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with coordinated adaptation, are compared and simulated in this paper. The results show that autonomous decentralized resource allocation is better for server availability and the tracking accuracy for the autonomous decentralized resource allocation with coordinated adaptation is comparable with the centralized control architecture.

The problem of apportioning multiple resources to satisfy a single QoS dimension is addressed in (Rajkumar et al. 1998). This paper first introduces Q-RAM, the QoS-based resource allocation model, and then discusses allocation of a single resource with multiple QoS dimensions. After the discussion of multiple QoS dimensions, the paper analyzes the allocation problem of multiple resources with single QoS dimension. The paper shows that the problem of finding optimal resource allocation is NP-hard. However, a simple polynomial algorithm based on computational geometry helps find a solution very near to the optimal solution of the resource allocation problem.

A decentralized market-based approach allocating resources in a heterogeneous overlay network is presented in (Smith et al. 2008). In this paper, a resource allocation strategy of the overlay network resources is defined to assign traffic dynamically based on the current utilization, thus enabling the system to accommodate fluctuating network demands. A mathematical model of the resource allocation environment is presented and the problem is regarded as a constrained optimization problem.

A resource allocation evaluation framework in Application Layer Networks is presented in (Streitberger et al. 2006). A pyramid of metrics is defined to evaluate resource allocation methods. Two layers of metrics are defined, technical parameters and economic parameters. Technical parameters are technical metrics in the system, such as Discovery Time, Message Latency, Message Size, Service Provisioning Time, and Negotiation Time. Economic parameters are, in the upper layer of the pyramid, derived from technical parameters.

3.2Resource Calculation
In the Software as a Service (SaaS) paradigm, the cost of deployment, customization and hosting of applications can be reduced by sharing with multiple tenants. However, to maximize user experience and minimize the cost, the service provider has to calculate how the resources are allocated. In the work of (Kwok & Mohindra 2008), the resource calculation is addressed and solutions are provided. A multi-tenant placement model is given to place multiple applications in a set of servers.

For satellite networks, to maximize the use of satellite is important. Certain model (Petraki et al. 2007)has been built for MF-TDMA satellite network to help provide minimum timeslots but still guarantee the QoS required in the network. Three algorithms, SIT side algorithm, NCS side algorithm and multiple subset sum algorithm, are given in this work.

3.3Resource Provisioning
Cloud, such as Amazon EC2 and S3, offers storage and computational resources that can be used on demand for a fee. Different usages of the resources in such a cloud have different impact on the final cost. The paper (Deelman et al. 2008)addresses this problem to minimize the total cost of cloud utilization. The paper first introduces a scientific application Montage of its computational workflow; then it presents the computational models and cost models (Amazon EC2); after