• L Nikhitha
• Dr K Venugopal Rao

Framework invariants demonstrate properties that hold in working states of a registering framework. Invariants might be mined from preparing datasets or surmised amid execution. Logical work has demonstrated that invariants' mining systems bolster a few exercises, including scope organization and recognition of disappointments, irregularities, and infringement of Service Level Agreements. Anyway, their viable application by activity engineers is as yet a test. We plan to fill this gap through an observational examination of three noteworthy methods for mining invariants in cloud-based utility registering frameworks: grouping, affiliation tenets, and choice rundown. The investigations utilize autonomous datasets from certifiable frameworks: a Google bunch, whose follows are freely accessible, and a Software-as-a-Service stage utilized by different organizations around the world. At long last, we propose a general heuristic for choosing likely invariants from a dataset.

Invariants, Cloud, Legitimacy, SaaS, Workload portrayal, Anomaly identification.

"Synthesis of Mining Techniques for Log records in Cloud Computing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.347-352, May-2019, Available :http://www.jetir.org/papers/JETIR1905349.pdf

"Synthesis of Mining Techniques for Log records in Cloud Computing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp347-352, May-2019, Available at : http://www.jetir.org/papers/JETIR1905349.pdf