Resource flexing is the notion of allocating resources on-demand as workload changes.
This is a key advantage of Virtualized Network Functions (VNFs) over their
non-virtualized counterparts. However, it is difficult to balance the timeliness and
resource efficiency when making resource flexing decisions due to unpredictable workloads and complex VNF processing logic.
In this work, we propose an Elastic resource flexing system for Network functions
VIrtualization (ENVI) that leverages a combination of VNF-level features and
infrastructure-level features to construct a neural-network-based scaling decision
engine for generating timely scaling decisions. To adapt to dynamic workloads,
we design a window-based rewinding mechanism to update the neural network with
emerging workload patterns and make accurate decisions in real time. Our experimental
results for real VNFs (IDS Suricata and caching proxy Squid) using workloads generated
based on real-world traces, show that ENVI provisions significantly fewer (up to 26%)
resources without violating service level objectives, compared to commonly used
rule-based scaling policies.
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RLDRM: Closed Loop Dynamic Cache Allocation with Deep Reinforcement Learning for Network Function Virtualization
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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- Award ID(s):
- 1730628
- PAR ID:
- 10221240
- Date Published:
- Journal Name:
- 2020 6th IEEE International Conference on Network Softwarization (NetSoft)
- Page Range / eLocation ID:
- 335 to 343
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/NetSoft48620.2020.9165471
