Edge Cloud (EC) is poised to brace massive
machine type communication (mMTC) for 5G and IoT by
providing compute and network resources at the edge. Yet,
the EC being regionally domestic with a smaller scale, faces
the challenges of bandwidth and computational throughput.
Resource management techniques are considered necessary
to achieve efficient resource allocation objectives. Software
Defined Network (SDN) enabled EC architecture is emerging as
a potential solution that enables dynamic bandwidth allocation
and task scheduling for latency sensitive and diverse mobile
applications in the EC environment. This study proposes a
novel Heuristic Reinforcement Learning (HRL) based flowlevel
dynamic bandwidth allocation framework and validates
it through end-to-end implementation using OpenFlow meter
feature. OpenFlow meter provides granular control and allows
demand-based flow management to meet the diverse QoS requirements
germane to IoT traffics. The proposed framework is
then evaluated by emulating an EC scenario based on real NSF
COSMOS testbed topology at The City College of New York. A
specific heuristic reinforcement learning with linear-annealing
technique and a pruning principle are proposed and compared
with the baseline approach. Our proposed strategy performs
consistently in both Mininet and hardware OpenFlow switches
based environments. The performance evaluation considers
key metrics associated with real-time applications: throughput,
end-to-end delay, packet loss rate, and overall system cost
for bandwidth allocation. Furthermore, our proposed linear
annealing method achieves faster convergence rate and better
reward in terms of system cost, and the proposed pruning
principle remarkably reduces control traffic in the network.
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MIRAS: Model-based Reinforcement Learning for Microservice Resource Allocation over Scientific Workflows
Microservice, an architectural design that decomposes applications into loosely coupled services, is adopted in modern software design, including cloud-based scientific workflow processing. The microservice design makes scientific workflow systems more modular, more flexible, and easier to develop. However, cloud deployment of microservice workflow execution systems doesn't come for free, and proper resource management decisions have to be made in order to achieve certain performance objective (e.g., response time) within constraint operation cost. Nevertheless, effective online resource allocation decisions are hard to achieve due to dynamic workloads and the complicated interactions of microservices in each workflow. In this paper, we propose an adaptive resource allocation approach for microservice workflow system based on recent advances in reinforcement learning. Our approach (1) assumes little prior knowledge of the microservice workflow system and does not require any elaborately designed model or crafted representative simulator of the underlying system, and (2) avoids high sample complexity which is a common drawback of model-free reinforcement learning when applied to real-world scenarios. We show that our proposed approach automatically achieves effective policy for resource allocation with limited number of time-consuming interactions with the microservice workflow system. We perform extensive evaluations to validate the effectiveness of our approach and demonstrate that it outperforms existing resource allocation approaches with read-world emulated workflows.
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- Award ID(s):
- 1827126
- NSF-PAR ID:
- 10192135
- Date Published:
- Journal Name:
- 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)
- Page Range / eLocation ID:
- 122 to 132
- 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/ICDCS.2019.00021
