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Creators/Authors contains: "Tai, Charlie"

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  1. ; ; ; ; ; ; (, ACM/IEEE International Symposium on Computer Architecture (ISCA))
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  2. ; ; ; ; ; (, IEEE International Symposium on High-Performance Computer Architecture (HPCA))
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  3. ; ; ; ; ; ; ; ; ; ; et al (, 2020 6th IEEE International Conference on Network Softwarization (NetSoft))
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    Network function virtualization (NFV) technologyattracts tremendous interests from telecommunication industryand data center operators, as it allows service providers to assignresource for Virtual Network Functions (VNFs) on demand,achieving better flexibility, programmability, and scalability. Toimprove server utilization, one popular practice is to deploy besteffort (BE) workloads along with high priority (HP) VNFs whenhigh priority VNF’s resource usage is detected to be low. The keychallenge of this deployment scheme is to dynamically balancethe Service level objective (SLO) and the total cost of ownership(TCO) to optimize the data center efficiency under inherentlyfluctuating workloads. With the recent advancement in deepreinforcement learning, we conjecture that it has the potential tosolve this challenge by adaptively adjusting resource allocationto reach the improved performance and higher server utilization.In this paper, we present a closed-loop automation systemRLDRM1to dynamically adjust Last Level Cache allocationbetween HP VNFs and BE workloads using deep reinforcementlearning. The results demonstrate improved server utilizationwhile maintaining required SLO for the HP VNFs. 
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