Improving RNA Assembly via Safety and Completeness in Flow Decompositions

Extended version of RECOMB 2022 paperDecomposing a network flow into weighted paths is a problem with numerous applications, ranging from networking, transportation planning, to bioinformatics. In some applications we look for a decomposition that is optimal with respect to some property, such as the number of paths used, robustness to edge deletion, or length of the longest path. However, in many bioinformatic applications, we seek a specific decomposition where the paths correspond to some underlying data that generated the flow. In these cases, no optimization criteria guarantee the identification of the correct decomposition. Therefore, we propose to instead report the safe paths, which are subpaths of at least one path in every flow decomposition. In this work, we give the first local characterization of safe paths for flow decompositions in directed acyclic graphs, leading to a practical algorithm for finding the complete set of safe paths. In addition, we evaluate our algorithm on RNA transcript data sets against a trivial safe algorithm (extended unitigs), the recently proposed safe paths for path covers (TCBB 2021) and the popular heuristic greedy-width. On the one hand, we found that besides maintaining perfect precision, our safe and complete algorithm reports a significantly higher coverage (≈50 compared with the other safe algorithms. On the other hand, the greedy-width algorithm although reporting a better coverage, it also reports a significantly lower precision on complex graphs (for genes expressing a large number of transcripts). Overall, our safe and complete algorithm outperforms (by ≈20 greedy-width on a unified metric (F-score) considering both coverage and precision when the evaluated data set has a significant number of complex graphs. Moreover, it also has a superior time (4−5×) and space performance (1.2−2.2×), resulting in a better and more practical approach for bioinformatic applications of flow decomposition.Peer reviewe

Online Multicast Traffic Engineering for Software-Defined Networks

Previous research on SDN traffic engineering mostly focuses on static traffic, whereas dynamic traffic, though more practical, has drawn much less attention. Especially, online SDN multicast that supports IETF dynamic group membership (i.e., any user can join or leave at any time) has not been explored. Different from traditional shortest-path trees (SPT) and graph theoretical Steiner trees (ST), which concentrate on routing one tree at any instant, online SDN multicast traffic engineering is more challenging because it needs to support dynamic group membership and optimize a sequence of correlated trees without the knowledge of future join and leave, whereas the scalability of SDN due to limited TCAM is also crucial. In this paper, therefore, we formulate a new optimization problem, named Online Branch-aware Steiner Tree (OBST), to jointly consider the bandwidth consumption, SDN multicast scalability, and rerouting overhead. We prove that OBST is NP-hard and does not have a $|D_{max}|^{1-\epsilon}$-competitive algorithm for any $\epsilon >0$, where $|D_{max}|$ is the largest group size at any time. We design a $|D_{max}|$-competitive algorithm equipped with the notion of the budget, the deposit, and Reference Tree to achieve the tightest bound. The simulations and implementation on real SDNs with YouTube traffic manifest that the total cost can be reduced by at least 25% compared with SPT and ST, and the computation time is small for massive SDN.Comment: Full version (accepted by INFOCOM 2018

Too many SDN rules? Compress them with MINNIE

International audienceSoftware Defined Networking (SDN) is gaining momentum with the support of major manufacturers. While it brings flexibility in the management of flows within the data center fabric, this flexibility comes at the cost of smaller routing table capacities. In this paper, we investigate compression techniques to reduce the forwarding information base (FIB) of SDN switches. We validate our algorithm, called MINNIE, on a real testbed able to emulate a 20 switches fat tree architecture. We demonstrate that even with a small number of clients, the limit in terms of number of rules is reached if no compression is performed, increasing the delay of all new incoming flows. MINNIE, on the other hand, reduces drastically the number of rules that need to be stored with a limited impact on the packet loss rate. We also evaluate the actual switching and reconfiguration times and the delay introduced by the communications with the controller

Online unicasting and multicasting in software-defined networks

Software-Defined Networking (SDN) has emerged as the paradigm of the next-generation networking through separating the control plane from the data plane. In a software-defined network, the forwarding table at each switch node usually is implemented by expensive and power-hungry Ternary Content Addressable Memory (TCAM) that only has limited numbers of entries. In addition, the bandwidth capacity at each link is limited as well. Provisioning quality services to users by admitting their requests subject to such critical network resource constraints is a fundamental problem, and very little attention has been paid. In this paper, we study online unicasting and multicasting in SDNs with an objective of maximizing the network throughput under network resource constraints, for which we first propose a novel cost model to accurately capture the usages of network resources at switch nodes and links. We then devise two online algorithms with competitive ratios O(log n) and O(Kϵlog n) for online unicasting and multicasting, respectively, where n is the network size, K is the maximum number of destinations in any multicast request, and ϵ is a constant with 0 < ϵ ≤ 1. We finally evaluate the proposed algorithms empirically through simulations. The simulation results demonstrate that the proposed algorithms are very promising

Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising

On the Complexity of Compressing Two Dimensional Routing Tables with Order

International audienceMotivated by routing in telecommunication network using Software Defined Network (SDN) technologies, we consider the following problem of finding short routing lists using aggregation rules. We are given a set of communications X , which are distinct pairs (s, t) ⊆ S × T , (typically S is the set of sources and T the set of destinations), and a port function π : X → P where P is the set of ports. A routing list R is an ordered list of triples which are of the form (s, t, p), If r(s, t) = π(s, t), then we say that (s, t) is properly routed by R and if all communications of X are properly routed, we say that R emulates (X , π). The aim is to find a shortest routing list emulating (X , π). In this paper, we carry out a study of the complexity of the two dual decision problems associated to it. Given a set of communication X , a port function π and an integer k, the A preliminary short version of this work has appeared in [7]. 2 Frédéric Giroire et al. first one called Routing List (resp. the second one, called List Reduction) consists in deciding whether there is a routing list emulating (X , π) of size at most k (resp. |X | − k). We prove that both problems are NP-complete. We then give a 3-approximation for List Reduction, which can be generalized to higher dimensions. We also give a 4-approximation for Routing List in the fundamental case when there are only two ports (i.e. |P | = 2), X = S × T and |S| = |T |

MINNIE: an SDN World with Few Compressed Forwarding Rules

Software Defined Networking (SDN) is gaining momentum with the support of major manufacturers. While it brings flexibility in the management of flows within the data center fabric, this flexibility comes at the cost of smaller routing table capacities. Indeed, the Ternary Content Addressable Memory (TCAM) needed by SDN devices has smaller capacities than CAMs used in legacy hardware. In this paper, we investigate compression techniques to maximize the utility of SDN switches forwarding tables. We validate our algorithm, called \algo, with intensive simulations for well-known data center topologies, to study its efficiency and compression ratio for a large number of forwarding rules. Our results indicate that \algo scales well, being able to deal with around a million of different flows with less than 1000 forwarding entry per SDN switch, requiring negligible computation time. To assess the operational viability of MINNIE in real networks, we deployed a testbed able to emulate a k=4 fat-tree data center topology. We demonstrate on one hand, that even with a small number of clients, the limit in terms of number of rules is reached if no compression is performed, increasing the delay of new incoming flows. MINNIE, on the other hand, reduces drastically the number of rules that need to be stored, with no packet losses, nor detectable extra delays if routing lookups are done in ASICs.Hence, both simulations and experimental results suggest that \algo can be safely deployed in real networks, providing compression ratios between 70% and 99%