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Systems for Internet of Things (IoT) have generated new requirements in all aspects of their development and deployment, including expanded Quality of Service (QoS) needs, enhanced resiliency of computing and connectivity, and the scalability to support massive numbers of end devices in a variety of applications. The research reported here concerns the development of a reliable and secure IoT/cyber physical system (CPS), providing network support for smart and connected communities, to be realized by means of distributed, secure, resilient Edge Cloud (EC) computing. This distributed EC system will be a network of geographically distributed EC nodes, brokering between end-devices and Backend Cloud (BC) servers. This paper focuses on three main aspects of the CPS: a) resource management in mobile cloud computing; b) information management in dynamic distributed databases; and c) biological-inspired intrusion detection system.
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Flow-level Dynamic Bandwidth Allocation in SDN-enabled Edge Cloud using Heuristic Reinforcement Learning
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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- PAR ID:
- 10289812
- Date Published:
- Journal Name:
- IEEE Conference, on Future Internet of Things and Cloud
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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