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that, simulations are conducted based on the models. Results show that for a data-intensive application with a small computational granularity, the storage costs are insignificant compared to CPU costs.

In Service Oriented Architecture (SOA), a set of low-level atomic services are composite to form the high-level services, in which capacity planning and resource provisioning is important. In paper (Chun et al. 2008), a black-box method for estimating service requests demanded CPU resource is presented. The method is based on linear regression between the observed requests and resource utilization. The service composition relationship discovery is also introduced. The discovered service composition relationship can further be used to improve the CPU demand estimation quality.

A absolute delay guaranteed resource provision technique for real-time applications is presented in (Chunfeng et al. 2003). Runtime network workload uneven distribution factors are considered to configure resources provided. A resource provision model for real-time applications is built, in which the end-to-end worst-case delay for real-time traffic, the bandwidth constraint, the delay constraint, and the admission probability are formulated. The results show that the overall resource utilization by admission probability is higher than the uniform UBAC method.

A general complex resource provisioning (CRP) model and the major requirements to the authorization service infrastructure (AuthZ) to support multi-domain CRP are defined in (Demchenko, Gommans & de Laat 2007). Two main issues are focused, AuthZ session support and policy expression for CRP models. The eXtensible Access Control Markup Language and its special profiles to specify complex resources access control policies are described. A XML based AuthZ ticket format is proposed to support extended AuthZ session context. Specific functionality are added to the gLite Java Authorization Framework (gJAF) to handle dynamic security context, e.g. the AuthZ sessions.

An application-level BPEL workflow on demand resource provisioning is introduced in (Dornemann, Juhnke & Freisleben 2009), in which BPEL service calls are scheduled according to the target host loads. The solution schedules workflow steps to underutilized servers and allocates extra hosts utilizing the Cloud computing infrastructures at service load peaks. The BPEL standard is not required to modify in this approach. The Cloud computing infrastructure utilized in this paper is Amazon’s Elastic Compute Cloud (EC2). The BPEL implementation is ActiveBPEL engine.

A Universal Factory Service (UFS) that provides dynamic resource deployment and resource broker named the Door service, is introduced in (Eun-Kyu et al. 2005).

Autonomous Decentralized Community System (ADCS) is proposed to adapt transportation services (Filali, Hafid & Gendreau 2008), in which global mobile communication technologies are used and service condition changes dynamically. Based on ADCS, resources are shared among transportation neighbors that each node provides resources to other node requests. Autonomous collaboration technology is proposed to satisfy the cooperation among computational nodes. By evaluation of a taxi dispatching application, effectiveness of ADCS is approved.

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