Geo-Distributed Machine Learning (GDML) aims to train large-scale machine learning models across geographically dispersed datacenters. However, the performance of GDML systems is constrained by the limited Wide Area Network (WAN) bandwidth and the presence of the straggler problem. Existing GDML designs often show contradictory effects in addressing these challenges, while in-network computing attempts are typically restricted to single datacenter environments rather than the more complex GDML scenarios. To overcome these limitations, this paper proposes L3DML to facilitate GDML using the P4-based Software-defined Network (SDN). Our approach incorporates three key innovations. Firstly, we introduce a novel network addressing scheme that enables location-specific in-network gradient aggregation for GDML, eliminating the need for parameter servers. Secondly, we utilize the P4 data plane to integrate lossless gradient transmission within switches. Thirdly, we address the straggler problem by employing a unique Deep Reinforcement Learning (DRL) model set and a corresponding rate synchronization routing approach. L3DML is implemented on a prototype system consisting of several Intel Tofino switches and the Spirent network emulator. Experimental results indicate that L3DML outperforms existing solutions in terms of goodput, model accuracy, and training speed gain for large-scale GDML.

Geocast is a one-to-many communication paradigm that enables the transmission of data packets to a designated area rather than an IP address. The most common geocast solutions rely on the application-layer Geolocation-to-IP database. But these IP-based approaches cannot cope with the challenges of flexibility and mobility in a granularity-customizable geocast scenario. While some non-IP network-layer (L3) attempts have resulted in low addressing accuracy and poor routing scalability. Besides, the clean-slate design is incompatible with the existing network. To address these issues, this paper proposes an innovative network-layer geographic addressing scheme that leverages P4-based Software Defined Networks (SDN) to enable flexible geocast with high accuracy. Based on the aggregation relationship of the geographic area, a network-layer routing strategy is designed to enhance routing scalability. Compatibility is improved by deploying the network-layer designs only at the network edge where granularity-customizable geocast is implemented, without requiring changes to the current IP infrastructure. Then, the network-layer functions are integrated with an application-layer mapping service to support intercommunication between different network edges. Furthermore, a prototype system is built to implement and evaluate the proposed L3Geocast, which outperforms the existing approaches in terms of communication latency and mapping overhead.

Task cooperation emerges as a efficacious strategy for the execution of intricate tasks within the context of challenging wireless edge networks characterized by limited resources and intermittent infrastructure connections. Presently, TCP/IP-based solutions encounter issues related to suboptimal utilization of network resources and a substantial dependency on infrastructure connections. In light of this, Information-Centric Networking (ICN) has surfaced as a promising architectural paradigm aimed at mitigating these challenges. In ICN-based task cooperation, the data reuse characteristic of ICN enhances the efficiency of network resource utilization. However, this also introduces plausible security vulnerabilities to the reused data, encompassing eavesdropping attacks and unauthorized access attacks. In this paper, we propose an ICN-based secure task cooperation scheme to mitigate the above threats without compromising the efficiency of task execution. We present the task cooperation model that quantifies the cost of securing data reuse in task execution. We also introduce a specific naming convention to support the acquisition of collaborative task requests and keys related to the task cooperation. Besides, we introduce a novel design for an enhanced name-based access control scheme that ensure both data confidentiality and access control in collaborative tasks accessed by the same sub-policy, streamlining the encryption process for content keys. Security analysis and experimental results demonstrate that our scheme effectively safeguards the security of data reuse. Furthermore, compared to existing schemes, our scheme incurs lower cost associated with computation and security.

Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking(ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, the specific naming convention is designed to support task cooperation and the acquisition of keys. In addition, our scheme implements fine-grained access control for data reuse in task cooperation combined with attribute-based encryption. Experimental results show that our scheme enhances the security of task cooperation with low cost compared with existing schemes.

Multi-domain networking slice orchestration is an essential technology for the programmable and cloud-native 5G network. However, existing research solutions are either based on the impractical assumption that operators will reveal all the private network information or time-consuming secure multi-party computation which is only applicable to limited computation scenarios. To provide agile and privacy-preserving end-to-end network slice orchestration services, this paper proposes NetChain, a multi-domain network slice orchestration architecture based on blockchain and trusted execution environment. Correspondingly, we design a novel consensus algorithm CoNet to ensure the strong security, scalability, and information consistency of NetChain. In addition, a bilateral evaluation mechanism based on game theory is proposed to guarantee fairness and Quality of Experience by suppressing the malicious behaviors during multi-domain network slice orchestration. Finally, the prototype of NetChain is implemented and evaluated on the Microsoft Azure Cloud with confidential computing. Experiment results show that NetChain has good performance and security under the premise of privacy-preserving.