# Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments
## 2 Background and Related Research Work
### Related Work  
Loosely-coupled virtual organizations (VOs) provide significant scalability and cost advantages over conventional distributed systems. However, the availability of nodes and the performance capacity (i.e., number of CPU cores, response time, queuing delay, etc.) of their resources vary significantly in these systems. The performance prediction aids both the Resource Management System (RMS) and users in making resource consumption decisions that are optimal for Quality of Service (QoS) compliance. As a consequence, substantial research on performance modeling of large-scale distributed computing systems, such as Grid [13; 14], Peer-to-Peer [15], and Cloud [16], have been conducted. Figure 3 depicts the most commonly used approaches for performance modeling, which consists of application-oriented and resource-oriented predictions. Application-oriented performance models project the execution times of submitted jobs on the distributed system or profile the application based on its runtime (i.e., short or long running times) or resource utilization patterns such as CPU or I/O intensive, or both [17]. The application-oriented method has the benefit of directly addressing the scheduler's ultimate performance measures (deadlines, runtimes, etc.) [18]. In contrast, resource-oriented performance models are often used to forecast host availability, resource reliability, CPU capability, and end-to-end network bandwidth. A prospective job's background load may be calculated using a resource-oriented strategy. This is directly related to predicting the available CPU capacity or resource level [18]. In this method, predicting the resource level is achievable regardless of the number of job tasks running at any given moment. To make accurate predictions, researchers have typically used time series algorithms to learn from past information or trace data. To model resource performance, several types of resources are explored including CPU load, CPU availability, memory, queuing delay, network bandwidth, disk speed, and so on [19]. [PERSON] et al. [20] examined the performance of numerous linear time series models for predicting the average load on a host system, including Autoregressive (AR), Moving Average (MV), Autoregressive Moving Average (ARMV), and Autoregressive Integrated Moving Average (ARIMV). According to their results, the Autoregressive technique is best suited for estimating CPU load on a single host. However, these kinds of static prediction techniques are effective only for the workloads that are relatively steady. Figure 2: Resource Capacity and Response Time of Each CE on BIOMED VO. [PERSON] et al. [21] have found a subset of hosts that have common statistical characteristics regarding availability. Additionally, they organize hosts by functionality, which helps with scheduling and hence minimizes the makspan. Historical data from SETI@Home was used to simulate resource availability [22]. The authors of [23] investigated the characteristics of node availability in a large-scale shared computing system. They investigated PlanetLab's [24] eligibility period for computing resources, built heuristic models, and projected idle resource capacity using PlanetLab utilization data traces. These preceding performance models often interpret resource availability as a binary state, implying that resources are either accessible or unavailable. However, the authors provide a multi-state availability model in the study [25] to capture the genuine states of resources such as available, user present, CPU threshold exceeded, job eviction, and unavailable. The Network Weather Service (NWS) [26] is a well-studied system for monitoring and predicting distributed resources by using statistical techniques such as running average, sliding window average, and the last measurement. To decrease prediction errors and choose the best predictors, NWS employs a number of adaptive techniques including adaptive window average, adaptive window media, media filter, a-trimmed mean, stochastic gradient, and autoregressive algorithms.