Sharding, as a Layer-1 scaling technique, is widely recognized as a promising solution to address the scalability limitations faced by blockchains. However, distributing accounts across shards generates numerous cross-shard transactions (CSTs) and inter-shard workload imbalances, potentially compromising scalability. Existing state-of-the-art methods typically optimize transaction distribution for subsequent epochs by periodically reallocating accounts using graph partitioning or community detection to balance loads and minimize CSTs. Nevertheless, these methods often involve computationally expensive account migration and overlook real-world transaction skewness, exacerbating workload imbalances. To address the above problems, we propose a two-phase account group migration service with dynamic load awareness in this paper. This service can monitor and analyze system state to determine the necessity of account migration. Once triggered, it employs a two-phase algorithm that combines account grouping with global group-level re-partitioning to balance workloads and minimize CSTs. Additionally, group-level migration further helps reduce computational overhead. We evaluate the designed service by replaying large-scale real Ethereum transactions. Experimental results demonstrate that, compared with the baselines, our method can not only improves system throughput and reduces transaction confirmation latency, but also enhances overall scalability.

Traditional single-cloud storage suffers from single points of failure, leading to low data availability. As a result, it fails to meet users' demands for reliable cloud storage services. Therefore, the current cloud storage paradigm has shifted to distributed-cloud storage (e.g., multi-cloud storage, JointCloud storage), where users store multiple replicas of data across multiple Cloud Service Providers (CSPs). However, this imposes significant storage pressure on CSPs. To reduce costs and maximize profits, some malicious CSPs may delete user data, undermining trust in cloud services and hindering the growth of the cloud computing industry. To address this issue, we propose a novel distributed-cloud storage based on permissioned blockchain, which effectively reduces storage costs while ensuring data availability. Firstly, we integrate Byzantine Fault Tolerance in permissioned blockchain with erasure coding (EC) to replace the traditional multi-cloud multi-replica storage approach. This integration significantly reduces storage costs while providing an efficient means for data recovery. Based on blockchain, we further propose a data integrity auditing approach that eliminates reliance on semi-trusted third-party auditors and enables decentralized data integrity verification. Combined with this auditing approach, our EC-based data recovery approach ensures data availability while enhancing users' trust in distributed-cloud storage. Theoretical analysis indicates that our scheme reduces storage overhead from$O(n)$to$O(1)$with$n$CSPs while ensuring data availability. Meanwhile, experimental results demonstrate that computational overhead is reduced by approximately 78% compared to traditional multi-cloud multi-replica storage, achieving the cost-effective and highly reliable distributed-cloud storage.