A comparative analysis of dynamic grids vs. virtual grids using the A3pviGrid framework

Avinash Shankaranarayanan,Christine Amaldas

doi:10.6026/97320630005186

Abstract

With the proliferation of Quad/Multi-core micro-processors in mainstream platforms such as desktops and workstations; a large number of unused CPU cycles can be utilized for running virtual machines (VMs) as dynamic nodes in distributed environments. Grid services and its service oriented business broker now termed cloud computing could deploy image based virtualization platforms enabling agent based resource management and dynamic fault management. In this paper we present an efficient way of utilizing heterogeneous virtual machines on idle desktops as an environment for consumption of high performance grid services. Spurious and exponential increases in the size of the datasets are constant concerns in medical and pharmaceutical industries due to the constant discovery and publication of large sequence databases. Traditional algorithms are not modeled at handing large data sizes under sudden and dynamic changes in the execution environment as previously discussed. This research was undertaken to compare our previous results with running the same test dataset with that of a virtual Grid platform using virtual machines (Virtualization). The implemented architecture, A3pviGrid utilizes game theoretic optimization and agent based team formation (Coalition) algorithms to improve upon scalability with respect to team formation. Due to the dynamic nature of distributed systems (as discussed in our previous work) all interactions were made local within a team transparently. This paper is a proof of concept of an experimental mini-Grid test-bed compared to running the platform on local virtual machines on a local test cluster. This was done to give every agent its own execution platform enabling anonymity and better control of the dynamic environmental parameters. We also analyze performance and scalability of Blast in a multiple virtual node setup and present our findings. This paper is an extension of our previous research on improving the BLAST application framework using dynamic Grids on virtualization platforms such as the virtual box.

Highlights

Bioinformatics heavily [7, 8] relies upon statistical and analytical methods of processing biological data
We propose a new methodology for Grid computing; to use virtual machines as Virtual Grid Environments (VGE) that provides computing resources to Grid users having customized requirements originating from different platforms having varied Quality of Service (QoS) constraints
Initial Results: All of the A3pviGrid agents initially ran on individual workstations and the initial results were obtained with a mini-grid test-bed of 10 nodes

Summary

Introduction

Bioinformatics heavily [7, 8] relies upon statistical and analytical methods of processing biological data. We propose a new methodology for Grid computing; to use virtual machines as Virtual Grid Environments (VGE) that provides computing resources to Grid users having customized requirements originating from different platforms having varied Quality of Service (QoS) constraints. We can agree that deploying virtual environments for Grid computing can bring about user enabled compute and resource customization, QoS sharing, data manipulation and easy management.

Results

Conclusion