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.
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.
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.
2557933
Packet loss probability for DiffServ over IP and MPLS reliable homogeneous multicast networks
Multicasting has become increasingly important with the emergence of Internet-based applications such as IP telephony, audio/video conferencing, distributed databases and software upgrading. IP multicasting is an efficient way to distribute information from a single source to multiple destinations at different locations. In practice IP is considered as a layer 3 protocol. Multiprotocol Label Switching (MPLS) replaces the IP forwarding by a simple label lookup. MPLS combines the flexibility of layer 3 routing and layer 2 switching.In order to provide QoS in group communications for real time applications such as video conferencing, reliable multicasting is used. Miscellaneous efforts have been undertaken to provide reliability on top of IP multicast. Two error control strategies have been popular in practice. These are the FEC (Forward Error Correction) strategy, which uses error correction alone, and the ARQ (Automatic Repeat Request) strategy, which uses error detection, combined with retransmission of data.In this paper, we present a new fair share policy (FSP) that utilizes Differentiated Services to solve the problems of QoS and congestion control when reliable ARQ multicast is used. The results should provide insight into the comparisons of the residual packet loss probability between IP multicast in MPLS networks using FSP and plain IP multicasting using the same policy when DiffServ are adopted and when reliable ARQ multicast is considered.
12571939
Inflight Rerouting for an Unmanned Aerial Vehicle
The availability of new information can often necessitate rerouting of a vehicle. In (1), the set of nonoverlapping useful vicinities of the reconnaissance sites, S, and the 3D space over them was represented using a discrete graph-model. Here, we consider two cases in which an unmanned reconnaissance aerial vehicle must be rerouted. The first scenario involves not visiting useful vicinities, S t ⊂ S, due to newly reported unacceptable threat- levels in their neighborhood. In the second scenario, visitations to useful vicinities, S r ⊂ S, is made redundant due to decreased importance of the sites to which they offer a vantage observation-point. Our earlier graph-model (1) is general enough to provide a basic framework for consideration of rerouting in both cases. We give an algorithm to obtain a graph-model, from the earlier one, that allows efficient gene encoding and efficient application of GA operators to obtain new alternative rerouting plans.
16989082
A Layered Architecture for NOC Design Methodology
ABSTRACT Multiprocessor system on chip (MPSoC) platform is an innovative trend of System on Chip (SoC) that enhances system performance. Demanding quality of service parameters and performance metrics, especially in mobile applications, are leading to the exploration of even more innovative architectures for SoC. These will have to incorporate highly scalable, reusable, predictable, cost and energy efficient architectures. Network on Chip (NOC) is a key example of this trend. NOC separates computing and communication concerns in an elegant manner. We propose here a seven layered architecture for designing NOC-based systems. Such a platform can separate domain specific issues in separate layers, which will allow for more effective modeling of concurrency and synchronization issues, in an attempt to develop an optimized system. For such a layered architecture, models of computation (MOC) will provide a framework to model various algorithms and activities, while accounting for and exploiting concurrency and synchronization aspects. These MOCs may differ from one to another NOC region. Further, a combination of these MOCs may be needed to truly represent a given NOC region. We have analyzed various models of computation (MOC) suitable for NOC. MLDesigner provides a system level modeling platform which allows one to integrate such MOCs together. We present our efforts and experiences so far.
6368187
Algorithms for derivation of structurally stable Hamiltonian signed graphs
A graph Sp,q,n refers to a signed graph with p nodes and q edges with n being the number of negative edges. We introduce two theorems to facilitate identification of the complete set of balanced signed graph configurations for any p-node Hamiltonian signed graph in terms of p, q and n. This allows for the development of computational procedures to efficiently determine the structural stability of a signed graph. This is potentially useful for the planning and analysis of complex situations or scenarios which can be depicted as signed graphs. Through the application of the theorems, the state of balance of a signed graph structure or its affinity towards balance can be determined in a more time-efficient manner compared to any explicit enumeration algorithm. E-mail: pg0212208t@ntu.edu.sg E-mail: fnh@crl.nmsu.edu E-mail: dwunsch@ece.umr.edu
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.
21945943
Heuristics and augmented neural networks for task scheduling with non-identical machines
Abstract We propose new heuristics along with an augmented-neural-network (AugNN) formulation for solving the makespan minimization task-scheduling problem for the non-identical machine environment. We explore four task and three machine-priority rules, resulting in 12 combinations of single-pass heuristics. The task-priority rules are Highest-Level-First (HLF), Highest-Total-Remaining-Processing-Time-First (HTRPTF), Smallest-Latest-Finish-Time-First (SLFTF) and Minimum-Slack-First (MSF). For machine priority, we propose a greedy rule called Fastest-Available-Machine-First (FAMF), and two non-greedy rules: (1) Fastest-Available-Machine-First-With-Conditional-Wait-1 (FAMF-CW-1), and (2) Fastest-Available-Machine-First-With-Conditional-Wait-2 (FMF-CW-2). The AugNN approach integrates neural-network learning with domain and problem specific knowledge through heuristics, to produce good results. A single-pass heuristic is embedded in a neural network designed specifically for each problem. We give the AugNN formulation for each of the 12 heuristics and show computational results on 100 randomly generated problems of sizes ranging from 20 to 70 tasks and 2 to 5 machines. Results demonstrate that AugNN provides significant improvement over single-pass heuristics. The reduction in the gap between the obtained solution and the lower-bound due to AugNN over single-pass heuristics ranged from 24.4% to 50%. As far as heuristics, the non-greedy machine-priority rules performed significantly better than the greedy rule. The average gaps for the non-greedy rules ranged from 16.1% to 23.5% compared to 33.7% to 40.4% for greedy. For AugNN, for non-greedy rules, the gap ranged from 11.5% to 15.5% compared to 18.4% to 25.0% for greedy. The HLF and HTRPTF task priority rules performed better than the other two. The HTRPTF/FAMF-CW-1 combination gave the best results, closely followed by HLF/FAMF-CW-2 and HTRPTF/FAMF-CW-2 combinations.