Evaluating container scheduling in clouds
There has been significant progress in cloud scheduler research. Cost-aware schedulers are cloud schedulers that aim to reduce the cloud costs by packing as many tasks as possible onto some number of instances. Stratus is a cost-aware scheduler developed by Andrew et al. to pack tasks efficiently to...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1814812024-12-04T02:10:33Z Evaluating container scheduling in clouds Ng, Hon Joo Tang Xueyan College of Computing and Data Science ASXYTang@ntu.edu.sg Computer and Information Science Cloud Scheduling There has been significant progress in cloud scheduler research. Cost-aware schedulers are cloud schedulers that aim to reduce the cloud costs by packing as many tasks as possible onto some number of instances. Stratus is a cost-aware scheduler developed by Andrew et al. to pack tasks efficiently to reduce cloud costs. This report compares Stratus against a baseline algorithm where one task is mapped to one instance using a round-robin scheme to select instance types. An experimental scheduler is built to feed a historical Microsoft Azure Compute virtual machine (VM) request dataset into both algorithms to compare their costs. Metrics like number of instances, instance core utilisation, memory utilisation, and average maximum task runtime of the instance pool were collected. After conducting the experiments, it is found that Stratus outperforms the baseline algorithm in reducing cloud costs. Bachelor's degree 2024-12-04T02:10:33Z 2024-12-04T02:10:33Z 2024 Final Year Project (FYP) Ng, H. J. (2024). Evaluating container scheduling in clouds. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181481 https://hdl.handle.net/10356/181481 en SCSE23-0841 application/pdf Nanyang Technological University |
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Computer and Information Science Cloud Scheduling Ng, Hon Joo Evaluating container scheduling in clouds |
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There has been significant progress in cloud scheduler research. Cost-aware schedulers are cloud schedulers that aim to reduce the cloud costs by packing as many tasks as possible onto some number of instances. Stratus is a cost-aware scheduler developed by Andrew et al. to pack tasks efficiently to reduce cloud costs. This report compares Stratus against a baseline algorithm where one task is mapped to one instance using a round-robin scheme to select instance types. An experimental scheduler is built to feed a historical Microsoft Azure Compute virtual machine (VM) request dataset into both algorithms to compare their costs. Metrics like number of instances, instance core utilisation, memory utilisation, and average maximum task runtime of the instance pool were collected. After conducting the experiments, it is found that Stratus outperforms the baseline algorithm in reducing cloud costs. |
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Tang Xueyan |
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Tang Xueyan Ng, Hon Joo |
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Final Year Project |
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Ng, Hon Joo |
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Ng, Hon Joo |
title |
Evaluating container scheduling in clouds |
title_short |
Evaluating container scheduling in clouds |
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Evaluating container scheduling in clouds |
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Evaluating container scheduling in clouds |
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Evaluating container scheduling in clouds |
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evaluating container scheduling in clouds |
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Nanyang Technological University |
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2024 |
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https://hdl.handle.net/10356/181481 |
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