Brief Announcement: On Self-Adjusting Skip List Networks

This paper explores the design of dynamic network topologies which adjust to the workload they serve, in an online manner. Such self-adjusting networks (SANs) are enabled by emerging optical technologies, and can be found, e.g., in datacenters. SANs can be used to reduce routing costs by moving frequently communicating nodes topologically closer. This paper presents SANs which provide, for the first time, provable working set guarantees: the routing cost between node pairs is proportional to how recently these nodes communicated last time. Our SANs rely on skip lists (which serve as the topology) and provide additional interesting properties such as local routing

Shared-object System Equilibria: Delay and Throughput Analysis

We consider shared-object systems that require their threads to fulfill the system jobs by first acquiring sequentially the objects needed for the jobs and then holding on to them until the job completion. Such systems are in the core of a variety of shared-resource allocation and synchronization systems. This work opens a new perspective to study the expected job delay and throughput analytically, given the possible set of jobs that may join the system dynamically. We identify the system dependencies that cause contention among the threads as they try to acquire the job objects. We use these observations to define the shared-object system equilibria. We note that the system is in equilibrium whenever the rate in which jobs arrive at the system matches the job completion rate. These equilibria consider not only the job delay but also the job throughput, as well as the time in which each thread blocks other threads in order to complete its job. We then further study in detail the thread work cycles and, by using a graph representation of the problem, we are able to propose procedures for finding and estimating equilibria, i.e., discovering the job delay and throughput, as well as the blocking time. To the best of our knowledge, this is a new perspective, that can provide better analytical tools for the problem, in order to estimate performance measures similar to ones that can be acquired through experimentation on working systems and simulations, e.g., as job delay and throughput in (distributed) shared-object systems

Analytical Results on the Performance of Shared Resource Allocation Systems

Resource allocation is a prevalent problem in a wide range of domains of computer science. Analytical tools that evaluate the performance of resource allocation systems allow us to compare with experimental ones, and utilize the design of such systems.We consider shared-object systems that require their threads to fulfill the system jobs by first acquiring sequentially the objects needed for the jobs and then holding on to them until the job completion. Such systems are in the core of a variety of shared-resource allocation and synchronization systems. We provide methods for estimating the performance of such systems in terms of expected task throughput and delay for completion. To the best of our knowledge, this is a new perspective that can provide better analytical tools for the problem, in order to estimate performance measures similar to ones that can be acquired through experimentation on working systems and simulations.We also study the problem of maximizing the energy utilization in the Smart Grid, where the energy supply becomes available in an online fashion (due to unpredictable energy sources) and the energy demand can have some flexibility (energy dispatch problem). Utilizing a proposed modeling of the energy dispatch problem as an online scheduling problem, we model supply-following demand in terms of the Adwords problem, in order to provide algorithmic solutions of measurable quality. In systems where demands are small compared to the individual supply, we prove a (1-1/e)-competitive ratio. For cases where this does not hold, we extend the Adwords problem to utilize dynamic budgets, and present an algorithm with a 1/2-competitive ratio

LightPIR: Privacy-Preserving Route Discovery for Payment Channel Networks

Payment channel networks are a promising approach to improve the scalability of cryptocurrencies: they allow to perform transactions in a peer-to-peer fashion, along multi-hop routes in the network, without requiring consensus on the blockchain. However, during the discovery of cost-efficient routes for the transaction, critical information may be revealed about the transacting entities. This paper initiates the study of privacy-preserving route discovery mechanisms for payment channel networks. In particular, we present LightPIR, an approach which allows a source to efficiently discover a shortest path to its destination without revealing any information about the endpoints of the transaction. The two main observations which allow for an efficient solution in LightPIR are that: (1) surprisingly, hub labelling algorithms - which were developed to preprocess "street network like" graphs so one can later efficiently compute shortest paths - also work well for the graphs underlying payment channel networks, and that (2) hub labelling algorithms can be directly combined with private information retrieval. LightPIR relies on a simple hub labeling heuristic on top of existing hub labeling algorithms which leverages the specific topological features of cryptocurrency networks to further minimize storage and bandwidth overheads. In a case study considering the Lightning network, we show that our approach is an order of magnitude more efficient compared to a privacy-preserving baseline based on using private information retrieval on a database that stores all pairs shortest paths

HIDE & SEEK: Privacy-Preserving Rebalancing on Payment Channel Networks

Payment channels effectively move the transaction load off-chain thereby successfully addressing the inherent scalability problem most cryptocurrencies face. A major drawback of payment channels is the need to ``top up\u27\u27 funds on-chain when a channel is depleted. Rebalancing was proposed to alleviate this issue, where parties with depleting channels move their funds along a cycle to replenish their channels off-chain. Protocols for rebalancing so far either introduce local solutions or compromise privacy. In this work, we present an opt-in rebalancing protocol that is both private and globally optimal, meaning our protocol maximizes the total amount of rebalanced funds. We study rebalancing from the framework of linear programming. To obtain full privacy guarantees, we leverage multi-party computation in solving the linear program, which is executed by selected participants to maintain efficiency. Finally, we efficiently decompose the rebalancing solution into incentive-compatible cycles which conserve user balances when executed atomically

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

Tailor your curves after your costume: Supply-following demand in Smart Grids through the Adwords problem

In deregulated energy markets, consumers -ranging from households to data centers- have access to multiple offers, often through multiple suppliers and energy carriers (i.e. electric, thermal) or through local generation, such as renewable energy sources and energy storage. Ideally, supply should match demand, leading to a balanced power grid, but this is challenging in practice: while some generation sources can be planned in advance (e.g. utility offers), others can be planned to a limited degree or cannot be planned altogether (e.g. storage and renewable energy sources respectively). In this context, we focus on how to address systematically this complex resource allocation problem in the presence of multiple actors.In this work, utilizing a proposed modeling of the energy dispatch problem as an online scheduling problem, we model supply-following demand in terms of the Adwords problem, in order to provide algorithmic solutions of measurable quality. Building on previous work, we extend the Adwords problem to incorporate load credit (i.e. storage) and we present and analyze online algorithms that can schedule demand, given availability constraints on supply, with guaranteed competitive ratio. In systems where demands are small compared to the individual supply, we prove a $\left(1-\frac{1}{e}\right)$-competitive ratio. For cases where this does not hold, we extend the Adwords problem to utilize dynamic budgets, and present an algorithm with a $\frac{1}{2}$-competitive ratio. We also provide examples of algorithmic performance in real world scenarios, by utilizing long term, fine-grained data from a pilot project in Sweden, while taking into account renewable generation on site