英语论文网

th and disk quota. A resource discovery protocol, including the overlay network topology and query routing, is also defined.

A online automatic resource selection approach based on control theory is presented in (Hao, Sorensen & Nazir 2009), in which a utility-based learning and tuning algorithm is used to enable the automatic resource tuning and selection. A middleware is designed to listen to the application execution performance report and re-configure execution environment intelligently to meet the performance requirements. Execution Satisfaction Degree (ESD) is defined to measure the satisfaction of application execution QoS. Applications need to define the ESD function to represent execution satisfaction. Furthermore, applications are required to be adaptive, that they are able to add or release resources during the execution at any time. Application Agent (AA), an application-level middleware interact with the underlying computing infrastructure to transparently provide the execution environment, is extended to provide resources autonomously. The theory of the utility-based policy is to find the utility to each candidate resource of the application. The utility is defined as accumulative contribution that a host made to the application. AA calculates the utility by the reported ESDs and the provided hosts handled. AA uses the ordered utility values to operate on hosts to make the ESD approaching 1.0. AA keeps updating the utility values iteratively and manages the resources as the result.

A empirical prediction model that generates an appropriate resource allocation specification based on a DAG structured workflow application is given in (Huang, Casanova & Chien 2007). The resource allocation specification includes the number of resources, the range of resources clock rates and network bandwidth. The model, using an optional utility function, with the application DAG structure kept in mind, trades off cost and performance. A resource model is also built to evaluate the application model since it’s too expensive and time consuming to do experiments in a large-scale distributed computing environment. The resource environment is assumed to be composed of reservation based resources and the middleware layer can acquire dedicated resources on behalf of the user. Compute resources are assumed to have identical processor architectures and their speed depends only on their clock rates. Applications are simulated based on performance models of the tasks. The resource selection system returns the user with a resource collection (RC) or a set of hosts on which the user can execute the desired application. The “best” RC is defined as one that minimizes application turn-around time. Two parameters of RC are given, number of hosts and coefficient of variance of host clock rates. Results show than using the prediction model is far more cost effective while achieving better performance.

The notion of accessibility to evaluate both availability and performance is introduced in (Kim, Chandra & Weissman 2008). A system model is provided that nodes are separated into two types, compute nodes and data nodes. Data nodes store data objects required in the computation; compute nodes execute actual application jobs. Both compute and data nodes are connected in an overlay structure. The nodes are decentralized that any node in the system can submit a job. A job is defined as a unit of work that does