Time-sensitive Streams Research Articles

Real-Time Reconfiguration of Time-Aware Shaper for ULL Transmission in Dynamic Conditions

Many applications such as Industrial Internet of Things (IIoT), tactile Internet, and 5G/6G mobile require Ultra-Low Latency (ULL) in data transmission. The end-to-end latency is required to be on the order of sub-millisecond in these applications. The IEEE 802.1 Time Sensitive Networking (TSN) standards have been developed to provide ULL networking. The IEEE 802.1Qbv Time-Aware Shaper (TAS), which is a typical flow control mechanism of TSN, is a traffic shaper to provide deterministic end-to-end ULL transmission for express traffic. The Gate Control List (GCL) of all TAS enabled nodes must be configured in a coordinated manner to ensure ULL. However, existing configuration schemes cannot be employed in highly dynamic conditions where the distribution of time-sensitive streams frequently changes. It will be a significant problem to deal with such dynamic conditions in the future. Therefore, this paper proposes a real-time adaptive gate scheduling scheme for TAS. The scheduling problem is formulated as a boolean satisfiability problem (SAT), and the employment of an FPGA-based solver is proposed to achieve runtime fast computation. The proposed scheme enables real-time reconfiguration for high flexibility and high bandwidth utilization to deal with the dynamics of network conditions. The feasibility of the proposed scheme is confirmed with computer simulations.

Time-Sensitive Networking: An Introduction

Ethernet is cheap and ubiquitous. As such, people want to use it to carry all sorts of traffic for which it was not originally intended. A popular current application area is the transport of multiple flows of data, each having different timing requirements. Such applications exist in professional audio, industrial and automotive networks, among others. This article briefly traces the history of the features of IEEE 802 Bridging intended to address those needs, and then describes recent advances in time-sensitive networking in more detail. As well as completed standards, some current projects are described. Areas for future standardization are identified. Encoding the priority of packets in the header allows high-priority packets to be scheduled for transmission ahead of lower-priority packets, providing a better quality of service for urgent traffic. Time-sensitive flows have varying requirements for maximum latency and latency variation. Audio-video bridging provides guaranteed quality of service in terms of those parameters, for booked traffic in a bridged network comprising only compliant bridges. Some types of flow (particularly in industrial networks) are very sensitive to packet loss. Time-sensitive networking can provide bounded latency and zero packet loss due to congestion. The most stringent guarantees and most efficient use of network resources is provided by cyclic queuing and forwarding, which combines time synchronization, transmission scheduling and per-stream filtering and policing to provide just-in-time delivery of time-sensitive streams. This requires careful planning and centralized control. Less stringent use cases allow use of distributed control techniques.

Efficient frequent itemset mining methods over time-sensitive streams

Stream data arrives dynamically and rapidly, and the characteristics cannot be reflected by the traditional transaction-based sliding window; thus, the mining results are inaccurate. This paper focuses on this problem and constructs a timestamp-based sliding window model, which can be further converted into a transaction-based sliding window. Based on this model, an extended enumeration tree is developed to incrementally maintain the essential information. In our proposed frequent itemset mining algorithm, we introduce the type transforming bound to dynamically classify the itemsets into categories; thus, certain itemset processing can be deferred or ignored, that is, an itemset will not be handled unless its type transforming bounds reach a threshold; as a result, the computational pruning can be conducted. Nevertheless, it only guarantees the conditions to obtain accurate results, and thus cannot achieve the best performance. This problem is further improved in our approximate mining algorithm, in which we propose a heuristic rule-based strategy. Additionally, it can save more computational cost with a tolerable mining error. Theoretical analysis and experimental studies demonstrate that our proposed algorithms have high accuracy, spend less computational time and memory, and significantly outperform the baseline method and state-of-the-art algorithms.

Enabling Data-Intensive Biomedical Science: Gaps, Opportunities, and Challenges

OMICS: A Journal of Integrative BiologyVol. 15, No. 4 Letters to the EditorEnabling Data-Intensive Biomedical Science: Gaps, Opportunities, and ChallengesDavid E. Hudak, Don Stredney, Prasad Calyam, Kevin Wohlever, Thomas Bitterman, Bradley Hittle, Thomas Kerwin, and Ashok KrishnamurthyDavid E. HudakSearch for more papers by this author, Don StredneySearch for more papers by this author, Prasad CalyamSearch for more papers by this author, Kevin WohleverSearch for more papers by this author, Thomas BittermanSearch for more papers by this author, Bradley HittleSearch for more papers by this author, Thomas KerwinSearch for more papers by this author, and Ashok KrishnamurthySearch for more papers by this authorPublished Online:8 Apr 2011https://doi.org/10.1089/omi.2011.0017AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 15Issue 4Apr 2011 InformationCopyright 2011, Mary Ann Liebert, Inc.To cite this article:David E. Hudak, Don Stredney, Prasad Calyam, Kevin Wohlever, Thomas Bitterman, Bradley Hittle, Thomas Kerwin, and Ashok Krishnamurthy.Enabling Data-Intensive Biomedical Science: Gaps, Opportunities, and Challenges.OMICS: A Journal of Integrative Biology.Apr 2011.231-233.http://doi.org/10.1089/omi.2011.0017Published in Volume: 15 Issue 4: April 8, 2011PDF download