Capacity planning for computing clusters

This disclosure provides techniques that enable elastic computing service providers to optimize allocation of computing resources across multiple tiers of service level objectives, while allowing for periods of oversubscription to be met by cross-tier movement of resources. Further, the techniques enable providers of computing services to plan their computing investments given rates of oversubscription and anticipated demand across tiers. Service providers frequently plan investments based on peak, rather than average, demand and may therefore be left with surplus capacity during periods of below-peak demand. While such surplus can be resold at lower levels of service guarantees, such reselling increases demand (oversubscription). By accounting for oversubscriptions and inflations of service level objectives across tiers, the techniques of this disclosure enable optimal multi-tier resource allocation

Grid computing in the optimization of content-based medical images retrieval GRID COMPUTING IN THE OPTIMIZATION OF CONTENT-BASED MEDICAL IMAGES RETRIEVAL*

OBJECTIVE: To utilize the grid computing technology to enable the utilization of a similarity measurement algorithm for content-based medical image retrieval. MATERIALS AND METHODS: The content-based images retrieval technique is comprised of two sequential steps: texture analysis and similarity measurement algorithm. These steps have been adopted for head and knee images for evaluation of accuracy in the retrieval of images of a single plane and acquisition sequence in a databank with 2,400 medical images. Initially, texture analysis was utilized as a pre-selection resource to obtain a set of the 1,000 most similar images as compared with a reference image selected by a clinician. Then, these 1,000 images were processed utilizing a similarity measurement algorithm on a computational grid. RESULTS: The texture analysis has demonstrated low accuracy for sagittal knee images (0.54) and axial head images (0.40). Nevertheless, this technique has shown effectiveness as a filter, pre-selecting images to be evaluated by the similarity measurement algorithm. Content-based images retrieval with similarity measurement algorithm applied on these pre-selected images has demonstrated satisfactory accuracy — 0.95 for sagittal knee images, and 0.92 for axial head images. The high computational cost of the similarity measurement algorithm was balanced by the utilization of grid computing. CONCLUSION: The approach combining texture analysis and similarity measurement algorithm for content-based images retrieval resulted in an accuracy of> 90%. Grid computin