Service level agreement framework for differentiated survivability in GMPLS-based IP-over-optical networks

In the next generation optical internet, GMPLS based IP-over-optical networks, ISPs will be required to support a wide variety of applications each having their own requirements. These requirements are contracted by means of the SLA. This paper describes a recovery framework that may be included in the SLA contract between ISP and customers in order to provide the required level of survivability. A key concern with such a recovery framework is how to present the different survivability alternatives including recovery techniques, failure scenario and layered integration into a transparent manner for customers. In this paper, two issues are investigated. First, the performance of the recovery framework when applying a proposed mapping procedure as an admission control mechanism in the edge router considering a smart-edge simple-core GMPLS-based IP/WDM network is considered. The second issue pertains to the performance of a pre-allocated restoration and its ability to provide protected connections under different failure scenarios

Spare capacity modelling and its applications in survivable iP-over-optical networks

As the interest in IP-over-optical networks are becoming the preferred core network architecture, survivability has emerged as a major concern for network service providers; a result of the potentially huge traffic volumes that will be supported by optical infrastructure. Therefore, implementing recovery strategies is critical. In addition to the traditional recovery schemes based around protection and restoration mechanisms, pre-allocated restoration represents a potential candidate to effect and maintain network resilience under failure conditions. Preallocated restoration technique is particularly interesting because it provides a trade-off in terms of recovery performance and resources between protection and restoration schemes. In this paper, the pre-allocated restoration performance is investigated under single and dual-link failures considering a distributed GMPLSbased IP/WDM mesh network. Two load-based spare capacity optimisation methods are proposed in this paper; Local Spare Capacity Optimisation (LSCO) and Global Spare Capacity Optimisation (GSCO)

Survivability in Time-varying Networks

Time-varying graphs are a useful model for networks with dynamic connectivity such as vehicular networks, yet, despite their great modeling power, many important features of time-varying graphs are still poorly understood. In this paper, we study the survivability properties of time-varying networks against unpredictable interruptions. We first show that the traditional definition of survivability is not effective in time-varying networks, and propose a new survivability framework. To evaluate the survivability of time-varying networks under the new framework, we propose two metrics that are analogous to MaxFlow and MinCut in static networks. We show that some fundamental survivability-related results such as Menger's Theorem only conditionally hold in time-varying networks. Then we analyze the complexity of computing the proposed metrics and develop several approximation algorithms. Finally, we conduct trace-driven simulations to demonstrate the application of our survivability framework to the robust design of a real-world bus communication network

A Survivable and Reliable Network Topological Design Model

This work is focused on the resolution of a mixed model for the design of large-sized networks. An algorithm is introduced, whose initial outcomes are promising in terms of topological robustness regarding connectivity and reliability. The algorithm combines the network survivability and the network reliability approaches. The problem of the topological design has been modeled based on the generalized Steiner problem with node-connectivity constraints (GSPNC), which is NP-hard. The aim of this study is to heuristically solve the GSP-NC model by designing low-cost highly connected topologies and to measure the reliability of such solutions with respect to a certain prefixed lower threshold. This research introduces a greedy randomized algorithm for the construction of feasible solutions for the GSP-NC and a local search algorithm based on the variable neighborhood search (VNS) method, customized for the GSP-NC. In order to compute the built network reliabilities, this work adapts the recursive variance reduction (RVR) technique, as a simulation method since the exact evaluation of this measurement is also NP-hard. The experimental tests were performed over a wide set of testing cases, which contained heterogeneous topologies, including instances of more than 200 nodes. The computational results showed highly competitive execution times, achieving minimal local optimal solutions of good quality fulfilling the imposed survivability and reliability conditions

Cellular decision-making bias: the missing ingredient in cell functional diversity

Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed.Comment: 18 pages; 6 figures, 2 tables, 4 supplemental figure

A Survivable & Reliable Network Topological Design Model

This paper is focused on the resolution of a mixed model for the design of large size networks which will be topologically robust regarding its connectivity and reliability. More precisely, we combined the Network Survivability & Network Reliability approaches. The problem of the topological design has been modeled based on the Generalized Steiner Problem with Node-Connectivity Constraints (GSP-NC), which is NP-Hard. Our aim is to heuristically solve the GSP-NC model by designing low cost highly connected topologies and to measure the reliability of such solutions with respect to a certain prefixed lower threshold. We introduce a Greedy Randomized algorithm for the construction of feasible solutions for the GSP-NC and a local search algorithm based on the Variable Neighbourhood Search (VNS) method customized for the GSP-NC. To compute the built networks reliabilities we adapted the Recursive Variance Reduction (RVR) technic as simulation method since the exact evaluation of this measurement is also NP-Hard. The experimental tests were performed over a wide set of testing cases which contained heterogeneous topologies, including instances of more than 200 nodes. The computational results showed highly competitive execution times, achieving minimal local optimal solutions of good quality fulfilling the imposed survivability and reliability conditions