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Wait reports is a concept that encompasses the idea of every system can perform a set of tasks and it “knows” how long it had to wait for them to be performed. By observing this information through the time, it is possible to, e.g. follow up the frequency and response time of different tasks over time and relate failure occurrences with high latencies.
Therefore, the purpose of this thesis is to explore statistical analysis and machine learning methods to summarize wait reports from different hosts.
Nowadays, data is an important asset to companies. Systems usually generate data to inform to the external world about its operation and even performance. Some of this data is human readable, e.g. log files, but most of it is a collection of resource utilization and business-driven metrics sampled in a regular interval.
In order to extract useful information from it, statistics and machine learning algorithms can provide insights about these metrics and relationships among them.
In this study, students need to combine several statistical analysis and machine learning techniques to provide insights, summarizing series of wait reports, isolate and recognize patterns, or even predicting overload scenarios.
With that, the objective is to promote a better use of resources and assist the non-technical user to operate the system.
If you feel interested, please send in your application as soon as possible. The start date can be adjusted to both your and the business needs – the intention is to start in January 2020.
While applying please attach your updated CV, current grades and cover letter written in English into one document (under CV field in the application tool) and clearly define your technical knowledge.
If you have any additional questions, please contact our Recruiter Elzbieta Penpeska at email@example.com
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Primary country and city: Sweden (SE) || || Karlskrona || Stud&YP
Req ID: 299902