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rter, St. Louis & Andert 1998)can help with issues of component migration, scheduling, and load balancing across dynamic computational resources. In such an environment, applications run on collections of interconnected computers, also known as networks of workstations (NOW). In this work, heterogeneous CORBA job migration is presented, and a prioritized set of job queues based on the job migration is demonstrated. To provide fault tolerance, application checkpoints are described, that failure of the system is detected automatically and restarted without loss of client jobs.

An efficient online algorithm for co-allocating resources and advanced resource reservations is presented in (Claris, George & Khaled 2009). The algorithm uses specific data structures to store availability of resources. Efficient range searches are designed to identify all available resources. The overall complexity for a successful scheduling attempt is, while is the spatial size of the reservation, N is the amount of servers, Q is the amount of partitions of the system.

The Service Oriented Architecture (SOA) and virtualization of physical resources combination has emerged the Service Oriented Infrastructure (SOI), which provide flexible solution for on demand component accessing. The problem of determining the optimum resource allocation for Virtual Machines is addressed in work (Danilo et al. 2009), in which the resource allocation problem is modeled non-linearly which is able to be solved optimally.

For asynchronous distributed environment, two algorithms, RBA* and OBA, are introduced in (Hegazy & Ravindran 2002)to maximize application QoS and minimize deadline missing ratio. Both algorithms build distributed application models based on Jenson benefit functions to model timely resource requirements in the distributed applications. Adaptation functions are proposed to predict resource requirements in the near future. Application adaptation models are built to accommodate dynamic application replication for sharing increased workload. The underlying network model is built as a switched real-time Ethernet network. The difference of the two algorithms is that RBA* allocates resources by analyzing the process response times of each application, while OBA makes allocation decisions based on the processor workloads. The time cost of RBA* is about, n is the size of the task set; the time cost of OBA is relatively lower, about, with the same meaning of n as RBA*. However, the experiments show that RBA* provides better QoS with less deadline missing.

A multiple degree load balancing resource allocation scheme is introduced in (Lee 2004). Each of load balance degree maps to each resource management configuration and reduces data traffic among distributed components. A High Level Architecture (HLA) bridge middleware environment is also introduced for data bridging among multiple federations.

An autonomous decentralized resource allocation scheme is presented in (Masuishi et al. 2005). A system architecture utilizing the autonomous decentralized resource allocation scheme is designed, in which there is a subsystem that has a production server to process request and a coordination server to adapt resources to load changes. Three architectures, centralized control architecture, autonomous decentralized architecture with independent adaptation and autonomous decentralized architecture