Optimal Algorithms for Near-Hitless Network Restoration via Diversity Coding

Diversity coding is a network restoration technique which offers near-hitless restoration, while other state-of-the art techniques are significantly slower. Furthermore, the extra spare capacity requirement of diversity coding is competitive with the others. Previously, we developed heuristic algorithms to employ diversity coding structures in networks with arbitrary topology. This paper presents two algorithms to solve the network design problems using diversity coding in an optimal manner. The first technique pre-provisions static traffic whereas the second technique carries out the dynamic provisioning of the traffic on-demand. In both cases, diversity coding results in smaller restoration time, simpler synchronization, and much reduced signaling complexity than the existing techniques in the literature. A Mixed Integer Programming (MIP) formulation and an algorithm based on Integer Linear Programming (ILP) are developed for pre-provisioning and dynamic provisioning, respectively. Simulation results indicate that diversity coding has significantly higher restoration speed than Shared Path Protection (SPP) and p-cycle techniques. It requires more extra capacity than the p-cycle technique and SPP. However, the increase in the total capacity is negligible compared to the increase in the restoration speed.Comment: An old version of this paper is submitted to IEEE Globecom 2012 conferenc

Distributed Failure Restoration for Asynchronous Transfer Mode (ATM) Tactical Communication Networks

Asynchronous Transfer Mode (A TM) is an attractive choice for future military communication systems because it can provide high throughput and support multi-service applications. Furthermore its use is consistent with the 'off the shelf technology policy that is currently operated by the Defence Engineering Research Agency of Great Britain. However, A TM has been developed as a civil standard and is designed to operate in network infrastructures with very low failure rates. In contrast, tactical networks are much less reliable. Indeed tactical networks operate on the premise that failures, particularly node failures, are expected. Hence, efficient, automatic failure restoration schemes are essential if an A TM based tactical network is to remain operational. The main objective of this research is the proposal and verification of one or more new restoration algorithms that meet the specific requirements of tactical networks. The aspects of ATM networks that influence restoration algorithms' implementation are discussed. In particular, the features of A TM networks such as the concept of Virtual Paths Virtual Channels and OAM (Operation And Maintenance) mechanisms that facilitate implementation of efficient restoration techniques. The unique characteristics of tactical networks and their impact on restoration are also presented. A significant part of the research was the study and evaluation of existing approaches to failure restoration in civil networks. A critical analysis of the suitability of these approaches to the tactical environment shows no one restoration algorithm fully meets the requirements of tactical networks. Consequently, two restoration algorithms for tactical A TM networks, DRA-TN (Dynamic Restoration Algorithm for Tactical Networks) and PPR-TN (Pre-planned Restoration Algorithm for Tactical Networks), are proposed and described in detail. Since the primary concern of restoration in tactical networks is the recovery of high priority connections the proposed algorithms attempt to restore high-priority connections by disrupting low-priority calls. Also, a number of additional mechanisms are proposed to reduce the use of bandwidth, which is a scarce resource in tactical networks. It is next argued that software simulation is the most appropriate method to prove the consistency of the proposed algorithms, assess their performance and test them on different network topologies as well as traffic and failure conditions. For this reason a simulation software package was designed and built specifically to model the proposed restoration algorithms. The design of the package is presented in detail and the most important implementation issues are discussed. The proposed restoration algorithms are modelled on three network topologies under various traffic loads, and their performance compared against the performance of known algorithms proposed for civil networks. It is shown that DRA-TN and PPR-TN provide better restoration of higher priority traffic. Furthermore, as the traffic load increases the relative performance of the DRA-TN and PPR-TN algorithms increases. The DRA-TN and PPR-TN algorithms are also compared and their advantages and disadvantages noted. Also, recommendations are given about the applicability of the proposed algorithms, and some practical implementation issues are discussed. The number of problems that need further study are briefly described.Defence Engineering Research Agency of Great Britai

