Abstract
Effective, long-lasting Industrial IoT (IIoT) solutions start with short-term gains and progressively mature with added capabilities and value. The heterogeneous nature of IIoT devices and services suggests frequent changes in resource requirements for different services, applications, and use cases. With such unpredictability, resource orchestration can be quite complicated even in basic use cases and almost impossible to handle in some extensively dynamic use cases. In this paper, we propose SDRM; an SDN-enabled Resource Management scheme. This novel orchestration methodology automatically computes the optimal resource allocation for different IIoT network models and dynamically adjust assigned resources based on predefined constraints to ensure Service Level Agreement (SLA). The proposed approach models resource allocation as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Constraint Satisfaction Problem</i> (CSP) where optimality is based on the solution of a predefined <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Satisfiability</i> (SAT) problem. This model supports centralized management of all resources using a software defined approach. Such resources include memory, power, bandwidth, and edge-cloud resources. SDRM aims at accelerating efficient resource orchestration through dynamic workload balancing and edge-cloud resource utilization, thereby reducing the cost of IIoT system deployment and improving the overall ROI for adopting IIoT solutions. We model our resource allocation approach on SAVILE ROW using ESSENSE PRIME modeling language, we then implement the network model on CloudSimSDN and PureEdgeSim. We present a detailed analysis of the system architecture and the key technologies of the model. We finally demonstrate the efficiency of the model by presenting experimental results from a prototype system. Our test results show an extremely low solver time ranging from 0.47 ms to 0.5 ms for nodes ranging from 100 to 500 nodes. With edge-cloud collaboration, our results show about 4 percent improvement in overall task success rates.
Highlights
THE ongoing industrial revolution, the Industry 4.0, aims at realizing interconnected, responsive, and self-optimizingThe associate editor coordinating the review of this manuscript and approving it for publication was Wei Quan.large-scale production of goods and assets through a seamless integration of advanced manufacturing techniques with Industrial Internet of Things (IIoT)
We propose SDRM, an Software Defined Networking (SDN)-enabled scalable and optimal resource allocation scheme based on SMT and Constraint Satisfaction Problem (CSP) modeling for IIoT applications and use cases
First we analyze the performance of the SAT constraint modeling approach we used for resource allocation based on the execution time of the solver and the number of solver nodes required for different network instances
Summary
THE ongoing industrial revolution, the Industry 4.0, aims at realizing interconnected, responsive, and self-optimizingThe associate editor coordinating the review of this manuscript and approving it for publication was Wei Quan.large-scale production of goods and assets through a seamless integration of advanced manufacturing techniques with Industrial Internet of Things (IIoT). IIoT promises to revolutionize the industrial sector through the power of connected machines, sensors, and devices. The number of such integrated devices are estimated to run into tens of billions of devices over the decade [7]. In [20], authors presented a resource service model for an underground intelligent mine leveraging on IoT platforms For this use case, authors proposed a resource service model based on Transparent Computing (TC). Authors proposed a resource service model based on Transparent Computing (TC) This model took a centralized approach to resource management, while distributed architecture was utilize for resource storage. The model provides a scalable approach to resource expansion, where increase in number of devices leads to a corresponding increase in the number of active servers set to handle various IIoT applications
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