Designing efficient selection techniques for graphical user interfaces (GUIs) is fundamental in HCI research.We derive selection techniques based on the multiple process model, a theory that details the motor control processes during goal-directed movements.Specifically, we deduce three theoretical assumptions on how control processes of pre-planning, impulse control, and limb-target control could influence selection movements when adjusting GUI elements, including visual feedback, cursor position, and target position.Corresponding to our assumptions, we develop three techniques that hide the cursor when a target is highlighted, snap the cursor when selection begins, and expand clustered objects during selection movements.After that, we pre-register the assumptions and research methodology and evaluate the techniques in three crowdsourcing-based pointing studies.Our results show that all techniques improved the selection efficiency compared to established baselines.We further discuss the design implications and reflect on how we derived techniques from theory.

We present a deployable dashboard for next-day stock prediction that combines a Long Short-Term Memory (LSTM) model with daily news-based sentiment. Using a 20-day sliding window of normalised close and aggregated VADER sentiment, the model forecasts the next-day close for large-cap equities. Across AAPL, MSFT, TSLA, and GOOGL, the system achieved MAE in the$2.5-\unicode{x0024} 5.0$range and directional accuracy up to 58%. In a two-month backtest, a simple rules-based trading bot driven by the model produced a$+2.5 \%$return with a 59% win rate, outperforming naïve and moving-average baselines. Gains were most evident during news-sensitive periods. Limitations include short horizons, lexicon-based sentiment, and simplified execution assumptions. The dashboard exposes forecasts, sentiment summaries, and simulated trades, offering practical utility for exploratory analysis and teaching.

While Deep Learning (DL) technologies are a promising tool to solve networking problems that map to classification tasks, their computational complexity is still too high with respect to real-time traffic measurements requirements. To reduce the DL inference cost, we propose a novel caching paradigm, that we named approximate-key caching, which returns approximate results for lookups of selected input based on cached DL inference results. While approximate cache hits alleviate DL inference workload and increase the system throughput, they however introduce an approximation error. As such, we couple approximate-key caching with an error-correction principled algorithm, that we named auto-refresh. We analytically model our caching system performance for classic LRU and ideal caches, we perform a trace-driven evaluation of the expected performance, and we compare the benefits of our proposed approach with the state-of-the-art similarity caching – this testifies the practical interest of our proposal.

While the cost of the access network could be considerably reduced by the use of caching, this is not currently happening because content providers (CPs), who alone have the detailed demand data required for optimal content placement, have no natural incentive to use them to minimize access network operator (ANO) expenditure. We argue that ANOs should therefore provide such an incentive in the form of direct subsidies paid to the CPs in proportion to the realized savings. We apply coalition game theory to design the required subsidy framework and propose a distributed algorithm, based on Lagrangian decomposition, allowing ANOs and CPs to collectively realize the optimal memory for bandwidth tradeoff. The considered access network is a cache hierarchy with per-CP central office caches, accessed by all ANOs, at the apex, and per-ANO dedicated bandwidth and storage resources at the lower levels, including wireless base stations, that must be shared by multiple CPs.

Software switches are increasingly used in network function virtualization (NFV) to route traffic between virtualized network functions (VNFs) and physical network interface cards (NICs). Understanding of alternative switch designs remains deficient, however, in the absence of a comprehensive, comparative performance analysis. In this paper, we propose a methodology intended to be fair and use it to compare the performance of seven state-of-the-art software switches. We first explore their respective design spaces and then compare their performance under four representative test scenarios. Each scenario corresponds to a specific case of routing NFV traffic between NICs and/or VNFs. Our experimental results show that no single software switch prevails in all scenarios. It is therefore important to choose the one that is best adapted to a given use-case. The presented results and analysis bring a better understanding of design tradeoffs and identify potential bottlenecks that limit the performance of software switches.

Interest in software networking has grown significantly since the introduction of Network Function Virtualization (NFV). Software switches are used in NFV to steer traffic between different virtualized network functions and physical Network Interface Cards (NICs). It is becoming more and more important to objectively evaluate and compare the performance of the multiple alternative implementations that have recently been proposed. A comprehensive performance analysis is still missing for two main reasons: (i) the amount of time required to configure and compare all such tools is enormous; (ii) it is very difficult to define a proper methodology to compare different solutions in a fair manner. In this paper we propose a methodology based on four simple yet representative test scenarios used to evaluate the performance of software switches. We apply this methodology to measure throughput and latency metrics for 6 state-of-the-art software switches namely, OVS-DPDK, snabb, BESS, FastClick, VPP and netmap VALE. Our work constitutes a first step to building a better understanding of design tradeoffs and identifying performance bottlenecks.

The paper revisits the performance evaluation of caching in a Named Data Networking (NDN) router where the content store (CS) is supplemented by a pending interest table (PIT) which aggregates requests for a given content that arrive within the download delay. We extend prior work on caching with non-zero download delay by proposing a novel mathematical framework that is applicable to general traffic models and alternative cache insertion policies. Specifically we consider the impact of time locality in demand due to finite content lifetimes and we evaluate the use of an LRU filter to improve CS hit rate performance. The analysis is used to demonstrate that the impact of the PIT on upstream bandwidth reduction is significant only for relatively small content catalogues or high average request rate per content. We also show that the filter can be counterproductive when contents have finite lifetimes and traffic intensity is low.

