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The complex distributed environment is a major hindrance in the service delivery process as per the customer requirement. Provisioning resources with minimalist conflict and on-demand pay-per-use service has occupied the center stage of service computing in recent times. The paper tries to address the issue of resource provisioning in a dynamic environment by adopting a biologically inspired approach. Using a linear combination of two different models the paper elucidates strategies for allocation of resources in stable and volatile scenarios. Agent technology plays a key role in controlling the parameters for optimized allocation.

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Enterprises are investing heavily in cloud data centers to meet the ever surging business demand. Data Center is a facility, which houses computer systems and associated components, such as telecommunications and storage systems. It generally includes power supply equipment, communication connections and cooling equipment. A large data center can use as much electricity as a small town. In todays world due to the emergence of data-center based computing services, it has become necessary to examine how the costs associated with data centers evolve over time, mainly in view of efficiency issues. We have presented a quasi form of Cobb-Douglas model, which addresses revenue and profit issues in running large data centers. The stochastic form has been introduced and explored along with the quasi Cobb-Douglas model to understand the behavior of the model in depth. Harrod neutrality and Solow neutrality are incorporated in the model to identify the technological progress in cloud data centers.This allows us to shed light on the stochastic uncertainty of cloud data center operations. A general approach to optimizing the revenue/cost of data centers using Cobb Douglas Stochastic Frontier Analysis( CD-SFA) is presented. Next, we develop the optimization model for large data centers. The mathematical basis of CD-SFA has been utilized for cost optimization and profit maximization in data centers. The results are found to be quite useful in view of production reorganization in large data centers around the world.

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