Compact Routing on Internet-Like Graphs

The Thorup-Zwick (TZ) routing scheme is the first generic stretch-3 routing scheme delivering a nearly optimal local memory upper bound. Using both direct analysis and simulation, we calculate the stretch distribution of this routing scheme on random graphs with power-law node degree distributions, $P_k \sim k^{-\gamma}$. We find that the average stretch is very low and virtually independent of $\gamma$. In particular, for the Internet interdomain graph, $\gamma \sim 2.1$, the average stretch is around 1.1, with up to 70% of paths being shortest. As the network grows, the average stretch slowly decreases. The routing table is very small, too. It is well below its upper bounds, and its size is around 50 records for $10^4$-node networks. Furthermore, we find that both the average shortest path length (i.e. distance) $\bar{d}$ and width of the distance distribution $\sigma$ observed in the real Internet inter-AS graph have values that are very close to the minimums of the average stretch in the $\bar{d}$- and $\sigma$-directions. This leads us to the discovery of a unique critical quasi-stationary point of the average TZ stretch as a function of $\bar{d}$ and $\sigma$. The Internet distance distribution is located in a close neighborhood of this point. This observation suggests the analytical structure of the average stretch function may be an indirect indicator of some hidden optimization criteria influencing the Internet's interdomain topology evolution.Comment: 29 pages, 16 figure

The Computational Power of Beeps

In this paper, we study the quantity of computational resources (state machine states and/or probabilistic transition precision) needed to solve specific problems in a single hop network where nodes communicate using only beeps. We begin by focusing on randomized leader election. We prove a lower bound on the states required to solve this problem with a given error bound, probability precision, and (when relevant) network size lower bound. We then show the bound tight with a matching upper bound. Noting that our optimal upper bound is slow, we describe two faster algorithms that trade some state optimality to gain efficiency. We then turn our attention to more general classes of problems by proving that once you have enough states to solve leader election with a given error bound, you have (within constant factors) enough states to simulate correctly, with this same error bound, a logspace TM with a constant number of unary input tapes: allowing you to solve a large and expressive set of problems. These results identify a key simplicity threshold beyond which useful distributed computation is possible in the beeping model.Comment: Extended abstract to appear in the Proceedings of the International Symposium on Distributed Computing (DISC 2015

A New Distribution-Sensitive Secure Sketch and Popularity-Proportional Hashing

Motivated by typo correction in password authentication, we investigate cryptographic error-correction of secrets in settings where the distribution of secrets is a priori (approximately) known. We refer to this as the distribution-sensitive setting. We design a new secure sketch called the layer-hiding hash (LHH) that offers the best security to date. Roughly speaking, we show that LHH saves an additional log H_0(W) bits of entropy compared to the recent layered sketch construction due to Fuller, Reyzin, and Smith (FRS). Here H_0(W) is the size of the support of the distribution W. When supports are large, as with passwords, our new construction offers a substantial security improvement. We provide two new constructions of typo-tolerant password-based authentication schemes. The first combines a LHH or FRS sketch with a standard slow-to-compute hash function, and the second avoids secure sketches entirely, correcting typos instead by checking all nearby passwords. Unlike the previous such brute-force-checking construction, due to Chatterjee et al., our new construction uses a hash function whose run-time is proportional to the popularity of the password (forcing a longer hashing time on more popular, lower entropy passwords). We refer to this as popularity-proportional hashing (PPH). We then introduce a frame-work for comparing different typo-tolerant authentication approaches. We show that PPH always offers a better time / security trade-off than the LHH and FRS constructions, and for certain distributions outperforms the Chatterjee et al. construction. Elsewhere, this latter construction offers the best trade-off. In aggregate our results suggest that the best known secure sketches are still inferior to simpler brute-force based approaches

A Novel Framework for Online Amnesic Trajectory Compression in Resource-constrained Environments

