Browsing by Subject "Virtual machine"

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Open Access
Energy efficient dynamic virtual machine allocation with cpu usage prediction in cloud datacenters
(2018-01) Urul, Gökalp
With tremendous increase in Internet capacity and services, the demand for cloud computing has also grown enormously. This enormous demand for cloud based data storage and processing forces cloud providers to optimize their platforms and facilities. Reducing energy consumption while maintaining service level agreements (SLAs) is one of the most important issues in this optimization effort. Dynamic virtual machine allocation and migration is one of the techniques to achieve this goal. This technique requires constant measurement and prediction of usage of machine resources to trigger migrations at right times. In this thesis, we present a dynamic virtual machine allocation and migration method utilizing CPU usage prediction to improve energy efficiency while maintaining agreed quality of service levels in cloud datacenters. Our proposed method, called LRAPS, tries to estimate short-term CPU utilization of hosts based on their utilization history. This estimation is then used to detect overloaded and underloaded hosts as part of live migration process. If a host is overloaded, some of the VMs running on that host are migrated to other hosts to avoid SLA violations; if a host is underloaded, all of the VMs in that host are tried to be migrated to other machines so that the host can be powered off. We did extensive simulation experiments using CloudSim to evaluate the efficiency and effectiveness of our proposed method. Our simulation experiments show that our method is feasible to apply and can signi cantly reduce power consumption and SLA violations in cloud systems.
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.