Safest path computation with multiple correlated failures is a challenging computational task, with several application possibilities. In communication backbone networks, for example, establishing a path as safe as possible between the two communication endpoints is a crucial component for achieving the ambitious availability requirements on which emerging technologies like autonomous driving, AR/VR applications, or telesurgery depend. In this paper, after proving the NP-hardness of the problem, we propose the Safest Path Ant Colony Optimization (SP-ACO) algorithm to solve the problem. The proposed algorithm is based on the Max-Min Ant System. Numerical experiments conducted on both real-world and synthetic inputs prove the effectiveness of the proposed approach. The proposed SP-ACO algorithm typically provides at least as safe paths as the state-of-the-art algorithms, even outperforming them in a significant share of the parameter settings. This grants a place for the SP-ACO among the best solutions for safest path finding in the presence of correlated failures.

Survivable routing is crucial in backbone networks to ensure connectivity, even during failures. During network design, groups of network elements prone to potential failure events are identified. These groups are referred to asShared Risk Link Groups(SRLGs). When these SRLGs consist of a set of links intersected by a connected region of the plane, they are termed regional-SRLGs. A recent study has presented a polynomial-time algorithm for finding amaximum number of regional-SRLG-disjoint pathsbetween two given nodes in a planar topology, where the paths are node-disjoint. However, existing algorithms for this problem are not practical due to their runtime and implementation complexities. This paper investigates a more general model in two aspects. First, instead of node-disjointness, we search for non-crossing regional-SRLG-disjoint paths. Second, we show how the algorithm can be extended to solve problems in directed networks. It introduces an efficient and easily implementable algorithmic framework, leveraging an arbitrarily chosen shortest path finding subroutine for graphs with possibly negative weights. Depending on the subroutine chosen, the framework either improves the previous worst-case runtime complexity or can solve the problem with high probability (w.h.p.) in near-linear expected time. The proposed framework enables the first additive approximation for a more generalNP-hard version of the problem, where the objective is to find the maximum number of regional-SRLG-disjoint paths. We validate our findings through extensive simulations.

Novel telecommunication systems build on a cloudified architecture running softwarized network services as disaggregated virtual network functions (VNFs) on commercial off-the-shelf (COTS) hardware to improve costs and flexibility. Given the stringent processing deadlines of modern applications, these systems are critically dependent on a closed-loop control algorithm to orchestrate the execution of the disaggregated components. At the moment, however, the formal model for implementing such real-time control loops is mostly missing. In this paper, we introduce a new real-time VNF execution environment that runs entirely on COTS hardware. First, we define a comprehensive formal model that enables us to reason about packet processing delays across disaggregated VNF processing chains analytically. Then we integrate the model into a gradient-optimization control algorithm to provide optimal scheduling for real-time infocommunication services in a programmable way. We present experimental evidence that our model gives a proper delay estimation on a real software switch. We evaluate our control algorithm on multiple representative use cases using a software switch simulator. Our results show the algorithm drives the system to a real-time capable state in just a few control periods even in case of complex services.

Survivable routing is crucial in backbone networks to ensure connectivity, even during failures. At network design, groups of network elements prone to potential failure events are identified. These groups are referred to as Shared Risk Link Groups (SRLGs), and if they are a set of links intersected by a connected region of the plane, we call them regional-SRLGs. A recent study has presented a polynomial-time algorithm for finding a maximum number of regional-SRLG-disjoint paths between two given nodes in a planar topology, with the paths being nodedisjoint. However, existing algorithms for this problem are not practical due to their runtime and implementation complexities.This paper investigates a more general model, the maximum number of non-crossing, regional-SRLG-disjoint paths problem. It introduces an efficient and easily implementable algorithmic framework, leveraging an arbitrarily chosen shortest path finding subroutine for graphs with possibly negative weights. Depending on the subroutine chosen, the framework improves the previous worst-case runtime complexity, or can solve the problem w.h.p. in near-linear expected time. The proposed framework enables the first additive approximation for a more general ${\mathcal{N}}{\mathcal{P}}$ -hard version of the problem, where the objective is to find the maximum number of regional-SRLG-disjoint paths. We validate our findings through extensive simulations.

Abstract This article deals with the complexity of problems related to finding cost‐efficient, disaster‐aware cable routes. We overview various mathematical problems studied to augment a backbone network topology to make it more robust against regional failures. These problems either consider adding a single cable, multiple cables, or even nodes too. They adapt simplistic or more sophisticated regional failure models. Their objective is to identify the network's weak points or minimize the investment cost concerning the risk of a network outage. We investigate the tradeoffs in mathematical modeling for the same real‐world scenario, where more sophisticated models face more computationally challenging problems. We have seen how efficiently computational geometry algorithms can be used to solve simplified problems even for sufficiently large networks. In this article, we aim to understand why different mathematical models formulated for the same real‐world scenario can or cannot be solved efficiently. In particular, we show simplistic mathematical models that formulate NP‐hard problems.

