CPU Torrent -- CPU Cycle Offloading to Reduce User Wait Time and Provider Resource Requirements

Developers of novel scientific computing systems are often eager to make their algorithms and databases available for community use, but their own computational resources may be inadequate to fulfill external user demand -- yet the system's footprint is far too large for prospective user organizations to download and run locally. Some heavyweight systems have become part of designated "centers" providing remote access to supercomputers and/or clusters supported by substantial government funding; others use virtual supercomputers dispersed across grids formed by massive numbers of volunteer Internet-connected computers. But public funds are limited and not all systems are amenable to huge-scale divisibility into independent computation units. We have identified a class of scientific computing systems where "utility" sub-jobs can be offloaded to any of several alternative providers thereby freeing up local cycles for the main proprietary jobs, implemented a proof-of-concept framework enabling such deployments, and analyzed its expected throughput and response-time impact on a real-world bioinformatics system (Columbia's PredictProtein) whose present users endure long wait queues

The Tradeoffs of Societal Computing

As Social Computing has increasingly captivated the general public, it has become a popular research area for computer scientists. Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. We highlight some of the relevant research topics and open problems that we foresee in Societal Computing where software engineering research approaches and techniques seem particularly likely to be fruitful. We feel that these topics, and Societal Computing in general, need to gain prominence as they will provide useful avenues of research leading to increasing benefits for society as a whole

Towards Using Cached Data Mining for Large Scale Recommender Systems

Recommender systems are becoming increasingly popular. As these systems become commonplace and the number of users increases, it will become important for these systems to be able to cope with a large and diverse set of users whose recommendation needs may be very different from each other. In particular, large scale recommender systems will need to ensure that users' requests for recommendations can be answered with low response times and high throughput. In this paper, we explore how to use caches and cached data mining to improve the performance of recommender systems by improving throughput and reducing response time for providing recommendations. We describe the structure of our cache, which can be viewed as a prefetch cache that prefetches all types of supported recommendations, and how it is used in our recommender system. We also describe the results of our simulation experiments to measure the efficacy of our cache

Us and Them - A Study of Privacy Requirements Across North America, Asia, and Europe

Data privacy when using online systems like Facebook and Amazon has become an increasingly popular topic in the last few years. However, only a little is known about how users and developers perceive privacy and which concrete measures would mitigate privacy concerns. To investigate privacy requirements, we conducted an online survey with closed and open questions and collected 408 valid responses. Our results show that users often reduce privacy to security, with data sharing and data breaches being their biggest concerns. Users are more concerned about the content of their documents and personal data such as location than their interaction data. Unlike users, developers clearly prefer technical measures like data anonymization and think that privacy laws and policies are less effective. We also observed interesting differences between people from different geographies. For example, people from Europe are more concerned about data breaches than people from North America. People from Asia/Pacific and Europe believe that content and metadata are more critical for privacy than people from North America. Our results contribute to developing a user-driven privacy framework that is based on empirical evidence in addition to the legal, technical, and commercial perspectives

End-User Regression Testing for Privacy

Privacy in social computing systems has become a major concern. End-users of such systems find it increasingly hard to understand complex privacy settings. As software evolves over time, this might introduce bugs that breach users' privacy. Further, there might be system-wide policy changes that could change users' settings to be more or less private than before. We present a novel technique that can be used by end-users for detecting changes in privacy, i.e., regression testing for privacy. Using a social approach for detecting privacy bugs, we present two prototype tools. Our evaluation shows the feasibility and utility of our approach for detecting privacy bugs. We highlight two interesting case studies on the bugs that were discovered using our tools. To the best of our knowledge, this is the first technique that leverages regression testing for detecting privacy bugs from an end-user perspective

Money for Nothing and Privacy for Free?

Privacy in the context of ubiquitous social computing systems has become a major concern for the society at large. As the number of online social computing systems that collect user data grows, this privacy threat is further exacerbated. There has been some work (both, recent and older) on addressing these privacy concerns. These approaches typically require extra computational resources, which might be beneficial where privacy is concerned, but when dealing with Green Computing and sustainability, this is not a great option. Spending more computation time results in spending more energy and more resources that make the software system less sustainable. Ideally, what we would like are techniques for designing software systems that address these privacy concerns but which are also sustainable - systems where privacy could be achieved "for free," i.e., without having to spend extra computational effort. In this paper, we describe how privacy can be achieved for free - an accidental and beneficial side effect of doing some existing computation - and what types of privacy threats it can mitigate. More precisely, we describe a "Privacy for Free" design pattern and show its feasibility, sustainability, and utility in building complex social computing systems

A Large-Scale, Longitudinal Study of Player Achievements in World of Warcraft

We present a survey of usage of the popular Massively Multiplayer Online Role Playing Game, World of Warcraft. By mining publicly available data, we collected a dataset consisting of the player history for approximately six million characters, with partial data for another six million characters. This paper focuses on player achievement data in particular, exposing trends in play from this highly successful game. From this data, we present several findings on players' play styles. We correlate achievements with motivations based upon a previously-defined motivation model, and then classify players based on the categories of achievements that they pursued. Experiments show players who fall within each of these buckets can play differently, and that as players progress through game content, their play style evolves as well

HALO (Highly Addictive, sociaLly Optimized) Software Engineering

In recent years, computer games have become increasingly social and collaborative in nature. Massively multiplayer online games, in which a large number of players collaborate with each other to achieve common goals in the game, have become extremely pervasive. By working together towards a common goal, players become more engrossed in the game. In everyday work environments, this sort of engagement would be beneficial, and is often sought out. We propose an approach to software engineering called HALO that builds upon the properties found in popular games, by turning work into a game environment. Our proposed approach can be viewed as a model for a family of prospective games that would support the software development process. Utilizing operant conditioning and flow theory, we create an immersive software development environment conducive to increased productivity. We describe the mechanics of HALO and how it could fit into typical software engineering processes

Effectiveness of Teaching Metamorphic Testing, Part II

We study the ability of students in a senior/graduate software engineering course to understand and apply metamorphic testing, a relatively recently invented advance in software testing research that complements conventional approaches such as equivalence partitioning and boundary analysis. We previously reported our investigation of the fall 2011 offering of the Columbia University course COMS W4156 Advanced Software Engineering, and here report on the fall 2012 offering and contrast it to the previous year. Our main findings are: 1) Although the students in the second offering did not do very well on the newly added individual assignment specifically focused on metamorphic testing, thereafter they were better able to find metamorphic properties for their team projects than the students from the previous year who did not have that preliminary homework and, perhaps most significantly, did not have the solution set for that homework. 2) Students in the second offering did reasonably well using the relatively novel metamorphic testing technique vs. traditional black box testing techniques in their projects (such comparison data is not available for the first offering). 3) Finally, in both semesters, the majority of the student teams were able to apply metamorphic testing to their team projects after only minimal instruction, which would imply that metamorphic testing is a viable strategy for student testers

weHelp: A Reference Architecture for Social Recommender Systems

Recommender systems have become increasingly popular. Most of the research on recommender systems has focused on recommendation algorithms. There has been relatively little research, however, in the area of generalized system architectures for recommendation systems. In this paper, we introduce weHelp: a reference architecture for social recommender systems — systems where recommendations are derived automatically from the aggregate of logged activities conducted by the system's users. Our architecture is designed to be application and domain agnostic. We feel that a good reference architecture will make designing a recommendation system easier; in particular, weHelp aims to provide a practical design template to help developers design their own well-modularized systems