Dynamic Prefetching of Data Tiles for Interactive Visualization

In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a client-server architecture, where the user interacts with a lightweight client-side interface to browse datasets, and the data to be browsed is retrieved from a DBMS running on a back-end server. We assume a detail-on-demand browsing paradigm, and optimize the back-end support for this paradigm by inserting a separate middleware layer in front of the DBMS. To improve response times, the middleware layer fetches data ahead of the user as she explores a dataset. We consider two different mechanisms for prefetching: (a) learning what to fetch from the user's recent movements, and (b) using data characteristics (e.g., histograms) to find data similar to what the user has viewed in the past. We incorporate these mechanisms into a single prediction engine that adjusts its prediction strategies over time, based on changes in the user's behavior. We evaluated our prediction engine with a user study, and found that our dynamic prefetching strategy provides: (1) significant improvements in overall latency when compared with non-prefetching systems (430% improvement); and (2) substantial improvements in both prediction accuracy (25% improvement) and latency (88% improvement) relative to existing prefetching techniques

Generalized techniques for using system execution traces to support software performance analysis

This dissertation proposes generalized techniques to support software performance analysis using system execution traces in the absence of software development artifacts such as source code. The proposed techniques do not require modifications to the source code, or to the software binaries, for the purpose of software analysis (non-intrusive). The proposed techniques are also not tightly coupled to the architecture specific details of the system being analyzed. This dissertation extends the current techniques of using system execution traces to evaluate software performance properties, such as response times, service times. The dissertation also proposes a novel technique to auto-construct a dataflow model from the system execution trace, which will be useful in evaluating software performance properties. Finally, it showcases how we can use execution traces in a novel technique to detect Excessive Dynamic Memory Allocations software performance anti-pattern. This is the first attempt, according to the author\u27s best knowledge, of a technique to detect automatically the excessive dynamic memory allocations anti-pattern. The contributions from this dissertation will ease the laborious process of software performance analysis and provide a foundation for helping software developers quickly locate the causes for negative performance results via execution traces

Network Aware Compute and Memory Allocation in Optically Composable Data Centres with Deep Reinforcement Learning and Graph Neural Networks

Resource-disaggregated data centre architectures promise a means of pooling resources remotely within data centres, allowing for both more flexibility and resource efficiency underlying the increasingly important infrastructure-as-a-service business. This can be accomplished by means of using an optically circuit switched backbone in the data centre network (DCN); providing the required bandwidth and latency guarantees to ensure reliable performance when applications are run across non-local resource pools. However, resource allocation in this scenario requires both server-level \emph{and} network-level resource to be co-allocated to requests. The online nature and underlying combinatorial complexity of this problem, alongside the typical scale of DCN topologies, makes exact solutions impossible and heuristic based solutions sub-optimal or non-intuitive to design. We demonstrate that \emph{deep reinforcement learning}, where the policy is modelled by a \emph{graph neural network} can be used to learn effective \emph{network-aware} and \emph{topologically-scalable} allocation policies end-to-end. Compared to state-of-the-art heuristics for network-aware resource allocation, the method achieves up to $20\%$ higher acceptance ratio; can achieve the same acceptance ratio as the best performing heuristic with $3\times$ less networking resources available and can maintain all-around performance when directly applied (with no further training) to DCN topologies with $10^2\times$ more servers than the topologies seen during training.Comment: 10 pages + 1 appendix page, 8 figure

An Experimental Investigation of Colonel Blotto Games

This article examines behavior in the two-player, constant-sum Colonel Blotto game with asymmetric resources in which players maximize the expected number of battlefields won. The experimental results support all major theoretical predictions. In the auction treatment, where winning a battlefield is deterministic, disadvantaged players use a “guerilla warfare” strategy which stochastically allocates zero resources to a subset of battlefields. Advantaged players employ a “stochastic complete coverage” strategy, allocating random, but positive, resource levels across the battlefields. In the lottery treatment, where winning a battlefield is probabilistic, both players divide their resources equally across all battlefields.Colonel Blotto, conflict resolution, contest theory, multi-dimensional resource allocation, rent-seeking, experiments

Lessons learned from evaluating eight password nudges in the wild

Background. The tension between security and convenience, when creating passwords, is well established. It is a tension that often leads users to create poor passwords. For security designers, three mitigation strategies exist: issuing passwords, mandating minimum strength levels or encouraging better passwords. The first strategy prompts recording, the second reuse, but the third merits further investigation. It seemed promising to explore whether users could be subtly nudged towards stronger passwords.Aim. The aim of the study was to investigate the influence of visual nudges on self-chosen password length and/or strength.Method. A university application, enabling students to check course dates and review grades, was used to support two consecutive empirical studies over the course of two academic years. In total, 497 and 776 participants, respectively, were randomly assigned either to a control or an experimental group. Whereas the control group received no intervention, the experimental groups were presented with different visual nudges on the registration page of the web application whenever passwords were created. The experimental groups’ password strengths and lengths were then compared that of the control group.Results. No impact of the visual nudges could be detected, neither in terms of password strength nor length. The ordinal score metric used to calculate password strength led to a decrease in variance and test power, so that the inability to detect an effect size does not definitively indicate that such an effect does not exist.Conclusion. We cannot conclude that the nudges had no effect on password strength. It might well be that an actual effect was not detected due to the experimental design choices. Another possible explanation for our result is that password choice is influenced by the user’s task, cognitive budget, goals and pre-existing routines. A simple visual nudge might not have the power to overcome these forces. Our lessons learned therefore recommend the use of a richer password strength quantification measure, and the acknowledgement of the user’s context, in future studies

Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

Bottom-Up vs. Top-Down Policies towards the Commercialization of University Intellectual Property

What national policies are most efficient in promoting the commercialization of university-generated knowledge? We address this question by characterizing and evaluating the policy pursued in Sweden and the US, two countries that put a great deal of resources into university R&D, but follow very different models for commercialization. Despite a leading academic record, there is an impression of laggard rates of commercialization of academic research results in Sweden. Although there exist no micro data to evaluate this impression, we argue that it is likely to be true in part due to the top-down nature of Swedish policies aimed at commercializing these innovations as well as an academic environment that discourages academics from actively participating in the commercialization of their ideas. This sits in stark contrast to a US institutional setting characterized by competition between universities for research funds and research personnel, which in turn has led to significant academic freedoms to interact with industry, including significant involvement in new firms.Academic entrepreneurship; Innovation; Intellectual property; R&D; Spin-off firms; Technology transfer; University-industry relations; Universities and business formation

Learning with bounded memory.

The paper studies infinite repetition of finite strategic form games. Players use a learning behavior and face bounds on their cognitive capacities. We show that for any given beliefprobability over the set of possible outcomes where players have no experience. games can be payoff classified and there always exists a stationary state in the space of action profiles. In particular, if the belief-probability assumes all possible outcomes without experience to be equally likely, in one class of Prisoners' Dilemmas where the average defecting payoff is higher than the cooperative payoff and the average cooperative payoff is lower than the defecting payoff, play converges in the long run to the static Nash equilibrium while in the other class of Prisoners' Dilemmas where the reserve holds, play converges to cooperation. Results are applied to a large class of 2 x 2 games.Cognitive complexity; Bounded logistic quantal response learning; Long run outcomes;