Self-Healing Computation

In the problem of reliable multiparty computation (RC), there are $n$ parties, each with an individual input, and the parties want to jointly compute a function $f$ over $n$ inputs. The problem is complicated by the fact that an omniscient adversary controls a hidden fraction of the parties. We describe a self-healing algorithm for this problem. In particular, for a fixed function $f$, with $n$ parties and $m$ gates, we describe how to perform RC repeatedly as the inputs to $f$ change. Our algorithm maintains the following properties, even when an adversary controls up to $t \leq (\frac{1}{4} - \epsilon) n$ parties, for any constant $\epsilon >0$. First, our algorithm performs each reliable computation with the following amortized resource costs: $O(m + n \log n)$ messages, $O(m + n \log n)$ computational operations, and $O(\ell)$ latency, where $\ell$ is the depth of the circuit that computes $f$. Second, the expected total number of corruptions is $O(t (\log^{*} m)^2)$, after which the adversarially controlled parties are effectively quarantined so that they cause no more corruptions.Comment: 17 pages and 1 figure. It is submitted to SSS'1

Resilient network dimensioning for optical grid/clouds using relocation

In this paper we address the problem of dimensioning infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We will provide an overview of our work in this area, and in particular focus on how to design the resulting grid/cloud to be resilient against network link and/or server site failures. To this end, we will exploit relocation: under failure conditions, a request may be sent to an alternate destination than the one under failure-free conditions. We will provide a comprehensive overview of related work in this area, and focus in some detail on our own most recent work. The latter comprises a case study where traffic has a known origin, but we assume a degree of freedom as to where its end up being processed, which is typically the case for e. g., grid applications of the bag-of-tasks (BoT) type or for providing cloud services. In particular, we will provide in this paper a new integer linear programming (ILP) formulation to solve the resilient grid/cloud dimensioning problem using failure-dependent backup routes. Our algorithm will simultaneously decide on server and network capacity. We find that in the anycast routing problem we address, the benefit of using failure-dependent (FD) rerouting is limited compared to failure-independent (FID) backup routing. We confirm our earlier findings in terms of network capacity savings achieved by relocation compared to not exploiting relocation (order of 6-10% in the current case studies)

Protection and restoration algorithms for WDM optical networks

Currently, Wavelength Division Multiplexing (WDM) optical networks play a major role in supporting the outbreak in demand for high bandwidth networks driven by the Internet. It can be a catastrophe to millions of users if a single optical fiber is somehow cut off from the network, and there is no protection in the design of the logical topology for a restorative mechanism. Many protection and restoration algorithms are needed to prevent, reroute, and/or reconfigure the network from damages in such a situation. In the past few years, many works dealing with these issues have been reported. Those algorithms can be implemented in many ways with several different objective functions such as a minimization of protection path lengths, a minimization of restoration times, a maximization of restored bandwidths, etc. This thesis investigates, analyzes and compares the algorithms that are mainly aimed to guarantee or maximize the amount of remaining bandwidth still working over a damaged network. The parameters considered in this thesis are the routing computation and implementation mechanism, routing characteristics, recovering computation timing, network capacity assignment, and implementing layer. Performance analysis in terms of the restoration efficiency, the hop length, the percentage of bandwidth guaranteed, the network capacity utilization, and the blocking probability is conducted and evaluated

Responsive Algorithms for Defending Recon gurable Networks

We present algorithms to self-heal reconfigurable networks when they are under attack. These algorithms reconfigure the network during attack to protect two critical invariants. First, they insure that the network remains connected. Second, they insure that no node increases its degree by more than O(log n). We show both theoretically and empirically that our algorithms can successfully maintain these invariants even for large networks under massive attack by a computationally unbounded adversary

Statistical Learning for Optimal Control of Hybrid Systems

In this paper we explore a randomized alternative for the optimization of hybrid systems\u27 performance. The basic approach is to generate samples from the family of possible solutions, and to test them on the plant\u27s model to evaluate their performance. This result is obtained by first presenting the general hybrid optimal control problem, and then converting it into an optimization problem within a statistical learning framework. The results are applied to examples already existing in the literature, in order to highlight certain operational aspects of the proposed methods