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Conference Paper
Design of the Cost Effective Execution Worker Scheduling Algorithm for FaaS Platform using Two-step Allocation and Dynamic Scaling
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- Authors
- Youngho Kim, Gyuil Cha
- Issue Date
- 2018-11
- Citation
- International Symposium on Cloud and Services Computing (SC2) 2018, pp.131-134
- Language
- English
- Type
- Conference Paper
- Abstract
- Function as a Service(FaaS) has been widely prevalent in the cloud computing area with the evolution of the cloud computing paradigm and the growing demand for event-based computing models. We have analyzed the preparation load required for the actual execution of a function, from assignment of a function execution walker to loading a function on the FaaS platform, by testing the execution of a dummy function on a simple FaaS prototype. According to the analysis results, we found that the cost of first worker allocation requires 1,850ms even though the lightweight container is used, and then the worker re-allocation cost require 470ms at the same node. The result shows that the function service is not enough to be used as a high efficiency processing calculation platform. We propose a new worker scheduling algorithm to appropriately distribute the worker's preparation load related to execution of functions so that FaaS platform is suitable for high efficiency computing environment. Proposed algorithm is to distribute the worker 's allocation tasks in two steps before the request occurs, and predict the number of workers required to be allocated in advance. When applying the proposed worker scheduling algorithm in FaaS platform under development, we estimate that worker allocation request can be processed with an allocation cost of less than 3% compared to the FaaS prototype. Therefore, it is expected that the functional service will become a high efficiency computing platform through the significant improvement of the worker allocation cost.
- KSP Keywords
- Allocation cost, Cloud Computing, Computing environment, Computing models, Computing platform, Cost Effective Execution, Dynamic Scaling, Event-Based, Function as a service, Lightweight container, Re-allocation