39910433
Augmented neural networks for task scheduling
Abstract We propose a new approach, called Augmented Neural Networks (AugNN) for solving the task-scheduling problem. This approach is a hybrid of the heuristic and the neural networks approaches. While retaining all the advantages of the heuristic approach, AugNN incorporates learning, to find improved solutions iteratively. This new framework maps the problem structure to a neural network and utilizes domain specific knowledge for finding solutions. The problem we address is that of minimizing the makespan in scheduling n tasks on m machines where the tasks follow a precedence relation and task pre-emption is not allowed. Solutions obtained from AugNN using various learning rules are compared with six different commonly used heuristics. AugNN approach provides significant improvements over heuristic results. In just a few iterations, the gap between the lower bound and the obtained solution is reduced by as much as 58% for some heuristics, without any increase in computational complexity. While the heuristics found solutions in the range of 5.8–26.9% of the lower bound, on average, AugNN found solutions in the range of 3.3–11.1%, a significant improvement.
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.
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.
37303795
Validation of SIP/H.323 Interworking Using SDL/MSC
SIP and H.323 are complementary protocols used (by different communities) to provide signaling for Internet multimedia sessions. An Interworking Facility (IWF) between SIP and H.323 has been proposed within the Internet Engineering Task Force (IETF). The IWF, SIP User Agent, SIP Server, H.323 End Point, and H.323 Gatekeeper have been formally specified in SDL, and validated using a combination of MSC comparison and interactive simulation. SDL was found to be simple to use, and its object-orientation made model enhancement easy. Suggested improvements to SDL include more flexible ways to define data.
5921169
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections or relations between entities. Meanwhile, Pagerank and variants find the stationary distribution of a reasonable but arbitrary Markov walk over a network, but do not learn from relevance feedback. We present a framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges. We propose two flavors of conductance learning problems in our framework. In the first setting, relevance feedback comparing node-pairs hints that the user has one or more hidden preferred communities with large edge conductance, and the algorithm must discover these communities. We present a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer. In the second setting, edges have types, and relevance feedback hints that each edge type has a potentially different conductance, but this is fixed across the whole network. Our algorithm learns the conductances using an approximate Newton method.
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.
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.