Online algorithms make decisions based on past inputs, with the goal of being competitive against an algorithm that sees also future inputs. In this work, we introduce time-local online algorithms; these are online algorithms in which the output at any given time is a function of only T latest inputs. Our main observation is that time-local online algorithms are closely connected to local distributed graph algorithms: distributed algorithms make decisions based on the local information in the spatial dimension, while time-local online algorithms make decisions based on the local information in the temporal dimension. We formalize this connection, and show how we can directly use the tools developed to study distributed approximability of graph optimization problems to prove upper and lower bounds on the competitive ratio achieved with time-local online algorithms. Moreover, we show how to use computational techniques to synthesize optimal time-local algorithms.

Most ISPs use sophisticated traffic engineering strategies based on link weight optimizations to efficiently provision their backbone network and to serve intra-domain traffic. While traditionally, traffic is split among the shortest weighted paths using ECMP, recently, an additional dimension for optimization arose in the context of segment routing: traffic can be steered away from congested shortest paths by inserting intermediate destinations, so-called waypoints.

Distributed applications generate a significant amount of network traffic. By collocating frequently communicating nodes (e.g., virtual machines) on the same clusters (e.g., server or rack), we can reduce the network load and improve application performance. However, the communication pattern of different applications is often unknown a priori and may change over time, hence it needs to be learned in an online manner. This paper revisits the online balanced partitioning problem that asks for an algorithm that strikes an optimal tradeoff between the benefits of collocation (i.e., lower network load) and its costs (i.e., migrations). Our first contribution is a significantly improved deterministic lower bound of Ω(k · ℓ) on the competitive ratio, where ℓ is the number of clusters and k is the cluster size, even for a scenario in which the communication pattern is static and can be perfectly partitioned; we also provide an asymptotically tight upper bound of O(k·ℓ) for this scenario. For k = 3, we contribute an asymptotically tight upper bound of Θ(ℓ) for the general model in which the communication pattern can change arbitrarily over time. We improve the result for k = 2 by providing a strictly 6-competitive upper bound for the general model.

Modern communication networks support fast path restoration mechanisms which allow to reroute traffic in case of (possibly multiple) link failures, in a completely decentralized manner and without requiring global route reconvergence. However, devising resilient path restoration algorithms is challenging as these algorithms need to be inherently local. Furthermore, the resulting failover paths often have to fulfill additional requirements related to the policy and function implemented by the network, such as the traversal of certain waypoints (e.g., a firewall). This paper presents local algorithms which ensure a maximally resilient path restoration for a large family of product graphs, including the widely used tori and generalized hypercube topologies. Our algorithms provably ensure that even under multiple link failures, traffic is rerouted to the other endpoint of every failed link whenever possible (i.e. detouring failed links), enforcing waypoints and hence accounting for the network policy. The algorithms are particularly well-suited for emerging segment routing networks based on label stacks.

Distributed applications, including batch processing, streaming, scale-out databases, or machine learning, generate a significant amount of network traffic. By collocating frequently communicating nodes (e.g., virtual machines) on the same clusters (e.g., server or rack), we can reduce the network load and improve application performance. However, the communication pattern of different applications is often unknown a priori and may change over time, hence it needs to be learned in an online manner. This paper revisits the online balanced partitioning problem (introduced by Avin et al. at DISC 2016) that asks for an algorithm that strikes an optimal tradeoff between the benefits of collocation (i.e., lower network load) and its costs (i.e., migrations). Our first contribution is a significantly improved deterministic lower bound of Ω(k · ℓ) on the competitive ratio, where ℓ is the number of clusters and k is the cluster size, even for a scenario in which the communication pattern is static and can be perfectly partitioned; we also provide an asymptotically tight upper bound of O(k · ℓ) for this scenario. For k = 3, we contribute an asymptotically tight upper bound of Θ(ℓ) for the general model in which the communication pattern can change arbitrarily over time. In contrast to most prior work, our algorithms respect all capacity constraints and do not require resource augmentation.

We consider the SDN network update problem in which a controller wants to update the routes of k (unsplittable) flows from their old paths to the new paths, consistently, i.e., without temporary congestion. As updates communicated by the controller take effect asynchronously, the challenge is to perform these updates fast, i.e., using a minimal number of rounds (controller interactions). We present the first fast, i.e., polynomial-time solution for scheduling such congestion-free network updates, for two flows and in the node ordering model. We also show that the problem is already NP-hard for six flows. We complement our formal results with simulations.

Fast rerouting is an essential mechanism in any dependable communication network, allowing to quickly, i.e., locally, recover from network failures, without invoking the control plane. However, while locality ensures a fast reaction, the absence of global information also renders the design of highly resilient fast rerouting algorithms more challenging. In this paper, we study algorithms for fast rerouting in emerging Segment Routing (SR) networks, where intermediate destinations can be added to packets by nodes along the path. Our main contribution is a maximally resilient polynomial-time fast rerouting algorithm for SR networks based on a hypercube topology. Our algorithm is attractive as it preserves the original paths (and hence waypoints traversed along the way), and does not require packets to carry failure information. We complement our results with an integer linear program formulation for general graphs and exploratory simulation results.