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A Decentralized Auction Framework to Promote Efficient Resource Allocation in Open Computational Grids
Computational grids enable the sharing, aggregation, and selection of (geographically distributed) computational resources and can be used for solving large scale and data intensive computing applications. Computational grids are an appealing target application for market-based resource allocation especially given the attention in recent years to “virtual organizations ” and policy requirements. In this paper, we present a framework for truthful, decentralized, dynamic auctions in computational grids. Rather than a fullyspecified auction, we propose an open, extensible framework that is sufficient to promote simple, truthful bidding by endusers while supporting distributed and autonomous control by resource owners. Our auction framework incorporates resource prediction in enabling an expressive language for end-users, and highlights the role of infrastructure in enforcing rules that balance the goal of simplicity for end users with autonomy for resource owners. The technical analysis leverages simplifying assumptions of “uniform failure” and “threshold-reliability” beliefs.Engineering and Applied Science
A hyper-heuristic for adaptive scheduling in computational grids
In this paper we present the design and implementation of an hyper-heuristic for efficiently scheduling independent jobs in computational grids. An efficient scheduling of jobs to grid resources depends on many parameters, among others, the characteristics of the resources and jobs (such as computing capacity, consistency of computing, workload, etc.). Moreover, these characteristics change over time due to the dynamic nature of grid environment, therefore the planning of jobs to resources should be adaptively done. Existing ad hoc scheduling methods (batch and immediate mode) have shown their efficacy for certain types of resource and job characteristics. However, as stand alone methods, they are not able to produce the best planning of jobs to resources for different types of Grid resources and job characteristics. In this work we have designed and implemented a hyper-heuristic that uses a set of ad hoc (immediate and batch mode) scheduling methods to provide the scheduling of jobs to Grid resources according to the Grid and job characteristics. The hyper-heuristic is a high level algorithm, which examines the state and characteristics of the Grid system (jobs and resources), and selects and applies the ad hoc method that yields the best planning of jobs. The resulting hyper-heuristic based scheduler can be thus used to develop network-aware applications that need efficient planning of jobs to resources. The hyper-heuristic has been tested and evaluated in a dynamic setting through a prototype of a Grid simulator. The experimental evaluation showed the usefulness of the hyper-heuristic for planning of jobs to resources as compared to planning without knowledge of the resource and job characteristics.Peer ReviewedPostprint (author's final draft
Small-world networks, distributed hash tables and the e-resource discovery problem
Resource discovery is one of the most important underpinning problems behind producing a scalable,
robust and efficient global infrastructure for e-Science. A number of approaches to the resource discovery
and management problem have been made in various computational grid environments and prototypes
over the last decade. Computational resources and services in modern grid and cloud environments can be
modelled as an overlay network superposed on the physical network structure of the Internet and World
Wide Web. We discuss some of the main approaches to resource discovery in the context of the general
properties of such an overlay network. We present some performance data and predicted properties based
on algorithmic approaches such as distributed hash table resource discovery and management. We describe
a prototype system and use its model to explore some of the known key graph aspects of the global
resource overlay network - including small-world and scale-free properties
gcodeml: A Grid-enabled Tool for Detecting Positive Selection in Biological Evolution
One of the important questions in biological evolution is to know if certain
changes along protein coding genes have contributed to the adaptation of
species. This problem is known to be biologically complex and computationally
very expensive. It, therefore, requires efficient Grid or cluster solutions to
overcome the computational challenge. We have developed a Grid-enabled tool
(gcodeml) that relies on the PAML (codeml) package to help analyse large
phylogenetic datasets on both Grids and computational clusters. Although we
report on results for gcodeml, our approach is applicable and customisable to
related problems in biology or other scientific domains.Comment: 10 pages, 4 figures. To appear in the HealthGrid 2012 con
The AliEn system, status and perspectives
AliEn is a production environment that implements several components of the
Grid paradigm needed to simulate, reconstruct and analyse HEP data in a
distributed way. The system is built around Open Source components, uses the
Web Services model and standard network protocols to implement the computing
platform that is currently being used to produce and analyse Monte Carlo data
at over 30 sites on four continents. The aim of this paper is to present the
current AliEn architecture and outline its future developments in the light of
emerging standards.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 10 pages, Word, 10 figures. PSN
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