A Greedy Load Balancing Algorithm for FaaS Platforms

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In this paper, we propose a new load balancing algorithm for function-as-a-service (FaaS) platforms. We argue that the load balancing algorithm greatly affects the performance of FaaS platforms because heavy operations, such as virtualization and initialization, can be reduced using caching techniques. We demonstrate that a load balancing algorithm that provides higher locality could accelerate the FaaS platforms by increasing the cache-hit ratio, and propose a greedy load balancing algorithm optimized for FaaS. To generalize the experimental results, we conducted the experiment under three different caching policies that could be adopted in various FaaS platforms. Our evaluation reveals that load balancing algorithms with higher locality and a uniform load balance exhibit better results in all three caching policies, and our proposed algorithm achieves better performance compared to the state-of-the-art algorithms.
Publisher
Association for Computing Machinery
Issue Date
2021-08-13
Language
English
Citation

5th International Conference on Cloud and Big Data Computing, ICCBDC 2021, pp.63 - 69

DOI
10.1145/3481646.3481657
URI
http://hdl.handle.net/10203/291694
Appears in Collection
CS-Conference Papers(학술회의논문)
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