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ulti-level scheduling, and infrastructure as a service (IaaS), are proposed in (Juve & Deelman 2008). Advance reservation is achieved by users requesting slots from batch schedulers that specify the number of resources to reserve and the duration of the reservation and the beginning and the end of the advance reservation. Rather than batch scheduler based advance reservations, another advance reservation is to use probabilistic advance reservations in which reservations are made based on statistical estimates of queue times. In multi-level scheduling, the allocation of resources and the management of application tasks are separated in which application tasks are submitted using standard mechanisms, but the node managers are in charge of contacting an external resource manager. Infrastructure as a service (IaaS) enables users to run applications on remote servers by configuring and launching virtual machines on these servers. The virtual machines can be allocated with certain amounts of CPU, disk space and memory, with certain type of operating systems, computing frameworks and application software.

A dynamic resource provisioning framework for dynamically provisioning virtual machines is introduced in (Kusic et al. 2009)to reduce the power consumption in large data centers, by sharing servers among multiple online services utilizing virtualization technology, achieving higher server utilization and energy efficiency while still maintain desired quality of services. A LLC framework is implemented and validated. The switching costs and the risk notion are explicitly encoded in the optimization problem. The experiments show that the LLC framework can save 22% on average in power consumption cost.

3.4Resource Discovery and Selection
There are resource discovery mechanisms such as self-announcer and resource broker. Self-announcer (Bouyer, Mohebi & Abdullah 2009)method is based on machine learning theory. A broker-based approach (Malarvizhi & Uthariaraj 2008)is built based on resource brokers.

Resource selection methods, by manual and by autonomous approaches, are enumerated. In (Malarvizhi & Uthariaraj 2008), local resource managers are implemented to help manage and schedule the resources. The detailed interaction during the resource selection is presented in this paper.

With regard to virtualization technology trends, Dynamic Virtual Infrastructures (DVI) requires dynamic adaptation to the changing user requirements. The Virtual Appliance (VA), the resources such as the guest operating system and applications hosted on the DVI, are deployed on computers across the network. To adapt the changing user requirements, proper VA nodes shall be selected to do the computation. In paper (Bergua et al. 2009), a function for efficient node selection is presented. Five metrics, compute performance, memory capacity, storage capacity, network cost and transfer performance are modeled into the algorithm and certain steps are performed to select optimum nodes.

Large-scale computing infrastructures are often comprised of heterogeneous hardware and software resources. A fully decentralized resource selection algorithm, by which resources are autonomously selected when the attributes match a query, is presented in (Costa et al. 2009). A system model is described that each node in the system is characterized by a set of (attribute, value) pairs like CPU, bandwid