Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

The mechanism of adapting RED parameters to TCP traffic

Random Early Detection (RED) can stabilize the queue within a given target range and thus achieve high throughput in the routers. However, the average queue size is quite sensitive to network scenarios and it is difficult to adapt RED parameters to changing network traffic. In this paper we use a previously developed dynamic model of TCP behavior and linear feedback model of TCP/RED to analyze and design a mechanism for RED parameter tuning in response to changing network conditions like traffic load, link capacity and round-trip time. Even though the values of four key RED parameters are determined by varying network conditions, they can be tuned independently without consideration of the interactions among these RED parameters. Simulation results show that this mechanism can stabilize the queue and maintain high link utilization in a wide variety of network conditions

Hybrid static/dynamic scheduling for already optimized dense matrix factorization

We present the use of a hybrid static/dynamic scheduling strategy of the task dependency graph for direct methods used in dense numerical linear algebra. This strategy provides a balance of data locality, load balance, and low dequeue overhead. We show that the usage of this scheduling in communication avoiding dense factorization leads to significant performance gains. On a 48 core AMD Opteron NUMA machine, our experiments show that we can achieve up to 64% improvement over a version of CALU that uses fully dynamic scheduling, and up to 30% improvement over the version of CALU that uses fully static scheduling. On a 16-core Intel Xeon machine, our hybrid static/dynamic scheduling approach is up to 8% faster than the version of CALU that uses a fully static scheduling or fully dynamic scheduling. Our algorithm leads to speedups over the corresponding routines for computing LU factorization in well known libraries. On the 48 core AMD NUMA machine, our best implementation is up to 110% faster than MKL, while on the 16 core Intel Xeon machine, it is up to 82% faster than MKL. Our approach also shows significant speedups compared with PLASMA on both of these systems

Application Layer Feedback-based SIP Server Overload Control

A SIP server may be overloaded by emergency-induced call volume, "American Idol" style flash crowd effects or denial of service attacks. The SIP server overload problem is interesting especially because the costs of serving or rejecting a SIP session can be similar. For this reason, the built-in SIP overload control mechanism based on generating rejection messages cannot prevent the server from entering congestion collapse under heavy load. The SIP overload problem calls for a pushback control solution in which the potentially overloaded receiving server may notify its upstream sending servers to have them send only the amount of load within the receiving server's processing capacity. The pushback framework can be achieved by either a rate-based feedback or a window-based feedback. The centerpiece of the feedback mechanism is the algorithm used to generate load regulation information. We propose three new window-based feedback algorithms and evaluate them together with two existing rate-based feedback algorithms. We compare the different algorithms in terms of the number of tuning parameters and performance under both steady and variable load. Furthermore, we identify two categories of fairness requirements for SIP overload control, namely, user-centric and provider-centric fairness. With the introduction of a new double-feed SIP overload control architecture, we show how the algorithms meet those fairness criteria