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Padmapriya, A.
- Fault Tolerant Dynamic Task Clustering to Improve Workflow make Span in Clouds
Authors
1 Department of Computer Application, Alagappa University, Karaikudi, Tamil Nadu, IN
2 Department of Computer Science, Alagappa University, Karaikudi, Tamil Nadu, IN
Source
International Journal of Knowledge Based Computer System, Vol 5, No 1 (2017), Pagination: 1-4Abstract
Task clustering is a compute intensive method that will reduce execution overhead there by improving the computational granularity in clouds. Usually a job is composed of one or more tasks. The jobs with multiple tasks are always having higher risk of failures when compared to single task job. Clustering strategies can be used to reduce the impact of job failures. Fault tolerant clustering methods are used to enhance the runtime execution of workflow executions. Static task clustering is the widely employed clustering method for faulty environments. The proposed work utilizes Dynamic task clustering to improve the workflow make span by dynamically modifying the clustering granularity whenever there arises chances of failure. The proposed method performs well to adapt unexpected behaviours and provides better make-spans when compared to the static method.
This paper discusses the implication of the rise of big data and especially that of high velocity data in the domain of high frequency trading (HFT), a growing niche of securities trading. We first take a brief look at the intricacies of HFT including some of the commonly used strategies used by HFT traders. The technological challenges in processing HFT and responding to the real time changes in the market conditions are also discussed. Some of the potential technological solutions to solve the issues thrown up by HFT are analyzed for their effectiveness to address the real time performance requirements of HFT. We identify Complex event processing (CEP) as a candidate to address the HFT problem. The paper is divided into 3 parts; part A deals with understanding HFT and the challenges that it poses to the technological processing. In Part B we look at complex Event Processing (CEP) and the types of problems it can be applied to. In Part C we show a framework to process HFT using techniques derived from CEP.
Keywords
Fault-Tolerant, Scientific Workflows, Task Clustering.References
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- Secure Ranked Keyword Search Over Encrypted Cloud Data
Authors
1 Department of Computer Application, Alagappa University, Karaikudi, Tamil Nadu, IN
2 Department of Computer Science, Alagappa University, Karaikudi, Tamil Nadu, IN
Source
International Journal of Knowledge Based Computer System, Vol 5, No 2 (2017), Pagination: 7-10Abstract
Distributed computing is a system of remote servers facilitated on the Internet and used to store, oversee and prepare information set up of neighborhood servers. In the existing system three techniques used to extract information from the cloud. In this paper, a vector space show is utilized and each record is spoken to by a vector, which implies each report can be viewed as a point in a high dimensional space. The interdependence between varieties of documents are grouped into several categories. The pursuit time can be to a great extent decreased by choosing the coveted class and deserting the insignificant classifications. Cloud server will first search the categories and get the minimum desired sub-category. At that point the cloud server will select the desired k documents from the least possible desired sub-category. The value of k is to be decided by the user earlier and sent to the cloud server. To verify the search result, user has to verify the virtual ischolar_main, instead of verifying every document. Furthermore, the proposed technique has favorable position over the traditional method in the rank privacy and relevance of retrieved documents.Keywords
Cloud Computing, Cloud Services, Keyword Search, Raking.References
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