Mobile Online Gaming via Resource Sharing

Mobile gaming presents a number of main issues which remain open. These are concerned mainly with connectivity, computational capacities, memory and battery constraints. In this paper, we discuss the design of a fully distributed approach for the support of mobile Multiplayer Online Games (MOGs). In mobile environments, several features might be exploited to enable resource sharing among multiple devices / game consoles owned by different mobile users. We show the advantages of trading computing / networking facilities among mobile players. This operation mode opens a wide number of interesting sharing scenarios, thus promoting the deployment of novel mobile online games. In particular, once mobile nodes make their resource available for the community, it becomes possible to distribute the software modules that compose the game engine. This allows to distribute the workload for the game advancement management. We claim that resource sharing is in unison with the idea of ludic activity that is behind MOGs. Hence, such schemes can be profitably employed in these contexts.Comment: Proceedings of 3nd ICST/CREATE-NET Workshop on DIstributed SImulation and Online gaming (DISIO 2012). In conjunction with SIMUTools 2012. Desenzano, Italy, March 2012. ISBN: 978-1-936968-47-

New Results on Online Resource Minimization

We consider the online resource minimization problem in which jobs with hard deadlines arrive online over time at their release dates. The task is to determine a feasible schedule on a minimum number of machines. We rigorously study this problem and derive various algorithms with small constant competitive ratios for interesting restricted problem variants. As the most important special case, we consider scheduling jobs with agreeable deadlines. We provide the first constant ratio competitive algorithm for the non-preemptive setting, which is of particular interest with regard to the known strong lower bound of n for the general problem. For the preemptive setting, we show that the natural algorithm LLF achieves a constant ratio for agreeable jobs, while for general jobs it has a lower bound of Omega(n^(1/3)). We also give an O(log n)-competitive algorithm for the general preemptive problem, which improves upon the known O(p_max/p_min)-competitive algorithm. Our algorithm maintains a dynamic partition of the job set into loose and tight jobs and schedules each (temporal) subset individually on separate sets of machines. The key is a characterization of how the decrease in the relative laxity of jobs influences the optimum number of machines. To achieve this we derive a compact expression of the optimum value, which might be of independent interest. We complement the general algorithmic result by showing lower bounds that rule out that other known algorithms may yield a similar performance guarantee

SOCR: Statistics Online Computational Resource

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student's intuition and enhance their learning.

EnviroNET: An online environmental interactions resource

EnviroNET is a centralized depository for technical information on environmentally induced interactions likely to be encountered by spacecraft in both low-altitude and high-altitude orbits. It provides a user-friendly, menu-driven format on networks that are connected globally and is available 24 hours a day - every day. The service pools space data collected over the years by NASA, USAF, other government research facilities, industry, universities, and the European Space Agency. This information contains text, tables and over one hundred high resolution figures and graphs based on empirical data. These graphics can be accessed while still in the chapters, making it easy to flip from text to graphics and back. Interactive graphics programs are also available on space debris, the neutral atmosphere, magnetic field, and ionosphere. EnviroNET can help designers meet tough environmental flight criteria before committing to flight hardware built for experiments, instrumentation, or payloads

Library resources, student success and the distance-learning university

Purpose - Research at the Open University Library Services has been investigating the relationshipbetween access to online library resources and student success to help to understand whether there is asimilar relationship at a distance-learning university to that found in other institutions. Design/methodology/approach - A small library data project was established to investigate this area.The study analysed online library resource data from access logs from the EZproxy and OpenAthens systems. A data set of 1.7 million online resource accesses was combined with student success data for around 90,000 undergraduate students and a series of analyses undertaken.Findings The study found a pattern where students who are more successful are accessing more library resources. A chi-square test indicated a statistically significant association between library resource accesses and module result, while an ANOVA test suggests a medium sized effect. The study also found that 152 (76%) of 199 modules had a small, medium or large positive correlation between student success, measured by the overall assessment score, and online library resource accesses.Originality/value - This study builds on evidence that there is a relationship between library use and student success by showing that this relationship extends to the setting of a non-traditional, innovative library service supporting part-time distance learners

Online support groups: an overlooked resource for patients

Online support groups: history, research, and source

Optimal Posted Prices for Online Cloud Resource Allocation

We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool

Online Resource Inference in Network Utility Maximization Problems

The amount of transmitted data in computer networks is expected to grow considerably in the future, putting more and more pressure on the network infrastructures. In order to guarantee a good service, it then becomes fundamental to use the network resources efficiently. Network Utility Maximization (NUM) provides a framework to optimize the rate allocation when network resources are limited. Unfortunately, in the scenario where the amount of available resources is not known a priori, classical NUM solving methods do not offer a viable solution. To overcome this limitation we design an overlay rate allocation scheme that attempts to infer the actual amount of available network resources while coordinating the users rate allocation. Due to the general and complex model assumed for the congestion measurements, a passive learning of the available resources would not lead to satisfying performance. The coordination scheme must then perform active learning in order to speed up the resources estimation and quickly increase the system performance. By adopting an optimal learning formulation we are able to balance the tradeoff between an accurate estimation, and an effective resources exploitation in order to maximize the long term quality of the service delivered to the users