Gaurav Raina

The authors present a metric-based hierarchical approach to reduce power consumption in core and edge networks. The proposal considers both unicast and the multicast cases. For unicast, the metric used is consumed-power to available-bandwidth and for multicast the metric is consumed-power to available-replication-capacity. With unicast, the metric is used to determine a low-power path between sources and destinations. The source and destination entities could be attached to Autonomous Systems (ASes) or to routing areas within the Autonomous System. Determining a low-power path within an Autonomous System provides a unique challenge as the topology of the constituent areas may not be known. To that end, we propose the use of a selective leak technique for disclosing low-power paths. Additionally, the proposed method can also be used to determine disjoint or redundant paths for load-balancing or fault tolerance. With multicast, the metric serves the twin purpose of finding low-power multicast paths as well as multicast replication points. Once low-power paths in either the unicast or the multicast cases are identified, then currently available traffic engineering techniques could be used to route the data packets.

We study Compound TCP (C-TCP), the transport protocol in the Windows operating system, in different buffer sizing regimes along with Drop-Tail and Random Exponential Marking (REM). The buffer sizing regimes we focus on are the widely deployed bandwidth-delay rule and a small buffer regime. The performance metrics we consider are stability of the queue size, queuing delay, link utilisation and packet loss. © 2013 IFIP.

In this paper, we explore various statistical techniques for anomaly detection in conjunction with the popular Long Short-Term Memory (LSTM) deep learning model for transportation networks. We obtain the prediction errors from an LSTM model, and then apply three statistical models based on (i) the Gaussian distribution, (ii) Extreme Value Theory (EVT), and (iii) the Tukey’s method. Using statistical tests and numerical studies, we find strong evidence against the widely employed Gaussian distribution based detection rule on the prediction errors. Next, motivated by fundamental results from Extreme Value Theory, we propose a detection technique that does not assume any parent distribution on the prediction errors. Through numerical experiments conducted on several real-world traffic data sets, we show that the EVT-based detection rule is superior to other detection rules, and is supported by statistical evidence.

We study the performance of Compound TCP with Random Early Detection (RED) in three different limiting regimes. In the first regime, averaging over the queue size helps to decides the probability of dropping packets. Then, we consider a model where averaging over the queue size is not performed, but the queue is modelled as an integrator. Finally, we consider a model where the threshold for dropping packets is so small that it is not possible to model the queue as an integrator. In these three regimes, we derive sufficient, as well as necessary and sufficient conditions for local stability. These conditions help to capture the dependence of protocol and network parameters on system stability. We also show that in the event of loss of local stability, the Compound TCP-RED system undergoes a Hopf bifurcation which would lead to limit cycles. Some of the analytical results are corroborated using packet-level simulations.

The increase in queueing delays in the Internet motivates the study of transmission control protocol (TCP) and queue management policies. This paper studies Compound TCP with a Proportional–Integral (PI) policy for queue management at the Internet routers. A nonlinear fluid model for the Compound TCP-PI system is considered. For this model, a sufficient condition for local stability, which yields some design guidelines, is derived. Fragility analysis of this model highlights that even marginal variations in the PI parameters may induce instability. Next, a regime where the integral component, of the PI policy, operates over a small time-scale is considered. For a fluid model befitting this regime, the necessary and sufficient condition for local stability is derived. It is explicitly shown that, when this condition is violated, the system undergoes a Hopf bifurcation, which would lead to limit cycles. Furthermore, a detailed local bifurcation analysis is conducted to characterize the type of the Hopf bifurcation and determine the orbital stability of the limit cycles. Packet-level simulations corroborate the analytical insight. The analysis and simulations demonstrate some drawbacks of the PI policy. Following this, a simple threshold-based queue policy, which has desirable stability properties, is proposed for queue management at routers. Furthermore, a simulation-based performance evaluation reveals that this policy outperforms PI. Through a combination of theory and simulations, it is shown that the threshold-based policy can ensure system stability, while ensuring reduced queueing delays.

Over the last decade, the use of Unmanned Aerial Vehicles (UAVs) has increased dramatically in both military surveillance and in civilian search and rescue operations. © 2012 IEEE.

We analyze Compound TCP, the default protocol in the Windows operating system, along with an Exponential-RED (E-RED) queue policy. The E-RED queue policy specially aims for high link utilization. Our setup considers two sets of TCP flows, each having a different round-trip time, operating over a single bottleneck link. For this system, we first derive a sufficient condition for local stability. The stability condition reveals that the link gain needs to scale with the maximum round-trip time in the system. Additionally, the Compound parameter α needs to be chosen judiciously if stability is to be ensured. We then computationally show that, as parameters vary, the system can undergo a Hopf bifurcation. This bifurcation alerts us about the emergence of limit cycles, in the system dynamics, as stability is just lost. Finally, we exhibit the emergence of such limit cycles, in the queue size, via packet-level simulations. These limit cycles can result in the loss of link utilization and should be avoided.

In networks like the Internet, it is desirable to reduce both power consumption and delays. In this paper, we propose an ant-colony heuristic algorithm which aims to minimize the product of the following two measures: consumed-power to available-bandwidth (PWR) and delay. Our current study is conducted using simulations on a topology which represents a 2-dimensional torus. We also compare the ant-colony based method with the well known Dijkstra's shortest path algorithm. As compared to Dijkstra's algorithm, we observe that the ant-colony heuristic can be computationally less intensive in obtaining a near optimal solution. This could be especially useful in large scale systems. As the PWR metric can be used across all levels of the Internet's routing hierarchy, thus methods like ant-colony algorithms could lead to a reduction in both power consumption and delays. © 2013 IEEE.

Time delays play an important role in determining the qualitative dynamical properties of a platoon of self-driven vehicles driving on a straight road. In this paper, we investigate the impact of Delayed Acceleration Feedback (DAF) on the dynamics of the Reduced Classical Car-Following Model (RCCFM). We first derive the Reduced Classical Car-Following Model with Delayed Acceleration Feedback (RCCFM-DAF). Next, we demonstrate that the transition of traffic flow from the locally stable to the unstable regime occurs via a Hopf bifurcation. The analysis also yields the necessary and sufficient condition for local stability. We characterise the type of Hopf bifurcation and the asymptotic orbital stability of the emergent limit cycles for the RCCFM by using Poincaré normal forms and the center manifold theory. We then use this analysis to infer requisite insights into the RCCFM-DAF by means of an appropriately defined linear transformation. The analysis is complemented with a stability chart and a bifurcation diagram. Our work reveals three effects of DAF on the RCCFM: (i) reduction in the stable region, (ii) increase in the frequency of the emergent limit cycles, and (iii) decrease in the amplitude of the emergent limit cycles. This, in turn, has two immediate repercussions: (i) decrease in resilience to the reaction delay, and (ii) an increase in the risk of a collision due to jerky vehicular motion.

In this paper, we use time-series modeling to forecast taxi travel demand, in the context of a mobile applicationbased taxi hailing service. In particular, we model the passenger demand density at various locations in the city of Bengaluru, India. Using the data, we first shortlist time-series models that suit our application. We then analyse the performance of these models by using Mean Absolute Percentage Error (MAPE) as the performance metric. In order to improve the model performance, we employ a multi-level clustering technique where we aggregate demand over neighboring cells/geohashes. We observe that the improved model based on clustering leads to a forecast accuracy of 80% per km2. In addition, our technique obtains an accuracy of 89% per km2 for the most frequently occurring use case.