Job scheduling (JS) is one of the most important issues in a Cloud system. The objective of job schedulers in cloud computing is to meet users' requirements and optimize the utilization of cloud resources. To achieve better QoS with high resource utilization in a cloud environment, an improved backfill algorithm (IBA) using balanced spiral (BS) method can be used. Results show that IBA minimizes resource idleness up to a great extent. However, IBA does not provide support for handling job priority and QoS. We need an algorithm to maximize the resource utilization while considering priority.
The occurrence of a connected equilibrium set is uncommon in control engineering, and to prove stability for such cases one needs an extended Lyapunov-like theorem, namely iv LaSalle's Invariance Principle. In the process of using this Principle, it is shown that this set of equations possesses a first integral as well. A first integral is identified using the compatibility method, and this first integral is utilized to prove asymptotic stability for a region of the connect equilibrium set. v To all impediments and distractions vi ACKNOWLEDGMENTS
Gear stiffness degrades over the life of a gearbox. In this paper stiffness degradation is identified using pattern classification techniques that rely on the spectral content of the vibration induced during the operation of the gearbox. In particular, the k-nearest-neighbor algorithm, as well as a novel neural network classifier was deployed to address this issue. The classification process was generally able to classify early signs of stiffness degradation. It was found, however, that multiple networks are essential to classification in regions of practical concern. To this end selection of features and clear understanding of the disparity among them play key roles. It was further determined that noise attenuation must be incorporated into the process for the results to be reliable. Finally, the effects of initial conditions must be well understood in order for the diagnostic process to produce reliable conclusions.
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