Efficient and scalable search on scale-free P2P networks

Unstructured peer-to-peer (P2P) systems (e.g. Gnutella) are characterized by uneven distributions of node connectivity and file sharing. The existence of “hub” nodes that have a large number of connections and “generous” nodes that share many files significantly influences performance of information search over P2P file-sharing networks. In this paper, we present a novel Scalable Peer-to-Peer Search (SP2PS) method with low maintenance overhead for resource discovery in scale-free P2P networks. Different from existing search methods which employ one heuristic to direct searches, SP2PS achieves better performance by considering both of the number of shared files and the connectivity of each neighbouring node. SP2PS enables peer nodes to forward queries to the neighbours that are more likely to have the requested files and also can help in finding the requested files in the future hops. The proposed method has been simulated in different power-law networks with different forwarding degrees and distances. From our analytic and simulation results, SP2PS achieves better performance when compared to other related methods.

An implementation of parallel file distribution in an agent hierarchy

PC grid is a cost-effective grid-computing platform that attracts users by allocating to their massively parallel applications as many desktop computers as requested. However, a challenge is how to distribute necessary files to remote computing nodes that may be unconnected to the same network file system, equipped with insufficient disk space to keep entire files, and even powered off asynchronously. Targeting PC grid, the AgentTeamwork grid-computing middleware deploys a hierarchy of mobile agents to remote desktops so as to launch, monitor, check-point, and resume a parallel and distributed computing job. To achieve high-speed file distribution, AgentTeamwork takes advantage of its agent hierarchy. The system partitions files into stripes at the tree root if they are random-access files, duplicates them at each tree level if they are shared among all remote nodes, fragments them into smaller messages if they are too large to relay to a lower tree level, aggregates such messages in a larger fragment if they are in transit to the same subtree, and returns output files to the user along multi-paths established within the tree. To achieve fault-tolerant file delivery, each agent periodically takes a snapshot of in-transit and on-memory file messages with its user job, and thus resumes them from the latest snapshot when they crash accidentally. This paper presents an implementation and its competitive performance of AgentTeamwork’s file-distribution algorithm including file partitioning, transfer, check-pointing, and consistency maintenance.

Deadline-constrained media uploading systems

This paper presents the design, implementation, and performance evaluation of a novel file uploading system. The system automatically uploads multimedia files to a centralized server given a client machine’s hard deadline—the time when a client machine will exhaust its available storage space due to on-going recording of media files. If existing files have not been uploaded and removed from the client machine’s hard disk by the deadlines, existing files may be overwritten or new files may not get created. Our uploading system was designed to provide a practical solution for emerging business needs. For instance, our system can be used in medical practice to gather videos generated from medical devices located in various procedure rooms for post-procedure analysis, and in law enforcement to collect video recordings from police cars during routine patrolling. Here we investigate two upload scheduling algorithms that determine which client machine should upload its file(s) first. We introduce two emergency control algorithms to handle situations when a client machine is about to exhaust its hard disk space. We evaluate the proposed algorithms via simulations and analysis. Our performance studies show that the upload scheduling algorithms and the emergency control algorithms have a significant impact on overall system performance.

A reinforcement learning framework for online data migration in hierarchical storage systems

Multi-tier storage systems are becoming more and more widespread in the industry. They have more tunable parameters and built-in policies than traditional storage systems, and an adequate configuration of these parameters and policies is crucial for achieving high performance. A very important performance indicator for such systems is the response time of the file I/O requests. The response time can be minimized if the most frequently accessed (“hot”) files are located in the fastest storage tiers. Unfortunately, it is impossible to know a priori which files are going to be hot, especially because the file access patterns change over time. This paper presents a policy-based framework for dynamically deciding which files need to be upgraded and which files need to be downgraded based on their recent access pattern and on the system’s current state. The paper also presents a reinforcement learning (RL) algorithm for automatically tuning the file migration policies in order to minimize the average request response time. A multi-tier storage system simulator was used to evaluate the migration policies tuned by RL, and such policies were shown to achieve a significant performance improvement over the best hand-crafted policies found for this domain.

Insight into redundancy schemes in DHTs

In order to provide high data availability in peer-to-peer (P2P) DHTs, proper data redundancy schemes are required. This paper compares two popular schemes: replication and erasure coding. Unlike previous comparison, we take user download behavior into account. Furthermore, we propose a hybrid redundancy scheme, which shares user downloaded files for subsequent accesses and utilizes erasure coding to adjust file availability. Comparison experiments of three schemes show that replication saves more bandwidth than erasure coding, although it requires more storage space, when average node availability is higher than 47%; moreover, our hybrid scheme saves more maintenance bandwidth with acceptable redundancy factor.

