Marios Lestas

Adaptive nonlinear congestion controller for a differentiated-services framework

The growing demand of computer usage requires efficient ways of managing network traffic in order to avoid or at least limit the level of congestion in cases where increases in bandwidth are not desirable or possible. In this paper we developed and analyzed a generic Integrated Dynamic Congestion Control (IDCC) scheme for controlling traffic using information on the status of each queue in the network. The IDCC scheme is designed using nonlinear control theory based on a nonlinear model of the network that is generated using fluid flow considerations. The methodology used is general and independent of technology, as for example TCP/IP or ATM. We assume a differentiated-services network framework and formulate our control strategy in the same spirit as IP DiffServ for three types of services: Premium Service, Ordinary Service, and Best Effort Service. The three differentiated classes of traffic operate at each output port of a router/switch. An IDCC scheme is designed for each output port, and a simple to implement nonlinear controller, with proven performance, is designed and analyzed. Using analysis performance bounds are derived for provable controlled network behavior, as dictated by reference values of the desired or acceptable length of the associated queues. By tightly controlling each output port, the overall network performance is also expected to be tightly controlled. The IDCC methodology has been applied to an ATM network. We use OPNET simulations to demonstrate that the proposed control methodology achieves the desired behavior of the network, and possesses important attributes, as e.g., stable and robust behavior, high utilization with bounded delay and loss, together with good steady-state and transient behavior.

Speed adaptive probabilistic flooding in cooperative emergency warning

Vehicular Ad Hoc Networks (VANETs) are emerging as the preferred network design for intelligent transportation systems and are envisioned to be useful in road safety and commercial applications. A significant issue in VANETs is the design of an effective broadcast scheme which can facilitate the fast and reliable dissemination of critical safety messages to neighbouring vehicles in case of an unexpected event, such as a traffic accident. Towards this goal, we propose a novel Speed Adaptive Probabilistic Flooding algorithm (SAPF). Its decision to rebroadcast a message is based on a probability, evaluated using the speed of the vehicle. The algorithm enjoys a number of benefits relative to other approaches: it is simple to implement and does not introduce additional communication burden as it requires local information only, it does not rely on the existence of a positioning system which may not always be available and above all, mitigates the effect of the broadcast storm problem, typical when utilizing blind flooding. Our results indicate that the proposed algorithm outperforms blind flooding, especially in cases of heavy congestion. The SAPF algorithm achieves high reachability and unlike blind flooding, it also maintains low latency as the density of vehicles in the road network increases.

Adaptive congestion protocol: A congestion control protocol with learning capability

There is strong evidence that the current implementation of TCP will perform poorly in future high-speed networks. To address this problem many congestion control protocols have been proposed in literature which, however, fail to satisfy key design requirements of congestion control protocols, as these are outlined in the paper. In this work we develop an adaptive congestion protocol (ACP) which is shown to satisfy all the design requirements and thus outperform previous proposals. Extensive simulations indicate that the protocol is able to guide the network to a stable equilibrium which is characterized by max-min fairness, high-utilization, small queue sizes and no observable packet drops. In addition, it is found to be scalable with respect to changing bandwidths, delays and number of users utilizing the network. The protocol also exhibits nice transient properties such as smooth responses with no oscillations and fast convergence. In realistic traffic scenarios comprising of a small number of long flows and a large number of short flows, ACP outperforms both TCP and XCP, even in the presence of random packet losses. ACP does not require maintenance of per flow states within the network and utilizes an explicit multi-bit feedback signalling scheme. To maintain stability it implements at each link a novel estimation algorithm which estimates the number of flows utilizing the link. Using a simple network model, we show analytically the effectiveness of the estimation algorithm. We use the same model to generate phase portraits which demonstrate that the ACP protocol is stable for all delays.

Speed Adaptive Probabilistic Flooding for Vehicular Ad Hoc Networks

A significant issue in vehicular ad hoc networks (VANETs) is the design of an effective broadcast scheme that can facilitate the fast and reliable dissemination of emergency warning messages in the vicinity of an unexpected event, such as a traffic accident. In this paper, we propose a novel solution to this problem, which we refer to as speed adaptive probabilistic flooding. The scheme employs probabilistic flooding to mitigate the effects of the broadcast storm problem, which is typical when using blind flooding, and its unique feature is that the rebroadcast probability is adaptively regulated based on the vehicle speed to account for varying traffic densities within the transportation network. The motivation behind this choice is the identification of the existence of phase transition phenomena in probabilistic flooding in VANETs, which dictate a critical probability being affected by the varying vehicle traffic density and are shown to be linearly related to the vehicle speed (a locally measurable quantity). The scheme is evaluated using simulations on different sections of the freeway system in the City of Los Angeles, CA, USA. Simulation results indicate that the proposed scheme fulfills its design objectives, as it achieves high reachability and low latency of message delivery with low overhead in a number of representative scenarios. The scheme is also shown to outperform existing solutions, including Global-Positioning-System-based, and exhibits robustness with respect to different road topologies and parameters such as the transmission range of vehicles and the number of hops.

Balancing Wireless Data Broadcasting and Information Hovering for Efficient Information Dissemination

Wireless data broadcasting is an efficient, bandwidth preserving way of data dissemination. However, as the amount of data increases, the waiting time of the clients becomes unacceptably high. The present paper proposes the combination of optimal wireless broadcasting and information hovering, as an effective means of performance improvement in vehicular networks with locality of demand. While state-of-the-art works exploit user collaboration only as a means of wireless coverage extension, the proposed scheme proposes parallel dissemination through broadcasting and user networking over the whole studied area. Optimal, periodic broadcast scheduling is adopted at the highest tier for data dissemination. At the lowest tier, users can exploit information hovering around selected anchoring points, in order to retrieve data faster than their next scheduled broadcast. The issue of sharing the dissemination load optimally between the broadcasting and the hovering subsystems is mapped to the classic pull-push balancing problem. Through analysis, the long-standing “optimal cut-off point” balancing method is shown to be suboptimal, and a new method is proposed which achieves lower client serving time in any of the cases. Simulation results in realistic VANETs show that the proposed dissemination scheme surpasses state-of-the-art works in terms of efficiency and client satisfaction.

