Successive Survivable Routing Research Articles

Spare capacity allocation using shared backup path protection for dual link failures

This paper extends the spare capacity allocation (SCA) problem from single failures to dual link failures on mesh-like IP or WDM networks. The SCA problem pre-plans traffic flows with mutually disjoint one working and two backup paths using the shared backup path protection (SBPP) scheme. The spare provision matrix (SPM) method aggregates per-flow based information and computes the shared spare capacity for dual link failures. When compared to previous two-flow based methods, it has better scalability and flexibility. The SCA problem is formulated as a non-linear integer programming model and partitioned into two sequential linear sub-models: one finds all primary backup paths, and the other finds all secondary backup paths. We extend the terminologies in the 1+1 and 1:1 link protection for the backup path protection: using “:” to indicate backup paths with shared spare capacity; and using “+” to indicate backup paths with dedicated capacity. Numerical results from five networks show that the network redundancy of the 1+1+1 dedicated path protection is in the range of 313–400%. It drops to 96–180% in the 1:1:1 shared backup path protection without loss of dual-link resiliency, but with the trade-off of the highest complexity on spare capacity shared by all backup paths. The 1+1:1 hybrid path protection provides intermediate redundancy at 187–310% with the moderate complexity. It has dedicated primary backup paths and shared secondary backup paths. We also compare passive sharing with active sharing. They perform spare capacity sharing either after or during the backup path routing, i.e., the active sharing approach performs share spare capacity within the backup path routing, while the passive sharing does so only after all backup paths are found. The active sharing approaches always achieve lower redundancy values than the passive sharing. The reduction percentages are about 12% for 1+1:1 and 25% for 1:1:1 respectively. The extension of the Successive Survivable Routing (SSR) heuristic algorithm to the dual failure case is given and the numerical results show that SSR maintains a 4–11% gap from optimal on small or medium networks, and scales up well on large networks.

Spare capacity reprovisioning for high availability shared backup path protection connections

Shared backup path protection (SBPP) has been widely studied in the Generalized MPLS (GMPLS) networks due to its efficient spare capacity sharing as well as simplicity and flexibility in service provisioning. This paper presents a novel availability evaluation strategy for the end-to-end (E2E) availability of an SBPP connection by considering up to two simultaneous failures, where the sequence of failures in a failure pattern is considered. To minimize the redundancy while meeting the E2E availability requirement, partial restoration is defined and embedded in the developed model, by which a novel parameter, called a protection level, is manipulated. Based on the proposed availability model, two Linear Program (LP) formulations are introduced, which aim to perform spare capacity reprovisioning along each link for dynamic allocation of SBPP connections under either failure-dependent or failure-independent policies. Extensive simulations are conducted to validate the proposed availability model and demonstrate the effectiveness of the spare capacity reprovisioning architecture. The proposed availability-aware spare capacity reprovisioning approaches are then implemented on top of a well known survivable routing scheme – Successive Survivable Routing (SSR), where the spare capacity saving ratio is taken as the performance measure. We will show that the proposed spare capacity reprovisioning framework is an effective approach for achieving the GMPLS-based recovery in packet-switched networks.

Spare Capacity Allocation in Two-Layer Networks

In this paper we consider the problem of provisioning spare capacity in two-layer backbone networks using shared backup path protection. First, two spare capacity allocation (SCA) optimization problems are formulated as integer linear programming (ILP) models for the cases of protection at the top layer against failures at the bottom layer. The first model captures failure propagation using overlay information between two layers for backup paths to meet diversity requirements. The second model improves bandwidth efficiency by moving spare capacity sharing from the top layer to the bottom layer. This exposes a tradeoff between bandwidth efficiency and extra cross-layer operation. Next, the SCA model for common pool protection is developed to allow spare capacity sharing between two layers. Our previous SCA heuristic technique, successive survivable routing (SSR) is extended for these optimization problems. Numerical results for a variety of networks indicate that the common pool protection is attractive to enhance bandwidth efficiency without loss of survivability and that the SSR heuristic quickly results in near optimal solutions

Group shared protection for spare capacity reconfiguration in optical networks

This paper introduces GSP, a group shared protection scheme, for wavelength division multiplexing (WDM) mesh networks with dynamically arrived connection requests. Based on the ( M: N) n control architecture, GSP has n mutually independent protection groups (PGs), each of which containing N shared risk link groups (SRLG) disjoint working paths protected by M protection paths. Due to the SRLG-disjointedness of the working paths in each PG, GSP not only allows the spare capacity to be totally sharable among the corresponding working paths, but also reduces the number of working paths affected due to a single link failure. Based on the framework, an integer linear program (ILP) formulation that can optimally reconfigure the spare capacity for a specific PG whenever a working–protection path-pair joins is proposed. This approach is appropriate for a dynamic traffic scenario where inter-arrival time is large and where arriving request can tolerate some delay, but may not be suitable where traffic arrival rate is high and incoming requests need to be served within a few seconds. To trade the performance (i.e., capacity efficiency) with the computation complexity, two heuristics, namely ring-shared protection (RSP) and link-shared protection (LSP) are proposed. The proposed schemes are compared with an exiting one, namely the successive survivable routing (SSR). The simulation results show that LSP, RSP and SSR yield similar performance in terms of resource sharing, whereas ILP outperforms all of them by 6–16%. Due to the limited number of working paths in each PG, ILP can handle dynamically arrived connection requests in a reasonable amount of time. Also, we find that the number of affected working paths in GSP is about half of that in SSR. We conclude that GSP provides a scalable and efficient solution for dynamic spare capacity reconfiguration following the ( M: N) n control architecture.

Approximating optimal spare capacity allocation by successive survivable routing

The design of survivable mesh based communication networks has received considerable attention in recent years. One task is to route backup paths and allocate spare capacity in the network to guarantee seamless communications services survivable to a set of failure scenarios. This is a complex multi-constraint optimization problem, called the spare capacity allocation (SCA) problem. This paper unravels the SCA problem structure using a matrix-based model, and develops a fast and efficient approximation algorithm, termed successive survivable routing (SSR). First, per-flow spare capacity sharing is captured by a spare provision matrix (SPM) method. The SPM matrix has a dimension the number of failure scenarios by the number of links. It is used by each demand to route the backup path and share spare capacity with other backup paths. Next, based on a special link metric calculated from SPM, SSR iteratively routes/updates backup paths in order to minimize the cost of total spare capacity. A backup path can be further updated as long as it is not carrying any traffic. Furthermore, the SPM method and SSR algorithm are generalized from protecting all single link failures to any arbitrary link failures such as those generated by Shared Risk Link Groups or all single node failures. Numerical results comparing several SCA algorithms show that SSR has the best trade-off between solution optimality and computation speed.