State-of-the-art trajectory compression methods usually involve high space-time complexity or yield unsatisfactory compression rates, leading to rapid exhaustion of memory, computation, storage and energy resources. Their ability is commonly limited when operating in a resource-constrained environment especially when the data volume (even when compressed) far exceeds the storage limit. Hence we propose a novel online framework for error-bounded trajectory compression and ageing called the Amnesic Bounded Quadrant System (ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that includes a normal version (BQS), Fast version (FBQS), and a Progressive version (PBQS). ABQS intelligently manages a given storage and compresses the trajectories with different error tolerances subject to their ages. In the experiments, we conduct comprehensive evaluations for the BQS algorithm family and the ABQS framework. Using empirical GPS traces from flying foxes and cars, and synthetic data from simulation, we demonstrate the effectiveness of the standalone BQS algorithms in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 45%). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41%. We then verify that with ABQS, given data volumes that are far greater than storage space, ABQS is able to achieve 15 to 400 times smaller errors than the baselines. We also show that the algorithm is robust to extreme trajectory shapes.Comment: arXiv admin note: substantial text overlap with arXiv:1412.032

Collaborative memory knowledge: A distributed reliabilist perspective

Collaborative remembering, in which two or more individuals cooperate to remember together, is an ordinary occurrence. Ordinary though it may be, it challenges traditional understandings of remembering as a cognitive process unfolding within a single subject, as well as traditional understandings of memory knowledge as a justified memory belief held within the mind of a single subject. Collaborative memory has come to be a major area of research in psychology, but it has so far not been investigated in epistemology. In this chapter, we attempt an initial exploration of the epistemological implications of collaborative memory research, taking as our starting point the “extended knowledge” debate which has resulted from the recent encounter between extracranialist theories of cognition and externalist theories of knowledge (Carter et al., 2014; Carter et al., forthcoming). Various forms of socially and technologically augmented memory have played important roles in the extended knowledge debate, but the debate has so far not taken collaborative memory, in particular, into account. We will argue that collaborative memory supports a novel externalist theory of knowledge: distributed reliabilism. Distributed reliabilism departs in two important respects from both traditional reliabilism (Goldman, 2012) and updated theories such as extended (Goldberg, 2010) and social reliabilism (Goldman, 2014). First, it acknowledges that belief-forming processes may extend extracranially to include processing performed both by other subjects and by technological artifacts. Second, it acknowledges that distributed sociotechnical systems themselves may be knowing subjects. Overall, then, the main aim of the chapter is to draw out the philosophical implications of psychological research on collaborative memory. But our argument will also suggest that it may be useful to broaden the standard conception of collaborative memory to include not only the sorts of direct interactions among subjects that have been the focus of psychological research so far but also a range of more indirect, technology-supported and -mediated interactions, and it thus has implications for psychology as well

Event-driven control in theory and practice : trade-offs in software and control performance

