ULabGrid, an infrastructure to develop distant laboratories for undergrad students over a Grid

Nowadays, there is a big discussion about two different topics: how distance learning and the old fashioned learning can be improved using the new technologies. In both cases, there are many collaborative tools based on the web infrastructure such as e-mail, web discussing groups, virtual campuses or audio and video conferences, that basically give a way of exchanging information among the different groups involved in learning tasks, but very few of them have been thought to help or to develop laboratory classes (labs). In this paper we describe a GRID infrastructure (ULabGrid) that supports distant laboratories for undergrad students.Peer Reviewe

Deadline-Driven Auctions for NPC Host Allocation on P2P MMOGs.

We present the design, implementation and evaluation of Deadline-Driven Auctions (DDAs), a novel task-mapping infrastructure for heterogeneous distributed environments. DDA is primarily designed for hosting Non-Player Characters (NPCs) in P2P Massively Multiplayer Online Games (MMOGs). Experimental and analytical results demonstrate that DDA provides four significant advantages. It is self-organising: the infrastructure is automatically managed. It efficiently allocates computing resources for large numbers (1000s) of real-time NPC tasks. It supports gaming interactivity by minimising communication latency between NPC hosts. Finally, it supports flexible matchmaking policies, and a friendly incentive policy establishes a cooperative economic model to motivate participants to contribute resources

A study of distributed clustering of vector time series on the grid by task farming

Traditional data mining methods were limited by availability of computing resources like network bandwidth, storage space and processing power. These algorithms were developed to work around this problem by looking at a small cross-section of the whole data available. However since a major chunk of the data is kept out, the predictions were generally inaccurate and missed out on significant features that was part of the data. Today with resources growing at almost the same pace as data, it is possible to rethink mining algorithms to work on distributed resources and essentially distributed data. Distributed data mining thus holds great promise. Using grid technologies, data mining can be extended to areas which were not previously looked at because of the volume of data being generated, like climate modeling, web usage, etc. An important characteristic of data today is that it is highly decentralized and mostly redundant. Data mining algorithms which can make efficient use of distributed data has to be thought of. Though it is possible to bring all the data together and run traditional algorithms, this has a high overhead, in terms of bandwidth usage for transmission, preprocessing steps which have to be to handle every format the received data. By processing the data locally, the preprocessing stage can be made less bulky and also the traditional data mining techniques would be able to work on the data efficiently. The focus of this project is to use an existing data mining technique, fuzzy c-means clustering to work on distributed data in a simulated grid environment and to review the performance of this approach viz., the traditional approach

Resource management through multilateral matchmaking

Federated distributed systems present new challenges to resource management, which cannot be met by conventional systems that employ relatively static resource models and centralized allocators. We previously argued that Matchmaking provides an elegant and robust resource management solution for these highly dynamic environments [5]. Although powerful and flexible, multiparty policies (e.g., co-allocation) cannot be accomodated by Matchmaking. In this paper we present Gang-Matching, a multilateral matchmaking formalism to address this deficiency. 1. Matchmaking Resource management via Matchmaking occurs as a four-step process. Entities (i.e., servers and customers) requiring matchmaking services express their characteristics