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This paper presents various transaction sampling algorithms for the proposed real-time crypto computing, and analytical model to assure their dependability under stringent real-time requirement. Efficacy of the algorithms is assessed in terms of the block dependability that expresses the probability for the pending transactions to be posted within the current or the target block delay. Algorithms on prioritising and sampling transactions from pool, to facilitate execution of those transactions within their deadline requirements, such as normal, random, sorted, and stratified, are proposed and simulated. Performance variables such as the number of pending transactions, average speed, gas fees, deadlines, number of miners, are identified and taken into the block dependability in order to reveal the influence of those variables. Extensive parametric simulation results are presented and discussed in the cases of the random and sorted transaction sampling algorithms along with a prototype built based on the Ethereum open source.

This paper proposes a new analytical model to evaluate the availability of a big data computing, namely, map-reduce computing on a Hadoop platform. Map-reduce computing is represented by a queueing model in this work in order to trace flow of tasks (either map or reduce) of their arrivals and exits in the course of computation. The objective of the model is to evaluate the probability for a map-reduce computation to be available at an instance of time, referred to as availability. The set of variables taken into account in this model lists the number of map and reduce tasks, the number of servers (or referred to as nodes in this paper) engaged, along with a few constants such as task arrival/exit rates and node failure/repair rates. The proposed model provides a comprehensive yet fundamental basis to assure and ultimately optimise the design of map-reduce computing in terms of availability with reference to its performance in a simultaneous manner. Parametric simulations have been conducted and demonstrated the efficacy of the proposed model in assessing the availability and the cost.