Page 29 - ITUJournal Future and evolving technologies Volume 2 (2021), Issue 1
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ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 1







               RECONFIGURATION ALGORITHMS FOR HIGH PRECISION COMMUNICATIONS IN TIME
              SENSITIVE NETWORKS: TIME‑AWARE SHAPER CONFIGURATION WITH IEEE 802.1QCC

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           Ahmed Nasrallah , Venkatraman Balasubramanian , Akhilesh S. Thyagaturu , Martin Reisslein , Hesham ElBakoury 5
           1,2,3,4 Arizona State University, School of Electrical, Computer, and Energy Engineering, 650 East Tyler Mall, Tempe, AZ
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             85287‑5706, USA, Intel Corporation, 5000 W. Chandler Blvd., Chandler, AZ 85226, USA, Formerly with Huawei
                       Technologies Co., now Self‑employed, 4211 Norwalk Dr., CC‑102, San Jose, CA 95129, USA
                                   NOTE: Corresponding author: Martin Reisslein, reisslein@asu.edu
          Abstract – As new networking paradigms emerge for different networking applications, e.g., cyber‑physical systems, and
          different services are handled under a converged data link technology, e.g., Ethernet, certain applications with mission critical
          traf ic cannot coexist on the same physical networking infrastructure using traditional Ethernet packet‑switched networking
          protocols. The IEEE 802.1Q Time Sensitive Networking (TSN) Task Group is developing protocol standards to provide de‑
          terministic properties, i.e., eliminates non‑deterministic delays, on Ethernet based packet‑switched networks. In particular,
          the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv, Time‑Aware Shaper (TAS), can be used to
          manage and control Scheduled Traf ic (ST) streams with periodic properties along with Best‑Effort (BE) traf ic on the same
          network infrastructure. We investigate the effects of using the IEEE 802.1Qcc management protocol to accurately and pre‑
          cisely con igure TAS enabled switches (with transmission windows governed by Gate Control Lists (GCLs) with Gate Control
          Entries (GCEs)) ensuring ultra‑low bounded latency, zero packet loss, and minimal jitter for ST TSN traf ic. We examine both
          a centralized network/distributed user model (hybrid model) and a fully‑distributed (decentralized) 802.1Qcc model on a
          typical industrial control network with the goal of maximizing the number of ST streams.
          Keywords – Cyber‑physical systems, low‑latency traf ic, protocol adaptation, recon iguration, Time Sensitive Networking
          (TSN).
          1.  INTRODUCTION                                     added or removed.   Or, nodes may inject additional
                                                               traf ic  lows or traf ic  lows may terminate, or the latency
          1.1 Motivation                                       requirements of  lows may change dynamically. Such
          IEEE 802.1 Time Sensitive Networking (TSN) provides a  dynamic changes have been included in the use cases
          standardized framework of tools for providing determin‑  de ined by the IEC/IEEE 802.1 TSN TG [10, 89]. In a
          istic Ultra‑Low Latency (ULL), e.g., for industrial control  typical industrial environment, sensors that periodically
          applications, automotive networking, smart grid applica‑  or sometimes sporadically send ambient measurements
          tions, and avionics communication systems [11, 22, 30,  to a local gateway require certain Quality of Service
          34, 57, 63, 88]. In particular, the IEEE 802.1Qbv Time  (QoS) guarantees [6,16,31,42,64]. In such a volatile and
          Aware Shaper (TAS) has received extensive attention as  dynamic environment, new machinery that requires pri‑
          a key tool for achieving a deterministic ULL network ser‑  oritized execution (e.g., emergency cooling procedures
          vice. The TAS operation requires careful planning of the  or maintenance tasks for network traf ic tests) may be
          synchronized time cycles [79, 85, 91] and the gate times  brought onto the factory  loor. To deal with such sce‑
          that are allocated to the Scheduled Traf ic (ST) and the  narios, the Time‑Aware Shaper (TAS) Gate Control Lists
          unscheduled Best‑Effort traf ic (BE). The TAS parameter  (GCLs) in coordination with the Network Management
          settings specifying the timing characteristics (cycle time,  Entities (NMEs), e.g., Centralized Network Con iguration
          gate slot allocations) are also commonly referred to as the  (CNC), have to adapt to changing environment conditions
          Qbv schedule or the TAS schedule. For a given static net‑  by judiciously applying recon iguration such that stream
          working scenario, the TAS operation with a properly con‑  deadlines and QoS are satis ied.
           igured Qbv schedule can ensure the deterministic ULL re‑  Generally, in such dynamic networking scenarios, apply‑
          quired by demanding industrial and automotive applica‑  ing only admission control will clearly guarantee (in ac‑
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          tions [8,27,40,59,62,77,84] .                        cordance with a traf ic shaper) the QoS metrics of the ad‑
          Modern network scenarios often involve dynamic       mitted  lows. However, for a given static network con ig‑
          changes with varied use cases, such as changes in the  uration, the total number of admissible streams may be
          network nodes and network topology, or the traf ic   well below the number of streams that seek network ser‑
          pattern. For instance, nodes or links may be dynamically  vice. Therefore, adding a dynamic recon iguration strat‑
                                                               egy to manage and con igure the network appears to be
          1 A preliminary abridged version of this study appeared in the IEEE
          Globecom 2019 workshop paper [60]. This journal article substantially  a plausible and attractive solution that intuitively should
          extends the prior workshop paper, as explained in Section 1.2.  lower capital and operational expenditures as it mitigates





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