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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Learning Optimal Traffic Routing Behaviors Using Markovian Framework in Microscopic Simulation
This article applies the existing Markovian traffic assignment framework to novel traffic control strategies. In the Markovian traffic assignment framework, transition matrices are used to derive the traffic flow allocation. In contrast to the static traffic assignment, the framework only requires flow split ratio at every intersection, bypassing the need of computing path flow allocation. Consequently, compared to static traffic assignment, drivers’ routing behaviors can be modeled with fewer variables. As a result, it could be used to improve the efficiency of traffic management, especially in large scale applications. To begin with, the article introduces Markovian traffic assignment and connects it to the classic static traffic assignment. Then, the framework is extended to dynamic traffic assignment using microscopic traffic simulator Simulation of Urban Mobility (SUMO). In a case study, the framework is applied to a standard benchmark network, where optimal routing behaviors are independently learned through grid search, random search, and evolution strategies, under three different reward functions (network outflow, total vehicle hours of travel, and average marginal regret). The case study shows that the this novel traffic control strategy is promising, as Markov chain theory supports the ability to scale up to larger networks.
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- Award ID(s):
- 1837244
- NSF-PAR ID:
- 10208681
- Date Published:
- Journal Name:
- Transportation Review Board Annual Meeting 2020
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP:d atap reservationp roblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit.In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
