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.
Original language | English |
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Title of host publication | 2017 IEEE 16th International Symposium on Network Computing and Applications |
Subtitle of host publication | NCA 2017 |
Place of Publication | Piscataway, NJ |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 1-8 |
Number of pages | 8 |
Volume | 2017 |
ISBN (Electronic) | 9781538614655 |
ISBN (Print) | 9781538614648 |
DOIs | |
Publication status | Published - 8 Dec 2017 |
Event | 16th IEEE International Symposium on Network Computing and Applications, NCA 2017 - Cambridge, United States Duration: 30 Oct 2017 → 1 Nov 2017 |
Conference
Conference | 16th IEEE International Symposium on Network Computing and Applications, NCA 2017 |
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Country/Territory | United States |
City | Cambridge |
Period | 30/10/17 → 1/11/17 |
Keywords
- Completely Fair Scheduler
- Containerised Scientific Workflows
- Containers
- Docker Engine
- Markov Chain