The design of highly reliable and highly utilized clouds strikes a balance between resource utilization that is high enough to use a majority of cloud resources yet is safely below the threshold in which many concurrent failures are likely.
The Cloud scheduling heuristics are adapted to the resource allocation settings (e.g., 1-hour time slots) of Clouds by focusing on maximizing Cloud resource utilization based on the remaining allocation times of Cloud resources.
At Cloud service level, MCCO capabilities span across a range of operations such as selection, assembly, deployment of cloud resources to monitoring their run-time QoS statistics (e.g., latency, utilization, and throughput).
Manual load testing with different sets of cloud resources, followed by comparison of test results to QoS goals is tedious and inaccurate due to the limitations of the load testing tools, challenges characterizing resource utilization, significant manual test orchestration effort, and challenges identifying resource bottlenecks.
The inclusion of cloud services within existing identity federations has gained interest in the last years, as a way to simplify the access to them, reducing the user management costs, and increasing the utilization of the cloud resources.
Utility takes into account the utilization of Cloud computing resources as well as the runtime of an application.
This work presents an adaptive framework optimizing the utilization of Cloud computing resources as well as the runtime of an application.
For instance, the area of green Cloud computing [29], emerging from green-IT [30], tackles the impact of the wide spread utilization of Clouds on the energy consumption regarding network and computing resources.
This motivates the utilization of clouds by scientific researchers as an alternative to using in-house resources [22].
