23363151
Deploying multicast communication over MPLS networks using tree numbering
A new scheme for deploying multicasting in MPLS networks is proposed. Each possible tree in an MPLS network is assigned a number, which is then used to classify the corresponding multicast session into its FEC. We call this approach tree numbering (TN). It provides the capability to aggregate different multicast flows (sessions) having the same tree "shape". The assigned number is calculated distributedly by adding the partial weight values generated by the LSRs and the ingress LER of the corresponding tree. The key point in our approach is that the assigned numbers needed to distinguish the "shapes" of all the possible trees depend on the number of possible egress LERs could be reached by that ingress LERs and not on the number of the LSRs (core routers). In terms of the memory size, the proposed approach outperforms the approach that stores the IP addresses of the multicast tree or the one that store the concatenation of the sub codes generated by the ingress LER and the LSRs.
27266792
A unified approach to fault-tolerance in communication protocols based on recovery procedures
Discusses fault tolerance in computer communication protocols, modeled by communicating finite state machines, by providing an efficient algorithmic procedure for recovery in such systems. Even when the communication network is reliable and maintains the order of messages, any kind of transient error that may not be detected immediately could contaminate the system, resulting in protocol failure. To achieve fault-tolerance, the protocol must be able to detect the error, and then it must recover from that error and eventually reach a legal (or consistent) state, and resume its normal execution. A protocol that possesses the latter feature of recovering and continuing its execution starting from a legal state is also called a self-stabilizing protocol. Our recovery procedure does not require the application of an intrusive checkpointing procedure. The stable storage requirement for each process is less than that required for other proposed recovery procedures. The recovery procedure provides us with a legal protocol state, which is the global state before reaching any illegal state and before the effects of the error make other states illegal. Only a minimal number of processes affected by error propagation are required to rollback. Our recovery procedure can be used to recover from any number of transient errors in the system. Our recovery procedure has also been modeled in PROMELA, a language to describe validation models, which shows the syntactic correctness of our recovery protocol design. Finally, our procedure is compared with the existing approaches of handing the errors, and an illustrative example is provided.
13650022
Recovery Approach to the Design of Stabilizing Communication Protocols
This paper addresses the problem of designing stabilizing computer communication protocols modelled by communicating finite state machines. A communication protocol is said to be stabilizing if, starting from or reaching any illegal state, the protocol will eventually reach a legal (or consistent) state, and resume its normal execution. To achieve stabilization, the protocol must be able to detect the error as soon as it occurs, and then it must recover from that error and revert back to a legal protocol state. The later issue related to recovery is tackled here, and an efficient procedure for the recovery in communications protocols is described. The recovery procedure does not require periodic checkpointing and, therefore, is less intrusive. It requires less time for rollback and fewer recovery control messages than other procedures. Only a minimal number of processes will roll back, and a minimal number of protocol messages will be retransmitted during recovery. Moreover, our procedure requires minimal stable storage to be used to record contextual information exchanged during the progress of the protocol. Finally, our procedure is compared with an existing recovery procedure, and an illustrative example is provided.
8220547
A tree division approach to support local failure recovery for multicasting in MPLS networks
This paper introduces a novel approach to support local failure recovery in multicast trees. Although, the presented approach can be implemented in any large backbone network that employs multicast communication mode, we concentrate our discussion on MPLS networks. In the proposed approach, a multicast tree is divided into several domains where each domain represents a sub-tree of the original one. Backup paths are built between the root of the domain and each leaf router which is called a border router. Simulation results conducted on two network topologies show the performance of the proposed approach in terms of the total capacity required for reserving the backup paths and in terms of the maximum delay needed to notify about link/node failure. The results have shown that tree division approach compared with global recovery approach reduces the time needed to notify the router that is responsible for switching the traffic to the backup path.
14456979
Supporting Quality of Service in IP multicast networks
Developers and providers of data, voice and video applications have to satisfy numerous Quality of Service (QoS) requirements and cope with different architectures emerging to provide these QoS in the Internet. IntServ, DiffServ or MPLS are technologies currently used in different administrative domains in the Internet to provide QoS. With multicast applications emerging, such as video conferencing, distance learning and tele surgery, it has become almost impossible to provide end-to-end QoS over multiple administrative business domains without interworking these technologies. This paper analyses general issues on supporting QoS for multicast applications and reviews different ways to implement IP multicasting in IntServ, DiffServ or MPLS based networks. Subsequently, network configurations based on these different architectures and their interworking to provide end-to-end QoS are also discussed.
