Browsing by Subject "Containers"

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Open Access
Automatic characterization of copy number polymorphism using high throughput sequencing
(TÜBİTAK, 2020) Alkan, Can
Genome structural variation, broadly defined as alterations longer than 50 bp, are important sources for genetic variation among humans, including those that cause complex diseases such as autism, developmental delay, and schizophrenia. Although there has been considerable progress in characterizing structural variation since the beginnings of the 1000 Genomes Project, one form of structural variation called segmental duplications (SDs) remained largely understudied in large cohorts. This is mostly because SDs cannot be accurately discovered using the alignment files generated with standard read mapping tools. Instead, they can only be found when multiple map locations are considered. There is still a single algorithm available for SD discovery, which includes various tools and scripts that are not portable and are difficult to use. Additionally, this algorithm relies on a priori information for regions where no structural variations are discovered in large number of genomes. Therefore, there is a need for fully automated, portable, and user-friendly tools to make SD characterization a part of genome analyses. Here we introduce such an algorithm and efficient implementation, called mrCaNaVaR, that aims to fill this gap in genome analysis toolbox.
Open Access
Stacked job scheduling on virtual machines with containers in cloud computing systems
(2016-06) Akın, Mustafa
Virtualization and use of virtual machines (VMs) is important for both public and private cloud systems and also for users. The allocation and use of virtual machines can be optimized by using knowledge about expectations of users, such as resource demands, network communication patterns, and total budget. However, both public and private cloud providers do not expose advanced configuration options to make use of custom needs of users. Adding upon to previous research, we propose a new approach for allocating and scheduling user jobs to virtual machines by use of container technologies like Docker, so that VM utilization can be increased and costs for users can be decreased. In our approach, by predicting resource demands, we can schedule different kinds of jobs on a single virtual machine without jobs affecting each other and without degrading performance to unacceptable levels. We also allow cost-performance tradeoff for users. We veri fied our approach in a real test-bed and evaluated it with extensive simulation experiments. We also adapted our approach into a real web-based application we developed, called PAGS (Programming Assignment Grading System), which enables efficient and convenient testing, submission and evaluation of programming assignments of a large number students in an interactive or batch manner in identical and isolated system environments. Our approach effectively schedules requests from teachers and students so that the system can horizontally scale in a cost efficient manner.