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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This content will become publicly available on June 12, 2025
Designing Reconfigurable Interconnection Network of Heterogeneous Chiplets Using Kalman Filter
Heterogeneous chiplets have been proposed for accelerating high-performance computing tasks. Integrated inside one package, CPU and GPU chiplets can share a common interconnection network that can be implemented through the interposer. However, CPU and GPU applications have very different traffic patterns in general. Without effective management of the network resource, some chiplets can suffer significant performance degradation because the network bandwidth is taken away by communication-intensive applications. Therefore, techniques need to be developed to effectively manage the shared network resources. In a chiplet-based system, resource management needs to not only react in real-time but also be cost-efficient. In this work, we propose a reconfigurable network architecture, leveraging Kalman Filter to make accurate predictions on network resources needed by the applications and then adaptively change the resource allocation. Using our design, the network bandwidth can be fairly allocated to avoid starvation or performance degradation. Our evaluation results show that the proposed reconfigurable interconnection network can dynamically
react to the changes in traffic demand of the chiplets and improve the system performance with low cost and design complexity.
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- NSF-PAR ID:
- 10511372
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- The 34th Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
- ISBN:
- 979-8-4007-0605-9
- Subject(s) / Keyword(s):
- Network-on-Chip, NoC, Chiplets, reconfigurable NoC
- Format(s):
- Medium: X
- Location:
- Tempa, FL, US
- Sponsoring Org:
- National Science Foundation
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