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The rapid evolution of Software-Defined Networking (SDN) has transformed network management by decoupling the control and data planes. It provides centralized control, enhanced flexibility, and programmability of network management services. However, this centralized control introduces security vulnerabilities and challenges related to data integrity, unauthorized access, and resource management. In addition, it brings forth significant challenges in secure and scalable data storage and computational resource management. These challenges are further increased by the need for real-time processing and the ever-increasing volume of data. To address these challenges, this paper presents a scalable blockchain-based framework for security and computational resource management in SDN architectures. The proposed framework ensures decentralized and tamper-resistant data handling and utilizes smart contracts for automated resource allocation. Due to the need for advanced security and scalability in SDN networks, this work incorporates sharding to improve parallel processing capabilities. The performance of sharded versus non-sharded blockchain systems under various network conditions is evaluated. Our findings demonstrate that the sharded blockchain model enhances scalability and throughput with robust security and fault tolerance. The framework is also assessed for its performance, scalability, and security to enhance SDN resilience against data breaches, malicious activities, and inefficient resource distribution.
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This content will become publicly available on December 4, 2026
Adaptive Sharding in Untrusted Environments
Distributed data management systems employ data sharding techniques to achieve scalability. Traditional sharding approaches typically operate under the assumption of a trusted environment, where nodes may crash,but do not act adversarially. In untrustworthy environments, however, this assumption is no longer valid. This paper presents Marlin,an adaptive scalable data management system specifically designed for untrustworthy environments. Marlinleverages data sharding to enhance scalability while dynamically redistributing data across clusters to adapt to dynamic workloads. We propose two architectures: a centralized architecture serving as a baseline, which employs hypergraph partitioning within a trusted administrative domain, and a decentralized architecture that eliminates the need for such a trusted domain by managing shards across nodes in a decentralized manner. Both architectures utilize real-time monitoring and adaptive algorithms to dynamically adjust sharding in response to workload characteristics and adversarial conditions. Experimental results show that Marlinmaintain consistent performance under diverse dynamic scenarios in untrustworthy environments by continuously optimizing shard distributions.
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- Award ID(s):
- 2436080
- PAR ID:
- 10657072
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 3
- Issue:
- 6
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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