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- W2774502204 abstract "Scientific workflows are increasingly containerised, which requires rethinking central processing unit (CPU) sharing policies to accommodate different workload types. However, container engines running scientific workflows struggle to share the CPU fairly, as workload characteristics are not taken into account. This paper proposes a sharing policy called the Adaptive Completely Fair Scheduling policy (adCFS), which considers the future state of CPU usage and proactively shares CPU cycles between various containers based on their corresponding workload metrics (e.g., CPU usage, task runtime, #tasks). adCFS estimates the weight of workload characteristics and redistributes the CPU based on the corresponding weights. The Markov chain model is used to predict CPU state use, and the adCFS policy is triggered to dynamically allocate containers to the proper CPU portions. Experimental results show enhanced container CPU response time for those containers that run heavy and large jobs: these display 12% faster response time compared with the default CFS (Completely Fair Scheduler). adCFS therefore enhances CFS by considering workload metrics, which leads to the CPU being shared fairly when it is fully used." @default.
- W2774502204 created "2017-12-22" @default.
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- W2774502204 date "2017-10-01" @default.
- W2774502204 modified "2023-10-14" @default.
- W2774502204 title "adCFS: Adaptive completely fair scheduling policy for containerised workflows systems" @default.
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- W2774502204 doi "https://doi.org/10.1109/nca.2017.8171362" @default.
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