Incorporating Queueing Dynamics into Schedule-Driven Traffic Control

Key to the effectiveness of schedule-driven approaches to real-time traffic control is an ability to accurately predict when sensed vehicles will arrive at and pass through the intersection. Prior work in schedule-driven traffic control has assumed a static vehicle arrival model. However, this static predictive model ignores the fact that the queue count and the incurred delay should vary as different partial signal timing schedules (i.e., different possible futures) are explored during the online planning process. In this paper, we propose an alternative arrival time model that incorporates queueing dynamics into this forward search process for a signal timing schedule, to more accurately capture how the intersection’s queues vary over time. As each search state is generated, an incremental queueing delay is dynamically projected for each vehicle. The resulting total queueing delay is then considered in addition to the cumulative delay caused by signal operations. We demonstrate the potential of this approach through microscopic traffic simulation of a real-world road network, showing a 10-15% reduction in average wait times over the schedule-driven traffic signal control system in heavy traffic scenarios.

Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree

Fat-tree networks have many equal-cost redundant paths between two hosts. To achieve low flow completion time and high network utilization in fat-tree, there have been many efforts to exploit topological symmetry. For example, packet scatter schemes, which spray packets across all equal-cost paths relying on topological symmetry, work well when there is no failure in networks. However, when symmetry of a network is disturbed due to a network failure, packet scatter schemes may suffer massive packet reordering. In this paper, we propose a new load balancing scheme named LBSP (Load Balancing based on Symmetric Path groups) for fat-trees. LBSP partitions equal-cost paths into equal sized path groups and assigns a path group to each flow so that packets of a flow are forwarded across paths within the selected path group. When a link failure occurs, the flows affected by the failure are assigned an alternative path group which does not contain the failed link. Consequently, packets in one flow can still experience almost the same queueing delay. Simulation results show that LBSP is more robust to network failures compared to the original packet scatter scheme. We also suggest a solution to the queue length differentials between path groups.

Support for 5G Mission-Critical Applications in Software-Defined IEEE 802.11 Networks

With the emergence of 5G networks and the stringent Quality of Service (QoS) requirements of Mission-Critical Applications (MCAs), co-existing networks are expected to deliver higher-speed connections, enhanced reliability, and lower latency. IEEE 802.11 networks, which co-exist with 5G, continue to be the access choice for indoor networks. However, traditional IEEE 802.11 networks lack sufficient reliability and they have non-deterministic latency. To dynamically control resources in IEEE 802.11 networks, in this paper we propose a delay-aware approach for Medium Access Control (MAC) management via airtime-based network slicing and traffic shaping, as well as user association while using Multi-Criteria Decision Analysis (MCDA). To fulfill the QoS requirements, we use Software-Defined Networking (SDN) for airtime-based network slicing and seamless handovers at the Software-Defined Radio Access Network (SD-RAN), while traffic shaping is done at the Stations (STAs). In addition to throughput, channel utilization, and signal strength, our approach monitors the queueing delay at the Access Points (APs) and uses it for centralized network management. We evaluate our approach in a testbed composed of APs controlled by SD-RAN and SDN controllers, with STAs under different workload combinations. Our results show that, in addition to load balancing flows across APs, our approach avoids the ping-pong effect while enhancing the QoS delivery at runtime. Under varying traffic demands, our approach maintains the queueing delay requirements of 5 ms for most of the experiment run, hence drawing closer to MCA requirements.

Throughput Maximization for full-duplex two-way relay with finite buffers

<div>Optimal queueing control of multi-hop networks remains a challenging problem, e</div><div>specially in two-way relaying systems, even in the most straightforward scenarios.</div><div>In this paper, we explore two-way relaying having a full-duplex decode-and-forward</div><div>relay with two fifinite buffers. Principally, we propose a novel concept based on the</div><div>multi-agent reinforcement learning (that maximizes the cumulative network through</div><div>put) based on the combination of the buffer states and the lossy links; a decision is</div><div>generated as to whether it can transmit, receive or even simultaneously receive and</div><div>transmit information. Towards this objective, chieflfly, based on the queue state transi</div><div>tion and the lossy links, an analytic Markov decision process is proposed to analyze</div><div>this scheme, and the throughput and queueing delay are derived. Our numerical results</div><div>reveal exciting insights. First, artifificial intelligence based on reinforcement learning</div><div>is optimal when the length of the buffer is superior to a certain threshold. Second, we</div><div>demonstrate that reinforcement learning can boost transmission effificiency and prevent</div><div>buffer overflflow.</div>