In previous work, we have elaborated a utility architecture that optimizes resource deployment according to business policies, as well as a mechanism for optimization in SLA negotiation.
The work presented in [16] describes an algorithm that optimizes resource allocation in a streaming scenario with multiple clients and access networks.
A tiered approach was developed that accommodates different data situations, optimizes resource usage, limits biotesting as far as possible and ensures adequate protection of the environment.
Contemporary cloud computing research has seldom focused on solutions that provide both high availability and real-time assurance to these applications in a way that also optimizes resource consumption in data centers, which is a key consideration for cloud providers.
This control system simultaneously optimizes resource energy consumption by limiting energy wastage through the real-time control of resource states, and by dynamically controlling the overall power consumption taking the limited availability of energy into consideration.
The outlined strategy keeps the initial data demands as low as possible (i.e. optimizes resource efficacy and limits unnecessary testing), while at the same time ensuring an adequate protection of the environment according to the philosophy and approaches of the BPD/BPR.
