A cost-provable solution for reliable in-network computing-enabled services deployment

Recently, the in-network computing (INC) technique has been widely adopted by various applications including the reliability-sensitive ones such as remote surgery, and autonomous vehicle systems. To deploy reliable INC-enabled services, redundant task replicas are hosted by network devices to meet a specified service reliability threshold such as 99.9%, 99.99%, and 99.999%. Most existing works assume that this threshold is solely impacted by the software reliability while neglecting the hardware reliability. This neglectedness likely leads to unexpected service interruptions when the software replicas are co-deployed over one unreliable hardware. This work jointly considers the heterogeneous reliability brought by both software and hardware and identifies a novel phenomenon called “Software-Reliability-Only Experience Degradation” (SRO-ED). To address this, we mathematically establish the INC-enabled services adoption with heterogeneous reliability (ISAHR) problem to optimize service costs and prove its NP-hardness. We introduce an effective Cost-Reliability (CR) measure to indicate the average cost needed to satisfy each reliability unit while considering both software and hardware reliabilities. Next, we propose an innovative algorithm called CR measure-based INC services deployment (CR-D), which is proved to be logarithm-approximate in cost optimization. Extensive simulation results validate the logarithmic approximation of CR-D, and show that it outperforms the benchmarks by an average of 29.42% and 35.77% in cost optimization. •Novel Problem Definition: Introduced the reliable INC-enabled service Deployment (RINC-SD) problem, focusing on the cost-effective and reliable deployment of INC-enabled services.•Mathematical Modeling: Formulated the reliable INC-enabled services deployment (RINC-SD) problem with deployment cost optimization as the objective, proving its NP-hardness.•CR measure Technique: Developed the Cost-Reliability (CR) measure technique to identify physical nodes for flexible and cost-efficient deployment of INC-enabled services.•Extensive Evaluation: Conducted thorough analysis and simulations, showing CR measure’s superior performance, achieving average cost savings of 29.42% and 35.77% over state-of-the-art methods.