With the recent development of open cloud systems a surge in outsourcing assignments from an internal server to a cloud supplier has been seen. The Cloud can facilitate its clients enormous resources hence even during heavy load conditions. Since the cloud needed to be handle multiple clients workload at same time and each client may have different resource requirements hence choosing proper resources for given workload in such a system, in any case, is a difficult problem. This paper addresses this streamlining issue in a cloud system with different client's priority groups and resource requirements and proposes a bee colony based Multi-Objective load balancing technique, to attain efficient load scheduling over virtual machines under cloud. The proposed algorithm assigns the workload on the virtual machines in such a way that it minimizes the total processing cost in cloud without sacrificing priority of tasks and load management performance.
Cloud Computing is an emerging technology and it is used where computation, data manipulation and information sharing are needed. As number of Cloud users is increasing day by day, which leads to various security issues like unauthorized and unnecessary service access. To provide access control or preventing unauthorized access of data stored at Cloud Server Storage, the existing system applies cryptographic methods by sharing the decryption keys among intended authorized users or members. However, to perform these operations, existing system needs heavy computation power and resources. achieving scalability and access control simultaneously for file storage service in cloud environment is a big concern. The issues are yet not fully resolved. Our scheme proposed the solution for these issues by a mechanism that defines a package of Role based access control, Attribute based access control and security with scalable infrastructure simultaneously.
Decision-making process is supported by Machine learningbased classification techniques in many areas of health care. Classification performance of decision system can be improved using the attribute reduction mainly in the situation of high data dimensionality dilemma .This paper proposes, Random forest Classifier (RFC) approach which is based on the Variable Precision Rough Set (VPRS) theory. The first phase of proposed approach focus at attribute reduction of available dataset using VPRS .Directing from dimensionality reduction to predictive model construction, and in next phase, the obtained abridged dataset is provided as the input of RFC to build a more accurate classification model. The performance is evaluated in terms of classification accuracy and time complexity. The experimental results show that the enhanced RFC has higher accuracy and correctly classified instances as compared with the existing algorithms.
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