This article proposes a random-forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud-ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single-precision (SP) floating-point operations (FLOPs), double-precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud-ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application-to-Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application-specific random forests (ARF) and application-class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application-class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real-world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

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This investigation was designed for the use of species selection index (SSI) for bio-resource selection under access and benefit sharing mechanism. The bio-resources are being utilized by different industries for manufacturing various end products. Uttarakhand is known for its bio-resources having highly medicinal properties. But, these bio-resources are on the verge of extinction because of over-exploitation. These bio-resources are not being utilized in a sustainable manner. Access and Benefit-sharing is the mechanism by which a species would benefit both producer and traders apart from the conservation. Selection of the concerned species from the area to be considered for ABS mechanism is a tedious work. In this paper, an attempt has been made to solve this issue. The objective was to formulate a methodology for the selection of native species for ABS. A novel method (Species Selection Index) has been formulated for this purpose. Study was conducted at organization, industry and village level. Different species from Uttarkashi, Pauri and Haridwar of Uttarakhand has been analysed by this method and compared with each other. Positive and Negative criteria were considered for the selection and based on the result Terminalia chebula and Zanthoxylem armatum was suggested for ABS. Therefore, The SSI can be used in the selection of any bio-resources to be considered for ABS mechanism or for any other developmental project. More SSI value means more suitable species to be considered for project related activity.

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