Datacenter network (DCN) design has been actively researched for over a decade. Solutions proposed range from end-to-end transport protocol redesign to more intricate, monolithic and cross-layer architectures. Despite this intense activity, to date we remark the absence of DCN proposals based on simple fair scheduling strategies. In this paper, we evaluate the effectiveness of FQ-CoDel in the DCN environment. Our results show, (i) that average throughput is greater than that attained with DCN tailored protocols like DCTCP, and (ii) the completion time of short flows is close to that of state-of-art DCN proposals like pFabric. Good enough performance and striking simplicity make FQ-CoDel a serious contender in the DCN arena.

The content-centric networking (CCN) paradigm proposed by PARC holds considerable promise as the architecture for the future Internet but remains incomplete. In this paper we argue that it is necessary to supplement CCN with mechanisms enabling controlled sharing of network bandwidth by concurrent flows. Traffic control is necessary to ensure low latency for conversational and streaming flows and to realize satisfactory bandwidth sharing between elastic flows. These objectives can be realized using “network neutral” traffic controls based on per-flow fair bandwidth sharing. The paper describes necessary CCN mechanisms and discusses the strategies to be implemented by end systems and routers. A simulation-based performance evaluation demonstrates the effectiveness of the proposed mechanisms and and the impact of possible strategies. We describe our experience in implementing some of the mechanisms using the CCNx prototype software.

Passive optical networks are increasingly used for access to the Internet and it is important to understand the performance of future long-reach, multi-channel variants. In this paper we discuss requirements on upstream dynamic wavelength and bandwidth allocation (DWBA) and propose a simple novel algorithm that is considerably more efficient than classical approaches. We demonstrate that the algorithm emulates a multi-server polling system and deduce capacity formulas that are valid for general traffic processes.

This paper concerns the design of distributed algorithms for sharing network bandwidth resources among contending flows. The classical fairness notion is the so-called max-min fairness. The alternative proportional fairness criterion has recently been introduced by F. Kelly (see Eur. Trans. Telecommun., vol.8, p.33-7, 1997); we introduce a third criterion, which is naturally interpreted in terms of the delays experienced by ongoing transfers. We prove that fixed-size window control can achieve fair bandwidth sharing according to any of these criteria, provided scheduling at each link is performed in an appropriate manner. We then consider a distributed random scheme where each traffic source varies its sending rate randomly, based on binary feedback information from the network. We show how to select the source behavior so as to achieve an equilibrium distribution concentrated around the considered fair rate allocations. This stochastic analysis is then used to assess the asymptotic behavior of deterministic rate adaption procedures.

We introduce a class of Markov models, termed best-effort networks, designed to capture performance indices such as mean transfer times in data networks with best-effort service. We introduce the so-called min bandwidth sharing policy as a conservative approximation to the classical max-min policy. We establish necessary and sufficient ergodicity conditions for best-effort networks under the min policy. We then resort to the mean field technique of statistical physics to analyze network performance deriving fixed point equations for the stationary distribution of large symmetrical best-effort networks. A specific instance of such networks is the star-shaped network which constitutes a plausible model of a network with an overprovisioned backbone. Numerical and analytical study of the equations allows us to state a number of qualitative conclusions on the impact of traffic parameters (link loads) and topology parameters (route lengths) on mean document transfer time.

This paper concerns the design of distributed algorithms for sharing network bandwidth resources among contending flows. The classical fairness notion is the so-called max-min fairness; Kelly (see Europ. Trans. Telecom. vol.8 p.33-37, 1997) has previously introduced the alternative proportional fairness criterion; we introduce a third criterion, which is naturally interpreted in terms of the delays experienced by ongoing transfers. We prove that fixed size window control can achieve fair bandwidth sharing according to any of these criteria, provided scheduling at each link is performed in an appropriate manner. We next consider a distributed random scheme where each traffic source varies its sending rate randomly, based on binary feedback information from the network. We show how to select the source behaviour so as to achieve an equilibrium distribution concentrated around the considered fair rate allocations. This stochastic analysis is then used to assess the asymptotic behaviour of deterministic rate adoption procedures.

Recent technological progress is making possible the creation of a new form of videoconference service based on a public network interconnecting private studios located on user premises. We suppose that transmission links in such a network would be reserved in advance by users as and when they arrange their meeting. This and other significant differences with telephony-like services make the use of traditional teletraffic models inappropriate for network dimensioning and performance evaluation. In this paper, we propose an original model of the videoconference reservation traffic process in which essential parameters are the size and distribution of the studio population and the mean studio busy hour utilization rate. This model is used to study certain network architectures in order to identify the effect on link and node dimensions of major design options. First conclusions suggest that the most economical structure might be a simple star network where all studios are connected directly to a central switching facility. Advance reservation makes it possible to employ elaborate routing algorithms, such as call rearrangement, which might be exploited to optimize network or switching node design.