We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

The all-pairs-similarity-search (or similarity join) problem has been extensively studied for text and a handful of other datatypes. However, surprisingly little progress has been made on similarity joins for time series subsequences. The lack of progress probably stems from the daunting nature of the problem. For even modest sized datasets the obvious nested-loop algorithm can take months, and the typical speed-up techniques in this domain (i.e., indexing, lower-bounding, triangular-inequality pruning and early abandoning) at best produce one or two orders of magnitude speedup. In this work we introduce a novel scalable algorithm for time series subsequence all-pairs-similarity-search. For exceptionally large datasets, the algorithm can be trivially cast as an anytime algorithm and produce high-quality approximate solutions in reasonable time. The exact similarity join algorithm computes the answer to the time series motif and time series discord problem as a side-effect, and our algorithm incidentally provides the fastest known algorithm for both these extensively-studied problems. We demonstrate the utility of our ideas for two time series data mining problems, including motif discovery and novelty discovery.

Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets

Cloud Computing has taken commercial computing by storm. However, adoption of cloud computing platforms and services by the scientific community is in its infancy as the performance and monetary cost-benefits for scientific applications are not perfectly clear. This is especially true for desktop grids (aka volunteer computing) applications. We compare and contrast the performance and monetary cost-benefits of clouds for desktop grid applications, ranging in computational size and storage. We address the following questions: (i) What are the performance tradeoffs in using one platform over the other? (ii) What are the specific resource requirements and monetary costs of creating and deploying applications on each platform? (iii) In light of those monetary and performance cost-benefits, how do these platforms compare? (iv) Can cloud computing platforms be used in combination with desktop grids to improve cost-effectiveness even further? We examine those questions using performance measurements and monetary expenses of real desktop grids and the Amazon elastic compute cloud.

/pdf/cost-benefit-analysis-of-cloud-computing-versus-desktop-4afd4za5rv.pdf

Cost-benefit analysis of Cloud Computing versus desktop grids

https://hal.inria.fr/hal-01017319/document

Versatile, Scalable, and Accurate Simulation of Distributed Applications and Platforms

The aim of docking is to accurately predict the structure of a ligand within the constraints of a receptor binding site and to correctly estimate the strength of binding. We discuss, in detail, methodological developments that occurred in the docking field in 2010 and 2011, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons. Several new, or at least newly invigorated, advances occurred in areas such as nonlinear scoring functions, using machine-learning approaches. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design. Where appropriate, we refer readers to exemplar case studies.

Latest developments in molecular docking: 2010-2011 in review.

We consider the problem of joining two long time series based on their most correlated segments. Two time series can be joined at any locations and for arbitrary length. Such join locations and length provide useful knowledge about the synchrony of the two time series and have applications in many domains including environmental monitoring, patient monitoring and power monitoring. However, join on correlation is a computationally expensive task, specially when the time series are large. The naive algorithm requires O (n4) computation where n is the length of the time series. We propose an algorithm, named Jocor, that uses two algorithmic techniques to tackle the complexity. First, the algorithm reuses the computation by caching sufficient statistics and second, the algorithm prunes unnecessary correlation computation by admissible heuristics. The algorithm runs orders of magnitude faster than the naive algorithm and enables us to join long time series as well as many small time series. We propose a variant of Jocor for fast approximation and an extension to a GPU-based parallel method to bring down the running-time to interactive level for analytics applications. We show three independent uses of time series join on correlation which are made possible by our algorithm.

/pdf/time-series-join-on-subsequence-correlation-2ibr03b6ve.pdf

Time Series Join on Subsequence Correlation

Middleware systems for volunteer computing convert a set of computers that is large and diverse (in terms of hardware, software, availability, reliability, and trustworthiness) into a unified computing resource. This involves a number of scheduling policies and parameters, which have a large impact on the throughput and other performance metrics. How can we study and refine these policies? Experimentation in the context of a working project is problematic, and it is difficult to accurately model complex middleware in a conventional simulator. Instead, we use an approach in which the policies being studied are “emulated”, using parts of the actual middleware. In this paper we describe EmBOINC, an emulator based on the BOINC middleware system. EmBOINC simulates a population of volunteered clients (including heterogeneity, churn, availability, and reliability) and emulates the BOINC server components. After describing the design of EmBOINC and its validation, we present three case studies in which the impact of different scheduling policies are quantified in terms of throughput, latency, and starvation metrics.

/pdf/performance-prediction-and-analysis-of-boinc-projects-an-4pj55gs61k.pdf

Performance Prediction and Analysis of BOINC Projects: An Empirical Study with EmBOINC

Several scientific projects use BOINC (Berkeley Open Infrastructure for Network Computing) to perform largescale simulations using volunteers? computers (workers) across the Internet. In general, the scheduling of tasks in BOINC uses a First-Come-First-Serve policy and no attention is paid to workers? past performance, such as whether or not they have tended to perform tasks promptly and correctly. In this paper we use SimBA, a discrete-event Simulator of BOINC Applications, to study new threshold-based scheduling strategies for BOINC projects that use availability and reliability metrics to classify workers and distribute tasks according to this classification. We show that if availability and reliability thresholds are selected properly, then the workers? throughput of valid results increases significantly in BOINC projects.

/pdf/the-effectiveness-of-threshold-based-scheduling-policies-in-54b2yd3ecv.pdf

The Effectiveness of Threshold-Based Scheduling Policies in BOINC Projects

SimBA (Simulator of BOINC Applications) is a discrete event simulator that models the main functions of BOINC, which is a well-known framework used in Volunteer Computing (VC) projects. SimBA simulates the generation and distribution of tasks that are executed in a highly volatile, heterogeneous, and distributed environment as well as the collection and validation of completed tasks. To understand the strengths and weaknesses of BOINC under distinct scenarios, project designers want to study and quantify project performance without negatively affecting people in the VC community. Although this is not possible on production systems, it is possible using SimBA, which is capable of testing a wide range of hypotheses in a short period of time. Our experience to date indicates that SimBA is a reliable tool for performance prediction of VC projects. Preliminary results show that SimBA's predictions of Predictor@ Home performance are within approximately 5% of the performance reported by this BOINC project.

SimBA: A Discrete Event Simulator for Performance Prediction of Volunteer Computing Projects

Volunteer Computing (VC) projects harness the power of computers owned by volunteers across the Internet to perform hundreds of thousands of independent jobs. In VC projects, the path leading from the generation of jobs to the validation of the job results is characterized by delays hidden in the job lifespan, i.e., distribution delay,in-progress delay, and validation delay. These delays are difficult to estimate because of the dynamic behavior and heterogeneity of VC resources. A wrong estimation of these delays can cause the loss of project throughput and job latency in VC projects. In this paper, we evaluate the accuracy of several probabilistic methods to model the upper time bounds of these delays. We show how our selected models predict up-and-down trends in traces from existing VC projects. The use of our models provides valuable insights on selecting project deadlines and taking scheduling decisions. By accurately predicting job lifespan delays, our models lead to more efficient resource use, higher project throughput, and lower job latency in VC projects.

Trilce Estrada

Papers

Time Series Join on Subsequence Correlation

Performance Prediction and Analysis of BOINC Projects: An Empirical Study with EmBOINC

The Effectiveness of Threshold-Based Scheduling Policies in BOINC Projects

SimBA: A Discrete Event Simulator for Performance Prediction of Volunteer Computing Projects

Modeling Job Lifespan Delays in Volunteer Computing Projects