605360

Liang-Min Wang, Timothy Miskell, Patrick Fu, Cunming Liang, Edwin Verplanke

{"title":"Implementation of a High-Throughput Virtual Switch Port Monitoring System","authors":"Liang-Min Wang, Timothy Miskell, Patrick Fu, Cunming Liang, Edwin Verplanke","doi":"10.1109/nas51552.2021.9605360","DOIUrl":"https://doi.org/10.1109/nas51552.2021.9605360","url":null,"abstract":"As SDN-based networking infrastructure continues to evolve, an increasing number of traditional network functions are deployed over virtualized networks. Similar to fixed function switching networks, traffic monitoring in a Software Defined Network is critical in order to ensure the security and performance of the underlying infrastructure. In the context of virtualized networks, deployment of a virtualized TAP service has been reported as an effective VNF that can provide the same monitoring capabilities as a physical TAP. For most virtual switch implementations, e.g., OvS, network device virtualization is based upon a para-virtualization technology, i.e., VIRTIO. One of the primary use cases for port mirroring is inter-VM communication, i.e., packet streams that exist between virtual network devices, which remains prohibitively expensive for TAP devices. Specifically, it has been observed that virtual TAPs can contribute up to 70% performance degradation to the source VNF(s). With reference to prior work, we previously presented a feasibility study that included a novel approach towards the reduction of port-mirroring overhead. In this paper we present our latest contributions, in which we integrate our design into OvS and develop a VLAN based filtering scheme to pass traffic from a source device to a monitoring device. In this case, both devices may reside either within the same or different switch domains. Furthermore, we present an improvement over RSPAN and discuss its feasibility in delivering mirrored traffic across switch domains, which, in contrast to ERSPAN, does not require an L3 overlay network.","PeriodicalId":135930,"journal":{"name":"2021 IEEE International Conference on Networking, Architecture and Storage (NAS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122813353","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