Feedback control algorithms are indispensable for the proper functioning of many industrial high-tech systems like copiers, wafer steppers and so on. Most research in digital feedback control considers periodic or time-driven control systems, where continuous-time signals are represented by their sampled values at a fixed frequency. In most applications, these digital control algorithms are implemented in a real-time embedded software environment. As a consequence of the time-driven nature of controllers, control engineers pose strong, non-negotiable requirements on the real-time implementations of their algorithms as the required control performance can be guaranteed in this manner. This might lead to non-optimal solutions if the design problem is considered from a broader multi-disciplinary system perspective. As an example, time-driven controllers perform control calculations all the time at a fixed rate, so also when nothing significant has happened in the process. This is clearly an unnecessary waste of resources like processor load and communication bus load and thus not optimal if these aspects are considered as well. To reduce the severe real-time constraints imposed by the control engineer and the accompanying disadvantages, this thesis proposes to drop the strict requirement of equidistant sampling. This enables the designers to make better balanced multidisciplinary trade-offs resulting in a better overall system performance and reduced cost price. By not requiring equidistant sampling, one could for instance vary the sample frequency over time and dynamically schedule the control algorithms in order to optimize over processor load. Another option is to perform a control update when new measurement data arrives. In this manner quantization effects and latencies are reduced considerably, which can reduce the required sensor resolution and thus the system cost price. As it is now an event (e.g. the arrival of a new measurement), rather than the elapse of time, that triggers the controller to perform an update, this type of feedback controllers is called event-driven control. In this thesis, we present two different event-driven control structures. The first one is sensor-based event-driven control in the sense that the control update is triggered by the arrival of new sensor data. In particular, this control structure is applied to accurately control a motor, based on an (extremely) low resolution encoder. The control design is based on transforming the system equations from the time domain to the angular position (spatial) domain. As controller updates are synchronous with respect to the angular position of the motor, we can apply variations on classical control theory to design and tune the controller. As a result of the transformation, the typical control measures that we obtain from analysis, are formulated in the spatial domain. For instance, the bandwidth of the controller is not expressed in Hertz (s¡1) anymore, but in rad¡1 and settling time is replaced by settling distance. For many high-tech systems these spatial measures directly relate to the real performance requirements. Moreover, disturbances are often more easily formulated in terms of angular position than in terms of time, which has clear advantages from a modeling point of view. To validate the theory, the controller is implemented on a high speed document printing system, to accurately control a motor based on an encoder resolution of only 1 pulse per revolution. By means of analysis, simulation and measurements we show that the control performance is similar to the initially proposed industrial controller that is based on a much higher encoder resolution. Moreover, we show that the proposed event-driven controller involves a significant lower processor load and hence outperforms the time-driven controller from a system perspective. The aim of the second type of event-driven controllers is to reduce the resource utilization for the controller tasks, such as processor load and communication bus load. The main idea is to only update the controller when it is necessary from a control performance point of view. For instance, we propose event-driven controllers that do not update the control value when the tracking/stabilization error is below a certain threshold. By choosing this threshold, a trade-off can be made between control performance and processor load. To get insight in this trade-off, theory is presented to analyze the control performance of these event-driven control loops in terms of ultimate bounds on the tracking/stabilization error. The theory is based on inferring properties (like robust positive invariance, ultimate boundedness and convergence indices) for the event-driven controlled system from discrete-time linear systems and piecewise linear systems. Next to the theoretical analysis, simulations and experiments are carried out on a printer paper path test-setup. It is shown that for the particular application the average processing time, needed to execute the controller tasks, was reduced by a factor 2 without significant degradation of the control performance in comparison to a timedriven implementation. Moreover, we developed a method to accurately predict the processor load for different processing platforms. This method is based on simulation models and micro measurements on the processing platform, such that the processor load can be estimated prior to implementing the controller on the platform. Next to these contributions in the field of event-driven control, a system engineering technique called "threads of reasoning" is extended and applied to the printer case study to achieve a focus on the right issues and trade-offs in a design. In summary, two types of event-driven controllers are theoretically analyzed and experimentally validated on a prototype document printing system. The results clearly indicate the potential benefits of event-driven control with respect to the overall system performance and in making trade-offs between control performance, software performance and cost price

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

Adaptive resource optimization for edge inference with goal-oriented communications

AbstractGoal-oriented communications represent an emerging paradigm for efficient and reliable learning at the wireless edge, where only the information relevant for the specific learning task is transmitted to perform inference and/or training. The aim of this paper is to introduce a novel system design and algorithmic framework to enable goal-oriented communications. Specifically, inspired by the information bottleneck principle and targeting an image classification task, we dynamically change the size of the data to be transmitted by exploiting banks of convolutional encoders at the device in order to extract meaningful and parsimonious data features in a totally adaptive and goal-oriented fashion. Exploiting knowledge of the system conditions, such as the channel state and the computation load, such features are dynamically transmitted to an edge server that takes the final decision, based on a proper convolutional classifier. Hinging on Lyapunov stochastic optimization, we devise a novel algorithmic framework that dynamically and jointly optimizes communication, computation, and the convolutional encoder classifier, in order to strike a desired trade-off between energy, latency, and accuracy of the edge learning task. Several simulation results illustrate the effectiveness of the proposed strategy for edge learning with goal-oriented communications