Hyperledger Fabric is one of the most popular federation chains and is widely used in many fields, such as healthcare, government, and education. However, in practical application scenarios, Fabric faces the challenge of handling a large number of aborted transactions. These transaction failures are primarily caused by read-write conflicts resulting from read-write locks during the execution and validation phases, as well as transaction ordering dependencies in the ordering phase. The frequent occurrence of aborted transactions significantly reduces system throughput, as each failed transaction not only wastes computational resources and network bandwidth but also requires reprocessing. Therefore, this paper proposes a solution called Fabric*, which includes a transaction reordering mechanism, a cache queue mechanism, and a lock-free mechanism. The former two mechanisms are used to abort conflicting transactions early in the ordering phase and to avoid aborting conflicting transactions again. The latter mechanism is used to abort transactions that read obsolete data during the simulation phase. Fabric* reduces abort transactions and increases the throughput of successful transactions in the system. Experimental results show that Fabric* improves throughput by up to about 23.9% and 7.4% over the original Fabric and Fabric++.

The Industrial Internet of Things (IIoT) generally uses cloud computing mode to process tasks. However, in cases of excessive task volume, a high workload will lead to significant processing delays. Edge computing, due to its ability to achieve load balancing and reduce processing latency, is an effective solution. Existing research mainly focuses on how to schedule multiple tasks and multiple edge nodes effectively but lacks consideration of the correlation between tasks. In view of the shortcomings of current work, this paper defines an edge computing scheduling problem for related tasks in IIoT scenario and proves that this problem is NP-Hard. A related task scheduling method is proposed based on a Directed Optimization Genetic Algorithm(DO-GA). Finally, an experimental environment is set up to perform a performance comparison analysis of the proposed algorithm. Experimental results show that, in terms of convergence, scheduling success rate and stability, the method proposed in this paper outperforms traditional Genetic Algorithm(GA) and Particle Swarm Optimization(PSO).

Throughput performance is a critical issue in blockchain technology, especially in blockchain sharding systems. Although sharding proposals can improve transaction throughput by parallel processing, the essence of each shard is still a small blockchain. Using serial execution of smart contract transactions, performance has not significantly improved, and there is still room for improvement. A smart contract concurrent execution strategy based on concurrency degree optimization is proposed for performance optimization within a single shard. This strategy is applied to each shard. First, it characterizes the conflicting contract feature information by executing a smart contract, analyzing the factors that affect the concurrent execution of the smart contracts, and clustering the contract transaction. Second, in shards with high transaction frequency, considering the execution time, conflict rate, and available resources of contract transactions, finding a serializable schedule of contract transactions by redundant computation and a Variable Shadow Speculative Concurrency Control (SCC-VS) algorithm for smart contract scheduling is proposed. Finally, experimental results show that the strategy increases the concurrency of smart contract execution by 39% on average and the transaction throughput of the whole system by 21% on average.

Recently there is a growing interest of incorporating cellular architecture (with wired base stations and last-hop wireless connections) into fieldbuses to support mobile real-time applications. A promising trend is that such cellular fieldbuses will go multichannel multiradio, due to the wide availability of cheap multichannel commercial-off-the-shelf (COTS) wireless nodes, and the rise of 4G and future cellular technologies. For multichannel multiradio cellular fieldbuses, per-flow real-time schedulability guarantee in the inter-cell level has not yet been well studied. Particularly, unlike 3G cellular networks, which use static FDMA/CDMA to isolate cells, the multichannel multiradio feature allows neighboring cells to use the same radio frequency channel at different time-slots, or the same time-slot at different radio frequency channels. How to carry out channel time-slot scheduling is therefore the focus of this paper. To address this issue, we propose a greedy scheduling algorithm, together with a polynomial time closed-form schedulability test. The relationship between the schedulability test result, greedy scheduling schedulability, and schedulability is explored. We prove the equivalence of the three for chained cellular fieldbus topology, a typical topology with broad applications. This also implies the optimality of greedy scheduling, and the sufficiency and necessity of the schedulability test in the context of chained topology. To demonstrate and validate these schedulability theories, we carry out a case study on a classic admission planning problem. The schedulability test not only serves as a planning constraint, but also guides us to propose an approximation algorithm to solve the NP-hard admission planning problem. Comparisons to exhaustive search corroborate the validity of our schedulability theories.