34654890
An efficient recovery procedure for fault tolerance in distributed systems
Abstract In this article, the problem of fault tolerance in real-time distributed systems is addressed by providing an efficient algorithmic procedure for recovery in such systems. Our recovery procedure does not require the application of an intrusive checkpointing procedure, but uses contextual information exchanged between the distributed system components during normal system progress. On detection of a failure, each process, through an efficient propagation mechanism, will have locally computed a recovery point that is mutually consistent with the recovery points computed by other processes. During recovery, each process considers the contextual information recorded after its established recovery point; a minimal amount of computations are redone. Also, a minimal number of processes are required to roll back, and the time needed to revert to normal operation is found to be minimal, thus contributing to the real-time requirements for such systems. Furthermore, the stable storage requirement needed to implement our recovery procedure is bounded and minimal. Proofs of correctness of our procedure are provided, in particular, the absence of orphan messages, message losses, and duplications is shown. An example illustrating our recovery procedure is also provided.
206717449
Tracking Articulated Motion Using a Mixture of Autoregressive Models
We present a novel approach to modelling the non-linear and time-varying dynamics of human motion, using statistical methods to capture the characteristic motion patterns that exist in typical human activities. Our method is based on automatically clustering the body pose space into connected regions exhibiting similar dynamical characteristics, modelling the dynamics in each region as a Gaussian autoregressive process. Activities that would require large numbers of exemplars in example based methods are covered by comparatively few motion models. Different regions correspond roughly to different action-fragments and our class inference scheme allows for smooth transitions between these, thus making it useful for activity recognition tasks. The method is used to track activities including walking, running, etc., using a planar 2D body model. Its effectiveness is demonstrated by its success in tracking complicated motions like turns, without any key frames or 3D information.
660603
Algorithms for portfolio management based on the Newton method
We experimentally study on-line investment algorithms first proposed by Agarwal and Hazan and extended by Hazan et al. which achieve almost the same wealth as the best constant-rebalanced portfolio determined in hindsight. These algorithms are the first to combine optimal logarithmic regret bounds with efficient deterministic computability. They are based on the Newton method for offline optimization which, unlike previous approaches, exploits second order information. After analyzing the algorithm using the potential function introduced by Agarwal and Hazan, we present extensive experiments on actual financial data. These experiments confirm the theoretical advantage of our algorithms, which yield higher returns and run considerably faster than previous algorithms with optimal regret. Additionally, we perform financial analysis using mean-variance calculations and the Sharpe ratio.
34027055
Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach
Abstract We propose an improvement-heuristic approach for the general flow-shop problem (n/m/Cmax) based on the idea of adaptive learning. The approach employs a one-pass heuristic to give a good starting solution in the search space and uses a weight parameter to perturb the data of the original problem to obtain improved solutions. The weights are then adjusted employing a learning strategy which involves reinforcement and backtracking. The learning is similar to that in neural networks. The random perturbation allows a non-deterministic local search. We apply the improvement-heuristic approach in conjunction with three well-known heuristics in the literature, namely, Palmer’s Slope Index, CDS and NEH. We test our approach on several benchmark problem sets including Taillard’s, Carlier’s, Heller’s and Reeves’. We compare our results to the best-known upper-bound solutions and find that for many problems we match the best-known upper bound. For one problem we discover a new upper bound.
6169933
Learning Parameters in Entity Relationship Graphs from Ranking Preferences
Semi-structured entity-relation (ER) data graphs have diverse node and edge types representing entities (paper, person, company) and relations (wrote, works for). In addition, nodes contain text snippets. Extending from vector-space information retrieval, we wish to automatically learn ranking function for searching such typed graphs. User input is in the form of a partial preference order between pairs of nodes, associated with a query. We present a unified model for ranking in ER graphs, and propose an algorithm to learn the parameters of the model. Experiments with carefully-controlled synthetic data as well as real data (garnered using CiteSeer, DBLP and Google Scholar) show that our algorithm can satisfy training preferences and generalize to test preferences, and estimate meaningful model parameters that represent the relative importance of ER types.
117080125
Logarithmic Regret Algorithms for Online Convex Optimization
In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After each point is chosen, it encounters a sequence of (possibly unrelated) convex cost functions. Zinkevich [Zin03] introduced this framework, which models many natural repeated decision-making problems and generalizes many existing problems such as Prediction from Expert Advice and Cover’s Universal Portfolios. Zinkevich showed that a simple online gradient descent algorithm achieves additive regret$O({\sqrt{T}})$, for an arbitrary sequence of T convex cost functions (of bounded gradients), with respect to the best single decision in hindsight. 
 
In this paper, we give algorithms that achieve regret O(log(T)) for an arbitrary sequence of strictly convex functions (with bounded first and second derivatives). This mirrors what has been done for the special cases of prediction from expert advice by Kivinen and Warmuth [KW99], and Universal Portfolios by Cover [Cov91]. We propose several algorithms achieving logarithmic regret, which besides being more general are also much more efficient to implement. 
 
The main new ideas give rise to an efficient algorithm based on the Newton method for optimization, a new tool in the field. Our analysis shows a surprising connection to follow-the-leader method, and builds on the recent work of Agarwal and Hazan [AH05]. We also analyze other algorithms, which tie together several different previous approaches including follow-the-leader, exponential weighting, Cover’s algorithm and gradient descent.