Fault tolerant file models for parallel file systems: introducing distribution patterns for every file

Parallelism in file systems is obtained by using several independent server nodes supporting one or more secondary storage devices. This approach increases the performance and scalability of the system, but a fault in one single node can stop the whole system. To avoid this problem, data must be stored using some kind of redundant technique, so any data stored in a faulty element can be recovered. Fault tolerance can be provided in I/O systems by using replication or RAID based schemes. However, most of the current systems apply the same technique for all files in the system. This paper describes the fault tolerance support provided by Expand, a parallel file system based on standard servers. This support can be applied to other parallel file systems with many benefices: fault tolerance at file level, flexible definition of fault tolerance scheme to be used, possibility to change the fault tolerant support used for a file, etc.

The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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Interacting with mobile services: an evaluation of camera-phones and visual tags

We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive, although they had several important reservations about camera-phone technology more generally. Data from our pointing-device task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings, design lessons for camera-phone and visual-tag applications are presented.

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Methods for quantitative usability requirements: a case study on the development of the user interface of a mobile phone

Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed.

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An application of a context-aware file system

Ubiquitous computing environments stretch the requirements of traditional infrastructures used to facilitate the development of applications. Activities are often supported by collections of applications, some of which are automatically launched with little or no human intervention. This task-driven environment challenges existing application construction and data management techniques. In this paper, we describe a file system that organises application data based on contextual information, imports user data based on its physical presence, and supports format conversions to accommodate device context. We describe several applications that we have developed within our ubiquitous computing infrastructure and show how they leverage the novel features of our file system to simplify their complexity.

Connecting the family with awareness systems

Awareness systems have attracted significant research interest for their potential to support interpersonal relationships. Investigations of awareness systems for the domestic environment have suggested that such systems can help individuals stay in touch with dear friends or family and provide affective benefits to their users. Our research provides empirical evidence to refine and substantiate such suggestions. We report our experience with designing and evaluating the ASTRA awareness system, for connecting households and mobile family members. We introduce the concept of connectedness and its measurement through the Affective Benefits and Costs of communication questionnaire (ABC-Q). We inform results that testify the benefits of sharing experiences at the moment they happen without interrupting potential receivers. Finally, we document the role that lightweight, picture-based communication can play in the range of communication media available.

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Understanding and capturing people’s privacy policies in a mobile social networking application

A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PeopleFinder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies.

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ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

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Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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On index load balancing in scalable P2P media distribution

Peer-Peer (P2P) technologies have recently been in the limelight for their disruptive power in particular they have emerged as a powerful multimedia content distribution mechanism. However, the widespread deployment of P2P networks are hindered by several issues, especially the ones that influence end-user satisfaction, including reliability. In this paper, we propose a solution for an efficient and user-oriented keyword lookup service on P2P networks. The proposed mechanism has been designed to achieve reliability via index load balancing and address the scalability issues of extremely popular keywords in the index. The system performance have been analytically derived as well implemented using the OpenDHT framework on PlanetLab.

Supporting ordering and consistency in a distributed Event Heap for Ubiquitous Computing

The Stanford Event Heap has been shown to provide appropriate support for constructing interactive workspace applications. Given this success it is natural to consider the Event Heap as a platform to support other classes of Ubiquitous Computing applications. In this paper we argue that the distributed, spontaneous nature of these applications places additional demands on the Event Heap that require extensions to both the engineering and API. Suitable extensions are described and their use to support a typical Ubicomp application is discussed.

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Ethical regulations on robotics in Europe

There are only a few ethical regulations that deal explicitly with robots, in contrast to a vast number of regulations, which may be applied. We will focus on ethical issues with regard to “responsibility and autonomous robots”, “machines as a replacement for humans”, and “tele-presence”. Furthermore we will examine examples from special fields of application (medicine and healthcare, armed forces, and entertainment). We do not claim to present a complete list of ethical issue nor of regulations in the field of robotics, but we will demonstrate that there are legal challenges with regard to these issues.

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Empirical analysis on the correlation between GCC compiler warnings and revision numbers of source files in five industrial software projects

This article discusses whether using warnings generated by the GNU C++ compiler can be used effectively to identify source code files that are likely to be error prone. We analyze five industrial projects written in C++ and belonging to the telecommunication domain. We find a significant positive correlation between the number of compiler warnings and the number of source files changes. We use such correlation to conclude that compiler warnings may be used as an indicator for the presence of software defects in source code. The result of this research is useful for finding defect-prone modules in newer projects, which lack change history.

Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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