Graph augmentation is a fundamental and well-studied problem that arises in network optimization. We consider a new variant of this model motivated by reconfigurable communication networks. In this variant, we consider a given physical network and the measured communication demands between the nodes. Our goal is to augment the given physical network with a matching, so that the shortest path lengths in the augmented network, weighted with the demands, are minimal. We prove that this problem is NP-hard, even if the physical network is a cycle. We then use results from demand-aware network design to provide a constant-factor approximation algorithm for adding a matching in case that only a few nodes in the network cause almost all the communication. For general real-world communication patterns, we design and evaluate a series of heuristics that can deal with arbitrary graphs as the underlying network structure. Our algorithms are validated experimentally using real-world traces (from e.g., Facebook) of data centers.

Peer review, as a widely used practice to ensure the quality and integrity of publications, lacks a well-defined and common mechanism to self-incentivize virtuous behavior across all the conferences and journals. This is because information about reviewer efforts and author feedback typically remains local to a single venue, while the same group of authors and reviewers participate in the publication process across many venues. Previous attempts to incentivize the reviewing process assume that the quality of reviews and papers authored correlate for the same person, or they assume that the reviewers can receive physical rewards for their work. In this paper, we aim to keep track of reviewing and authoring efforts by users (who review and author) across different venues while ensuring self-incentivization. We show that our system, DecentPeeR, incentivizes reviewers to behave according to the rules, i.e., it has a unique Nash equilibrium in which virtuous behavior is rewarded.

Graph augmentation is a fundamental and well-studied problem that arises in network optimization. We consider a new variant of this model motivated by reconfigurable communication networks. In this variant, we differentiate between a given physical network and the measured communication demands between the nodes. Our goal is to minimize the weighted average shortest path length via matching augmentation, where the weights correspond to the communication frequency of any pair of nodes. We use results from demand-aware network design to provide a constant-factor approximation algorithm for adding a matching on a ring in case only a few nodes in the network cause almost all the communication. Since the problem is NP-hard, we design and evaluate a series of heuristics that can deal with arbitrary graphs as underlying network structures. We evaluate our heuristics on general real-world communication patterns and show that already with simple and efficient heuristics we are able to reach near-optimal quality.

We introduce self-adjusting partially ordered lists, a generalization of self-adjusting lists where additionally there may be constraints for the relative order of some nodes in the list. The lists self-adjust to improve performance while serving input sequences exhibiting favorable properties, such as locality of reference, but the constraints must be respected.We design a deterministic adjusting algorithm that operates without any assumptions about the input distribution and without maintaining frequency statistics or timestamps. Despite the more general model, we show that our deterministic algorithm performs closely to optimum (it is 4-competitive). In addition, we design a family of randomized algorithms with improved competitive ratios, handling also a more general rearrangement cost model, scaled by an arbitrary constant d ≥1. Moreover, we observe that different constraints influence the competitiveness of online algorithms, and we shed light on this aspect with a lower bound.We investigate the applicability of our self-adjusting lists in the context of network packet classification. Our evaluations show that our classifier performs similarly to a static list for low-locality traffic, but significantly outperforms Efficuts (by factor 7x), CutSplit (3.6x) and the static list (14x) for high locality and small rulesets.

We consider the fundamental problem of designing a self-adjusting tree, which efficiently and locally adapts itself towards the demand it serves (namely accesses to the items stored by the tree nodes), striking a balance between the benefits of such adjustments (enabling faster access) and their costs (reconfigurations). This problem finds applications, among others, in the context of emerging demand-aware and reconfigurable datacenter networks and features connections to self-adjusting data structures. Our main contribution is SeedTree, a dynamically optimal self-adjusting tree which supports local (i.e., greedy) routing, which is particularly attractive under highly dynamic demands. SeedTree relies on an innovative approach which defines a set of unique paths based on randomized item addresses, and uses a small constant number of items per node. We complement our analytical results by showing the benefits of SeedTree empirically, evaluating it on various synthetic and real-world communication traces.

Programmable wireless environments (PWEs) utilize internetworked intelligent metasurfaces to transform wireless propagation into a software-controlled resource. In this article, the interplay is explored between the user devices, the metasurfaces, and the PWE control system from the theory to the end-to-end implementation. This article first discusses the metasurface hardware and software, covering the complete workflow from the user device initialization to its final service via the PWE. Furthermore, to be compatible with the 5G and 6G wireless systems, the software-defined networking (SDN) paradigm is extended to achieve scalable internetworking and central control in PWE deployments with multiple metasurfaces and multihop communication. Subsequently, the set of SDN foundations is exploited in order to abstract the physics behind PWEs and a theoretical framework is established to describe and manipulate them in an algorithmic form. This can lead to smart radio environments that are readily accessible from various engineering disciplines, facilitating their integration into existing networks, wireless systems, and applications. This article is concluded by outlining strategies for the optimal placement of metasurfaces within a PWE-controlled space, open challenges in PWE security, specialized SDN integration issues, and theoretical problems toward the graph-driven modeling of PWEs.

To enhance performance, common processors feature relaxed memory models that reorder instructions. However, the correctness of concurrent programs is often dependent on the preservation of the program order of certain instructions. Thus, the instruction set architectures offer memory fences. Using fences is a subtle task with performance and correctness implications: using too few can compromise correctness and using too many can hinder performance. Thus, fence insertion algorithms that given the required program orders can automatically find the optimum fencing can enhance the ease of programming, reliability, and performance of concurrent programs. In this paper, we consider the class of programs with structured branch and loop statements and present a greedy and polynomial-time optimum fence insertion algorithm. The algorithm incrementally reduces fence insertion for a control-flow graph to fence insertion for a set of paths. In addition, we show that the minimum fence insertion problem with multiple types of fence instructions is NP-hard even for straight-line programs.