Tamás Henk

A new degree of freedom in ATM network dimensioning: optimizing the logical configuration

A mathematical model is presented that provides a well-defined formulation of the logical configuration problem of ATM networks (the carriers of future B-ISDN) with the objective of maximizing the total expected network revenue, given the physical network parameters and the traffic requirements of each virtual subnetwork. A two-phase solution procedure is developed in which the decision variables are the logical link capacities that specify the logical decomposition into virtual subnetworks, and the load sharing parameters. The first phase of the solution finds a global optimum in a rougher model. The second phase uses this as an initial point for a gradient-based hill climbing that applies the partial derivatives of the network revenue function obtained in a more refined model. >

Generic multipath routing concept for dynamic traffic engineering

This letter presents a generic adaptive multipath routing solution. The method, called Core-State-Limited Load Sharing (CSLLS), has two parameters. One determines the adaptation time to load changes and the other one controls how long the path of a flow is unchanged. With these two parameters CSLLS is capable of modelling previous multipath routing concepts like ECMP, OMP and QoSPF. CSLLS overcomes the limitations of earlier approaches by being adaptive (opposed to ECMP), by ensuring path integrity for a flow (opposed to OMP), and by relying on only a few stored states in core routers (opposed to QoSPF)

Analog neural optimization for ATM resource management

This paper addresses the issue of how to solve convex programming problems by analog artificial neural networks (ANNs), with applications in asynchronous transfer mode (ATM) resource management. We first show that the essential and difficult optimization problem of dimensioning the system of virtual subnetworks in ATM networks can be modeled as a convex programming task. Here the transformation of the problem into a convex programming task is a nontrivial step. We also present and analyze an analog ANN architecture that is capable of solving such convex programming tasks with time-varying penalty multipliers. The latter property makes it possible to perform quick sensitivity analysis with respect to the constraints in order to identify the bottleneck capacities in the network or those which give the highest return if we invest in extending them.

Resilient reduced-state resource reservation

Due to the strict requirements of emerging applications, per-flow admission control is gaining increasing importance. One way to implement per-flow admission control is using an on-path resource reservation protocol, where the admission decision is made hop-by-hop after a new flow request arrives at the network boundary. The next-steps in signaling (NSIS) working group of the Internet engineering task force (IETF) is standardising such an on-path signaling protocol. One of the reservation methods considered by NSIS is reduced-state mode, which, suiting the differentiated service (DiffServ) concept, only allows per-class states in interior nodes of a domain. Although there are clear benefits of not dealing with per-flow states in interior nodes — like scalability and low complexity —, without per-flow states the handling of re-routed flows, e.g., after a failure, is a demanding and highly non-trivial task. To be applied in carrier-grade networks, the protocol needs to be resilient in this situation. In this article, we will explain the consequences of a route failover to resource reservation protocols: Severe congestion and incorrect admission decisions due to outdated reservation states. We will set requirements that handling solutions need to fulfill, and we propose extensions to reduced-state protocols accordingly. We show with a set of simulated scenarios that with the given solutions reduced-state protocols can handle re-routed flows practically as fast and robust as stateful protocols.

Efficient bounds for bufferless statistical multiplexing

This paper is concerned with the saturation probability of aggregate traffic data arrival rate on a communication link. This performance metric also referred to as the tail distribution of aggregate traffic is essential in traffic control and management algorithms of high speed networks including future QoS Internet. In this paper, efficient closed form upper bounds have been derived for the saturation probability when very little information is available on the aggregate traffic. The performance of these estimators is also investigated via numerical examples.

State correction after re-routing with reduced state resource reservation protocols

Recently, a new working group - Next Steps In Signalling (NSIS) - was established within the IETF to develop a general signalling protocol primarily to support QoS resource reservation. Two main operation modes have been clarified: stateful and reduced state. The first mode relies on per flow-state information in all interior nodes while the other uses aggregated states. Stateful operation is based on the resource reservation protocol (RSVP), while reduced state operation will be similar to the resource management in a Diffserv (RMD) framework. With the use of aggregated states instead of per-flow granularity, one looses fine control over resources but derives a scalable and more efficient protocol. However, some problems need new handling mechanisms. In this paper, we discuss the problem of re-routing from the aspect of reduced state resource reservation. We show how quickly and easily RSVP handles such situations but reduced state solutions like RMD require new mechanisms for a quick reaction. We propose and analyse several solutions.

Activity Recognition for Personal Time Management

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

GEFCOM 2014 - Probabilistic Electricity Price Forecasting

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.

Path integrity aware traffic engineering [telecom traffic]

Dynamic traffic engineering requires the operation of load balanced routing. Load sharing, however, implies a thus far neglected, currently hidden problem: insufficient path integrity. Since load balancing requires changing the routes of traffic in order to achieve proper load distribution, path changes of ongoing communications go hand in hand with dynamic traffic engineering. Frequent path changes will not only degrade the performance of current TCP based applications but they will also dramatically influence the grade of service experienced by future QoS critical applications. To overcome this problem, we present a two level hashing based mechanism for route pinning under load balancing. We analytically formalise the path integrity and convergence time achievable for our routing model, and use these formulas for dimensioning path integrity aware load balancing.

Performance evaluation of a time division multiplexing method applicable for dynamic transfer mode networks

The paper is concerned with analyzing a time-division multiplexing method with two priority classes. A discrete-time model is established for analyzing the queuing behaviour of low priority traffic streams. The motivation came from dynamic transfer mode (DTM) networks which uses time-division multiplexing of channels, however the results might also be used for other fast circuit switched networks. The model to be investigated is based on the important observation that from the viewpoint of low priority traffic the system behaves as a queuing system subjected to periodic server interruption. The probability generating function (p.g.f.) of the system time and system content of low priority messages is expressed in closed forms from which the first two moments can be calculated in a straightforward manner. Tail probabilities of system content and system time are also derived by applying an efficient approximation technique. Finally, we also give numerical examples as simple illustrations.