How to linearize your cluster

In the exploration of potential vulnerabilities within transaction processing, particularly in the context of Replace-by-Fee (RBF) and Child-Pays-For-Parent (CPFP) scenarios, a novel proposal has been put forward to mitigate the time-consuming challenge of finding optimal chunking or linearization for transactions. This approach suggests that individuals submitting transactions that either conflict with existing ones in the mempool or propose an improvement to the current mempool structure should also provide a chunking/linearization solution for all affected transactions. This requirement aims to address the concern that computing an optimal chunking could be prohibitively time-consuming, especially when conducted on low-specification hardware, despite it being feasible to calculate in a reasonable timeframe.

The discussion acknowledges that while most transactions may not necessitate this level of intervention, there exists a subset where such a measure could significantly enhance processing efficiency without imposing undue burdens on the network's infrastructure. To facilitate this, the Core could potentially offer local computation of the chunking/linearization before forwarding the transactions, ensuring that only those submissions that genuinely improve the mempool's state are processed. Such a mechanism would enable the verification of improvements in linear time, which is highlighted as being no more complex than the current validation processes employed within the system.

Moreover, the communication touches upon the feasibility of encoding these chunking solutions in a manner that adds minimal space overhead to the transactions themselves. This aspect is crucial in maintaining the efficiency and scalability of the blockchain, ensuring that the proposed changes do not adversely affect the network's performance. Through this innovative approach, the proposal aims to enhance the security and efficiency of transaction processing, particularly in scenarios where adversarial actions might seek to exploit the computational demands of chunking optimization.