# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 2 Background and Related Research Work
### Background Study  
The large-scale scientific computing platform pools resources from many administrative domains, individuals, and institutions to create a Virtual Organization (VO) [3]. VO enables users to access the large amount of computing resources as a single virtualized computing platform distributed across multiple resource provider locations. The interactions between users, VOs, and shared resources are shown in Figure 1. A user may be a member of one or more VOs, and the same resources can be shared across multiple VOs. Additionally, Figure 1 demonstrates that a VO is composed of a number of _Computing Elements_ (CEs). A CE is a collection of computing resources that are physically located at a resource provider site (i.e., a cluster, supercomputer, a computing farm), and the cluster is a collection of Worker Nodes (WNs) that actually perform the tasks.  
Shared resources are inherently very dynamic and volatile which implies that a resource that was accessible at a certain time or day in the past may not be available in the future for an extended length of time or for a few days at a later time. It is also difficult to locate the available free computing resources since shared resources are constantly departing and rejoining the VO for a variety of reasons including load balancing, hardware/software maintenance, etc. Moreover, the capacity of each computing element, which is defined as the number of available CPU cores, varies significantly across the participating institutions and also fluctuates over time. Furthermore, the performance (i.e., response time) of each resource varies greatly, potentially due to the end-to-end network bandwidth, current system load, local scheduling strategy, and so on.  
For example, Figure 2a presents a typical one-day available CEs and the amount of accessible free CPU cores at the BIOMED VO [12], whereas Figure 2b depicts the average response times of each CE. As we can see from the Figure 2, not only the number of CPU cores available in each CE is varied, but also the response times are, which makes it difficult to forecast the available resources and associated performance of each CE in a Virtual Organization. On the other hand, there is currently no effective Information Service (IS) that can deliver accurate information about shared resources and their states. This  
Figure 1: Large-scale distributed computing infrastructure and interactions between users, VOs, and resource providers.  
information is critical for identifying and monitoring VO resources as well as scheduling user tasks on the relevant CEs.  
In order to address these obstacles and challenges, we have been interested in understanding the computing resource behaviors of VOs over time periods ranging from a few days to weeks that can be utilized for developing an adaptive resource profiling model that takes into account both the resource availability and associated performance. Our ultimate goal is to evaluate the behaviors of CEs and utilize this dynamic information to predict the probability that a CE will be able to supply a certain number of free CPU cores with reasonable amount of response time.