P4 is a widely used Domain-specific Language for Programmable Data Planes. A critical step in P4 compilation is finding a feasible and efficient mapping of the high-level P4 source code constructs to the physical resources exposed by the underlying hardware, while meeting data and control flow dependencies in the program. In this paper, we take a new look at the algorithmic aspects of this problem, with the motivation to understand the fundamental theoretical limits and obtain better P4 pipeline embeddings, and to speed up practical P4 compilation times for RMT and dRMT target architectures. We report mixed results: we find that P4 compilation is computationally hard even in a severely relaxed formulation, and there is no polynomial-time approximation of arbitrary precision (unless$\mathcal {P}$=$\mathcal {N}$$\mathcal {P}$), while the good news is that, despite its inherent complexity, P4 compilation is approximable in linear time with a small constant bound even for the most complex, nearly real-life models.

The current best practice in survivable routing is to compute link or node disjoint paths in the network topology graph. It can protect single-point failures; however, several failure events may cause the interruption of multiple network elements. The set of network elements subject to potential failure events is called Shared Risk Link Group (SRLG), identified during network planning. Unfortunately, for any given list of SRLGs, finding two paths that can survive a single SRLG failure is NP-Complete. In this paper, we provide a polynomial-time SRLG-disjoint routing algorithm for planar network topologies and a large set of SRLGs. Namely, we focus on regional failures, where the failed network elements must not be far from each other. We use a flexible definition of regional failure, where the only restrictions are that i) the topology is a planar graph, ii) each SRLG forms a set of connected edges in the dual of the planar graph, and iii) for each node$v$, the links incident to$v$are part of an SRLG. The proposed algorithm is based on a max-min theorem. Through extensive simulations, we show that the algorithm scales well with the network size, and one of the paths returned by the algorithm is only 4% longer than the shortest path on average.

The current best practice in survivable routing is to compute link or node disjoint paths in the network topology graph. It can protect single-point failures; however, several failure events may cause the interruption of multiple network elements. The set of network elements subject to potential failure events is called Shared Risk Link Group (SRLG), identified during network planning. Unfortunately, for any given list of SRLGs, finding two paths that can survive a single SRLG failure is NP-Complete. In this paper, we provide a polynomial-time SRLG-disjoint routing algorithm for planar network topologies and a large set of SRLGs. Namely, we focus on regional failures, where the failed network elements must not be far from each other. We use a flexible definition of regional failure, where the only restriction is that the topology is a planar graph, and the SRLGs form a set of connected edges in the dual of the planar graph. The proposed algorithm is based on a max-min theorem. Through extensive simulations, we show that the algorithm scales well with the network size, and one of the paths returned by the algorithm is only 4% longer than the shortest path on average.

Modeling and listing the joint device failures of telecommunication optical backbone networks caused by large-scale regional disasters is the aim of my dissertation [1] digested in the following. The use-cases of these failure lists include helping the operators of modern telecommunication networks to meet the predefined Quality-of-Service (QoS) conditions. Informally speaking, the task tackled is translating the composed geometric problem of protecting telecommunication networks against regional failures to small-sized purely combinatorial and probabilistic problems, respectively. The development of this translation framework relied on the following pillars: 1) constructing failure models that make the best use of the data available, 2) giving fast algorithms for determining the resulting failure lists, 3) providing a theoretical and practical analysis of the complexity of the algorithms and the properties of the failure lists. The offered failure lists can be leveraged for enhancing network preparedness against disasters.

To evaluate the expected availability of a backbone network service, the administrator should consider all possible failure scenarios under the specific service availability model stipulated in the corresponding service-level agreement. Given the increase in natural disasters and malicious attacks with geographically extensive impact, considering only independent single component failures is often insufficient. This paper builds a stochastic model of geographically correlated link failures caused by disasters to estimate the hazards an optical backbone network may be prone to and to understand the complex correlation between possible link failures. We first consider link failures only and later extend our model also to capture node failures. With such a model, one can quickly extract essential information such as the probability of an arbitrary set of network resources to fail simultaneously, the probability of two nodes to be disconnected, the probability of a path to survive a disaster. Furthermore, we introduce standard data structures and a unified terminology on Probabilistic Shared Risk Link Groups (PSRLGs), along with a pre-computation process, which represents the failure probability of a set of resources succinctly. In particular, we generate a quasilinear-sized data structure in polynomial time, which allows the efficient computation of the cumulative failure probability of any set of network elements. Our evaluation is based on carefully pre-processed seismic hazard data matched to real-world optical backbone network topologies.