Adaptive probabilistic flooding for Information Hovering in VANETs

Information Hovering applies in many applications in Vehicular Ad Hoc Networks, where useful information needs to be made available to all vehicles within a confined geographical area for a specific time interval. A straightforward approach is to have all vehicles within the hovering area exchange messages with each other. However, this method does not guarantee that all vehicles within the hovering area will receive the message due to potential partitioning of the network in areas with low traffic density and/or low market penetration rate. To alleviate this problem in this work we propose a scheme which is based on the application of epidemic routing within the hovering area and probabilistic flooding outside the hovering area. Informed vehicles outside the area can serve as information bridges towards partitioned uninformed areas thus leading to high reachability. A unique feature of the proposed protocol is that it is adaptive in the sense that the rebroadcast probability outside the hovering area is adaptively regulated based on estimates of the vehicle density within the hovering area. We evaluate the performance of the proposed scheme using VISSIM. The reference model used in all simulation experiments represents a section of the road network in the cities of Bellevue and Redmond in Washington. The obtained simulation results indicate that the proposed protocol is successful in satisfying its design objectives and that it outperforms other candidate hovering protocols.

A new estimation scheme for the effective number of users in internet congestion control

Many congestion control protocols have been recently proposed in order to alleviate the problems encountered by TCP in high-speed networks and wireless links. Protocols utilizing an architecture that is in the same spirit as the ABR service in ATM networks require estimates of the effective number of users utilizing each link in the network to maintain stability in the presence of delays. In this paper, we propose a novel estimation algorithm that is based on online parameter identification techniques and is shown through analysis and simulations to converge to the effective number of users utilizing each link. The algorithm does not require maintenance of per-flow states within the network or additional fields in the packet header, and it is shown to outperform previous proposals that were based on pointwise division in time. The estimation scheme is designed independently from the control functions of the protocols and is thus universal in the sense that it operates effectively in a number of congestion control protocols. It can thus be successfully used in the design of new congestion control protocols. In this paper, to illustrate its universality, we use the proposed estimation scheme to design a representative set of Internet congestion control protocols. Using simulations, we demonstrate that these protocols satisfy key design requirements. They guide the network to a stable equilibrium that is characterized by high network utilization, small queue sizes, and max-min fairness. In addition, they are scalable with respect to changing bandwidths, delays, and number of users, and they generate smooth responses that converge quickly to the desired equilibrium.

Broadcast Scheduling With Multiple Concurrent Costs

Data dissemination via periodic broadcasting considers a set of items, each with a given request probability, size and scheduling cost. The goal is to construct a broadcast schedule that minimizes the mean query serving time and the mean scheduling cost at the same time. This task has been proven to be NP-Hard, and related studies have gradually discarded the scheduling cost attribute in an effort to simplify the problem. The present study reinstates the cost attribute, as well as any number of additional cost attributes per data item. The proposed, MULTIOPT scheduling algorithm then achieves optimal mean serving time and mean values for all costs concurrently. Comparison with brute-force results and related approaches yield optimality in all tested cases.

Speed Adaptive Probabilistic Flooding for vehicular ad-hoc networks

A significant issue in vehicular ad hoc networks is the design of an effective broadcast scheme which can facilitate the fast and reliable dissemination of emergency warning messages in the vicinity of an expected event, such as a car accident. In this work we propose a novel solution to this problem, which we refer to as Speed Adaptive Probabilistic Flooding. The scheme employs probabilistic flooding to mitigate the effects of the broadcast storm problem, typical when using blind flooding, and its unique feature is that the rebroadcast probability is regulated adaptively based on the vehicle speed to account for varying traffic densities within the transportation network. The protocol enjoys a number of benefits relative to other approaches: it is simple to implement, it does not introduce additional communication burden, as it relies on local information only and it does not rely on the existence of a positioning system which may not always be available. The scheme is evaluated on different sections of the highway system in the City of Los Angeles using an integrated platform combining the OPNET Modeler and the VISSIM simulator. Simulation results indicate that the proposed scheme fulfills its design objectives as it achieves high reachability and low latency of message delivery in a number of scenarios. Its robustness with respect to changing number of hops and transmission ranges is also demonstrated.

Information Hovering in Vehicular Ad-Hoc Networks

Information Hovering is a relatively new concept of information dissemination over a mobile set of peers. It naturally applies in many applications in Vehicular Ad-Hoc Networks, where useful information needs to be made available to all vehicles within a confined geographical area for a specific time interval. A straightforward approach, is to have all vehicles within the hovering area exchange messages with each other. However, this method does not guarantee that all vehicles within the hovering area will receive the message due to potential parti- tioning of the network in areas with low traffic density and/or low market penetration rate. In this work, we address the problem by applying probabilistic flooding schemes outside the hovering area. Informed vehicles outside the hovering area can serve as information bridges towards partitioned uninformed areas thus leading to high reachability. We consider a number of rebroadcast probability functions and we evaluate their performance using the microscopic simulation tool VISSIM. Our reference model represents a section of the road network in the cities of Bellevue and Redmond in Washington. The obtained results indicate that probabilistic flooding with a Gaussian like probability function outperforms other approaches by achieving high reachability values and a relatively small number of exchanged messages.