Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined.By analyzing the spot price histories of Amazon’s EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. Our findings suggest that usually prices are not market-driven, as sometimes previously assumed. Rather, they are likely to be generated most of the time at random from within a tight price range via a dynamic hidden reserve price mechanism. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

/pdf/deconstructing-amazon-ec2-spot-instance-pricing-4sfykd81o9.pdf

Deconstructing Amazon EC2 Spot Instance Pricing

Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined. By analyzing the spot price histories of Amazon's EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. We find that prices are usually not market-driven as sometimes previously assumed. Rather, they are typically generated at random from within a tight price interval via a dynamic hidden reserve price. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

/pdf/deconstructing-amazon-ec2-spot-instance-pricing-56r6vo8w7i.pdf

Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.

/pdf/recent-advances-in-computer-aided-drug-design-4yci586fak.pdf

Recent advances in computer-aided drug design

Structural biology, homology modelling and rational drug design require accurate three-dimensional macromolecular coordinates. However, the coordinates in the Protein Data Bank (PDB) have not all been obtained using the latest experimental and computational methods. In this study a method is presented for automated re-refinement of existing structure models in the PDB. A large-scale benchmark with 16 807 PDB entries showed that they can be improved in terms of fit to the deposited experimental X-ray data as well as in terms of geometric quality. The re-refinement protocol uses TLS models to describe concerted atom movement. The resulting structure models are made available through the PDB_REDO databank (http://www.cmbi.ru.nl/pdb_redo/). Grid computing techniques were used to overcome the computational requirements of this endeavour.

/pdf/pdb-redo-automated-re-refinement-of-x-ray-structure-models-1t2k5gwesw.pdf

PDB_REDO : automated re-refinement of X-ray structure models in the PDB

The Grid computing vision promises to provide the needed platform for a new and more demanding range of applications. For this promise to become true, a number of hurdles, including the design and deployment of adequate resource management and information services, need to be overcome. In this context, understanding the characteristics of real Grid workloads is a crucial step for improving the quality of existing Grid services, and in guiding the design of new solutions. Towards this goal, in this work we present the characteristics of traces of four real Grid environments, namely LCG, Grid3, and TeraGrid, which are among the largest production Grids currently deployed, and the DAS, which is a research Grid. We focus our analysis on virtual organizations, on users, and on individual jobs characteristics. We further attempt to quantify the evolution and the performance of the Grid systems from which our traces originate. Finally, given the scarcity of the information available for analysis purposes, we discuss the requirements of a new format for Grid traces, and we propose the establishment of a virtual center for workload-based Grid benchmarking data: The Grid Workloads Archive.

/pdf/how-are-real-grids-used-the-analysis-of-four-grid-traces-and-5bzbnakvax.pdf

How are Real Grids Used? The Analysis of Four Grid Traces and Its Implications

WISDOM is an international initiative to enable a virtual screening pipeline on a Grid infrastructure. Its first attempt was to deploy large scale in silico docking on a public Grid infrastructure. Protein–ligand docking is about computing the binding energy of a protein target to a library of potential drugs using a scoring algorithm. Previous deployments were either limited to one cluster, to Grids of clusters in the tightly protected environment of a pharmaceutical laboratory or to desktop Grids. The first large scale docking experiment ran on the EGEE Grid production service from 11 July 2005 to 19 August 2005 against targets relevant to research on malaria and saw over 41 million compounds docked for the equivalent of 80 years of CPU time. Up to 1,700 computers were simultaneously used in 15 countries around the world. Issues related to the deployment and the monitoring of the in silico docking experiment as well as experience with Grid operation and services are reported in the paper. The main problem encountered for such a large scale deployment was the Grid infrastructure stability. Although the overall success rate was above 80%, a lot of monitoring and supervision was still required at the application level to resubmit the jobs that failed. But the experiment demonstrated how Grid infrastructures have a tremendous capacity to mobilize very large CPU resources for well targeted goals during a significant period of time. This success leads to a second computing challenge targeting avian flu neuraminidase N1.

/pdf/grid-enabled-virtual-screening-against-malaria-2lfkl0oks0.pdf

Grid-enabled Virtual Screening Against Malaria

With Grids, we are able to share computing resources and to provide for scientific communities a global trans-parent access to local facilities. In such an environment the problems of fair resource sharing and best usage arise. In this paper, the analysis of the LPC cluster usage (Clermont-Ferrand, France) in the EGEE Grid environment is done, and from the results a model for job arrival is proposed

Workload analysis of a cluster in a grid environment

With Grids, we are able to share computing resources and to provide for scientific communities a global transparent access to local facilities. In such an environment the problems of fair resource sharing and best usage arise. In this paper, the analysis of the LPC cluster usage (Laboratoire de Physique Corpusculaire, Clermont-Ferrand, France) in the EGEE Grid environment is done, and from the results a model for job arrival is proposed.

/pdf/workload-analysis-of-a-cluster-in-a-grid-environment-7d2ivjw07i.pdf

Large scale grids for in silico drug discovery open opportunities of particular interest to neglected and emerging diseases. In 2005 and 2006, we have been able to deploy large scale virtual docking within the framework of the WISDOM initiative against malaria and avian influenza requiring about 100 years of CPU on the EGEE, Auvergrid and TWGrid infrastructures. These achievements demonstrated the relevance of large scale grids for the virtual screening by molecular docking. This also allowed evaluating the performances of the grid infrastructures and to identify specific issues raised by large scale deployment.

/pdf/virtual-screening-on-large-scale-grids-jg9kk877pm.pdf

Emmanuel Medernach

Papers

Grid-enabled Virtual Screening Against Malaria

Workload analysis of a cluster in a grid environment

Workload analysis of a cluster in a grid environment

Virtual screening on large scale grids

Fair resource allocation for different scenarios of demands