Many recent studies shed light on the vulnerability of networks against large-scale natural disasters. The corresponding network failures, called regional failures, are manifested at failing multiple network elements that are physically close to each other. The recovery mechanisms of current backbone networks protect failures listed as Shared Risk Link Groups (SRLGs). We aim to design an algorithm for the routing engines, which can generate a reasonable list of SRLGs based on the limited geometric information available. As a first step towards this direction, in this paper, we propose a limited geographic information failure model for the network topology that enables efficient algorithms to compute the set of links that are expected to be close to each other. More precisely, we work with (1) relative node positions without knowing the real distances, (2) an area in the map defines the route of each physical cable, and (3) a regional failure is a circular disk with k=0,1, ... nodes in its interior. We describe an efficient algorithm for listing SRLGs based on our limited geographic information failure model and show that under realistic assumptions, the obtained list of SRLGs is short, having approximately 1.2 n and 2.2n elements for k=0 and k=1, respectively, where n is the number of nodes of the network.

Abstract Several recent works shed light on the vulnerability of networks against regional failures, which are failures of multiple pieces of equipment in a geographical region as a result of a natural disaster. To enhance the preparedness of a given network to natural disasters, regional failures and associated Shared Risk Link Groups (SRLGs) should be first identified. For simplicity, most of the previous works assume the network is embedded on a Euclidean plane. Nevertheless, they are on the Earth's surface; this assumption causes distortion. In this work, we generalize some of the related results on the plane to the sphere. In particular, we focus on algorithms for listing SRLGs as a result of regional failures of circular or other fixed shape.

At backbone network planning, an important task is to identify the failures to get prepared for. Technically, a list of link sets, called Shared Risk Link Groups (SRLG), is defined. The observed reliability of network services strongly depends on how carefully this list was selected and whether it contains every high-risk failure event. Regional failures often cause the breakdown of multiple elements of the network, which are physically close to each other. In this article, we show that operators should prepare a network for only a small number of possible regional failure events. In particular, we give an approach to generate the list of SRLGs that hit every possible circular disk shaped disaster of a given radius r. We show that this list has O((n + x)ρr) SRLGs, where n is the number of nodes in the network and x is the number of link crossings, and ρ, is the maximal number of links that could be hit by a circular disaster of radius r. We give a fast polynomial algorithm to enumerate the list of SRLGs and show that its worst-case time complexity is asymptotically optimal under some practical restrictions. Finally, through extensive simulations, we show that this list in practice has a size of ≈ 1.2n.

Data analysis is widely used in all domains of the economy. While the amount of data to process grows, the time criteria and the resource consumption constraints get stricter. These phenomena call for advanced resource orchestration for the big data applications. The challenge is actually even greater at the advent of edge computing: orchestration of big data resources in a hybrid edge-cloud infrastructure is challenging. The difficulty stems from the fact that wide-area networking and all its well-known issues come into play and affect the performance of the application. In this paper we present the steps we made towards network-aware big data application design over such distributed systems. We propose a HDFS block placement algorithm for the network reliability problem we identify in geographically distributed topologies. The heuristic algorithm we propose provides better big data application performance compared to the default block placement method. We implement our solution in our simulation environment and show the improved quality of big data applications.

In order to evaluate the expected availability of a service, a network administrator should consider all possible failure scenarios under the specific service availability model stipulated in the corresponding service-level agreement. Given the increase in natural disasters and malicious attacks with geographically extensive impact, considering only independent single link failures is often insufficient. In this paper, we build a stochastic model of geographically correlated link failures caused by disasters, in order to estimate the hazards a network may be prone to, and to understand the complex correlation between possible link failures. With such a model, one can quickly extract information, such as the probability of an arbitrary set of links to fail simultaneously, the probability of two nodes to be disconnected, the probability of a path to survive a failure, etc. Furthermore, we introduce a pre-computation process, which enables us to succinctly represent the joint probability distribution of link failures. In particular, we generate, in polynomial time, a quasilinear-sized data structure, with which the joint failure probability of any set of links can be computed efficiently.

Shared Risk Link Group (SRLG) is a failure the network is prepared for, which contains a set of links subject to a common risk of single failure. During planning a backbone network, the list of SRLGs must be defined very carefully, because leaving out one likely failure event will significantly degrade the observed reliability of the network. Regional failures are manifested at multiple locations of the network, which are physically close to each other. In this paper we show that operators should prepare a network for only a small number of possible regional failure events. In particular, we give a fast systematic approach to generate the list of SRLGs that cover every possible circular disk failure of a given radius r. We show that this list has O((n + x)σr) SRLGs, where n is the number of nodes in the network, x is the number of link crossings, and σris the maximal number of links that could be hit by a disk failure of radius r. Finally through extensive simulations we show that this list in practice has size of ≈ 1.2 n.