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 Constraint Satisfaction Problem (CSP) where optimality is based on the solution of a predefined Satisfiability (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 0.47ms to 0.5ms for nodes ranging from 100 to 500 nodes. With edge-cloud collaboration, our results show about 4 percent improvement in overall task success rates.
| Original language | English |
|---|---|
| Pages (from-to) | 115839-115854 |
| Journal | IEEE Access |
| Volume | 9 |
| DOIs | |
| Publication status | Published - 18 Aug 2021 |
| MoE publication type | A1 Journal article-refereed |
Funding
This work was supported in part by the Academy of Finland through the projects: 6G Flagship and DigiHealth under Grant 318927 and Grant 326291, and in part by the AI Enhanced Mobile Edge Computing Project through the Future Makers Program of Jane and Aatos Erkko Foundation and Technology Industries of Finland Centennial Foundation. The work of Ijaz Ahmad was supported by Jorma Ollila Grant.
Keywords
- Cloud Computing
- Constraint Satisfaction Problem (CSP)
- Edge Computing
- Industrial IoT (IIoT)
- Industry 4.0
- Internet of Things (IoT)
- Resource Management
- Software Defined Networking (SDN)
- Software Defined Resource Management (SDRM)
- resource management
- software defined networking (SDN)
- software defined resource management (SDRM)
- edge computing
- Constraint satisfaction problem (CSP)
- cloud computing
