Fault Tolerance and Scaling in e-Science Cloud Applications: Observations from the Continuing Development of MODISAzure

Abstract

*Abstract-It can be natural to believe that many of the traditional issues of scale have been eliminated or at least greatly reduced via cloud computing. That is, if one can create a seemingly wellfunctioning cloud application that operates correctly on small or moderatesized problems, then the very nature of cloud programming abstractions means that the same application will run as well on potentially significantly larger problems. In this paper, we present our experiences taking MODISAzure, our satellite data processing system built on the Windows Azure cloud computing platform, from the proof-of-concept stage to a point of being able to run on significantly larger problem sizes (e.g., from national-scale data sizes to global-scale data sizes). To our knowledge, this is the longest-running eScience application on the nascent Windows Azure platform. We found that while many infrastructure-level issues were thankfully masked from us by the cloud infrastructure, it was valuable to design additional redundancy and fault-tolerance capabilities such as transparent idempotent task retry and logging to support debugging of user code encountering unanticipated data issues. Further, we found that using a commercial cloud means anticipating inconsistent performance and black-box behavior of virtualized compute instances, as well as leveraging changing platform capabilities over time. We believe that the experiences presented in this paper can help future eScience cloud application developers on Windows Azure and other commercial cloud providers.

An energy-aware fault tolerant scheduling framework for soft error resilient cloud computing systems

“…At the grand scale of cloud computing, this problem can only worsen [2,3,4,5,6], especially for cost-effective cloud systems built with commodity components [7]. Researchers have witnessed unacceptably high failure rates when running scientific workloads in cloud or grid systems [8,9].…”

A Platform for Parallel R-based Analytics on Cloud Infrastructure

“…climate simulation and analysis [1], biomedical image processing [2], fMRI brain imaging [3], satellite ground systems [4] and data processing [5], astronomy applications [6], Geographic Information Systems (GIS) [7] and disaster response systems [8]). In both the commercial and scientific settings the talents of computer scientists and expert developers are brought to bear in order to efficiently exploit cloud-based infrastructure.…”

Early Observations on the Performance of Windows Azure

“…We will first briefly describe the application and its architecture and then introduce our analysis of its performance on Windows Azure with attention specifically on what we call "VM task execution timeouts". Our description of the application itself is very brief and interested readers are encouraged to examine our other publications for more detail on the development and design of ModisAzure itself [15,16].…”