Distributed applications are increasingly deployed at the edge to provide low-latency user access. The hierarchical and localized distribution of edge nodes presents challenges for traditional consensus protocols. The push-based replication method, where the leader pushes updates to followers, can become a performance bottleneck in bandwidth-constrained edge environments. Furthermore, the variation in resources and processing capabilities among edge nodes makes it difficult for this replication method to adapt to the specific conditions of each node, thereby affecting system performance. This paper proposes a new consensus protocol, PPaxos, which employs a pull-based replication method. In this approach, follower nodes within a group proactively pull data from relay nodes, and the relay nodes pull data from the leader node. We have designed an adaptive load-aware pull strategy that allows each node to dynamically adjust its pull frequency based on its own state and to dynamically select relay nodes to reduce performance fluctuations and instability factors. Additionally, a cross-group multi-link communication mechanism ensures that the system remains stable even during relay node failures. Experimental results indicate that PPaxos outperforms other protocols in terms of throughput and latency. Specifically, under conditions of limited bandwidth in edge networks, PPaxos achieves approximately a ${2 9 . 6 \%}$ improvement in throughput and a ${3 4 . 8 8 \%}$ reduction in latency.

The rapid growth of medical data in cloud servers offers a rich resource for medical research and analysis. However, the sensitive nature of this data necessitates robust access controls to ensure privacy and confidentiality. Balancing the utilization of valuable medical information with the protection of personal data privacy poses a significant challenge. To address this challenge, this paper proposes a Hierarchical Retrieval scheme with Bilateral security based on Attribute encryption (BAHR) to facilitate multi-level access control. Based on an attribute-based encryption architecture, the hierarchical access control categorizes users into distinct access groups, allowing for precise data retrieval while maintaining encryption integrity at specified levels. Furthermore, the proposed scheme ensures bilateral security, providing robust protection of patients’ private data, database, and user access patterns within untrusted domains. Through security analysis and experimental evaluation, BA-HR demonstrates good performance regarding retrieval time, decryption time and storage space.

Stateful serverless systems commonly adopt an architectural paradigm characterized by compute and storage separation within cloud data centers. Nevertheless, guaranteeing prompt response for real-time tasks at the edge becomes challenging due to network overheads. This paper introduces a Low-Latency state management framework for real-time tasks in edge serverless systems called LoLa, which adaptively places states proximate to functions, thereby mitigating delays in accessing states within edge serverless systems. Our approach aims at mitigating network latency and optimizing resource utilization by co-locating functions and states, thereby enhancing the system’s overall efficiency. We introduce an adaptive strategy to coordinate the migration of states. It dynamically adjusts the positions of states based on historical data and real-time feedback. Additionally, we designed an in-memory state storage mechanism to facilitate low-latency access and implement a lightweight and fine-grained state management to ensure stored state consistency. Evaluation results showcase the efficacy of LoLa in reducing state read and write latency within edge serverless systems. Specifically, the average response latency is observed to decrease by 65.2% and 38.1% in the best and worst-case scenarios, respectively.