Even with substantial endeavors to test and validate processors, computational errors may still arise post-installation. One particular category of CPU errors transpires discreetly, without crashing applications or triggering hardware warnings. These elusive errors pose a significant threat by undermining user data, and their detection is challenging. This paper introduces Orthrus, a solution for the timely detection of silent user data corruption caused by post-installation CPU errors. Orthrus safeguards user data in cloud applications by providing simple annotations and compiler support for users to identify data operators and validating these operators asynchronously across cores while maintaining a low overhead (2%–6%), making it practical for production deployment. Our evaluation, using carefully injected errors, demonstrates that Orthrus can detect 87% of data corruptions with just a single core dedicated to validation, increasing to 91% and 96% when two and four cores are used, respectively.

Grant-free random access (GFRA) becomes attractive in Internet of Things (IoT) due to its low signaling overhead and short access latency. In this article, we investigate GFRA in a multicel massive multiple-input-multiple-output (MIMO) system. As the IoT device usually has sporadic traffic, we set a packet buffer for each device to describe its temporal traffic, and also use the stochastic geometry to describe the randomness of devices’ spatial locations. With the backoff mechanism, only devices with a nonempty buffer and a successful backoff are activated and allowed to request access. Unlike previous works that regard all devices selecting the same pilot (i.e., the colliding devices) as undetectable, we give a more accurate model that the base station (BS) can detect colliding devices when they locate far away from each other, and we set a unique collision area for each device to quantify the boundary that the collision can be ignored. A tight approximation for the number of packets successfully transmitted at the unit area and time slot, named packet throughput, is derived. Based on it, we obtain the optimal backoff parameter that maximizes the packet throughput under devices’ delay constraints. It is shown that when pilots are insufficient or device packet traffic is heavy, a long backoff time is needed. However, as the pilot grows or the packet traffic turns light, devices should gradually reduce the backoff time. In particular, if pilots are surplus, cheap detectors can be equipped on the BS without an obvious packet throughput reduction.

Grant-free random access (GFRA) becomes attractive in Internet-of-Things (IoT) due to its low signaling overhead. In this paper, we investigate the GFRA in a multicell massive multiple-input multiple-output (MIMO) system after considering both the spatial and temporal traffic of devices. By introducing the backoff mechanism, only devices with non-empty buffer and a successful backoff can request access. Unlike previous works on GFRA that regard all devices selecting the same pilot as undetectable, we set a unique collision area for each device to quantify the boundary that BS can detect the collision. With tools of stochastic geometry and queueing theory, we derive a tight approximation for the number of packets successfully transmitted at unit area and time slot, named as packet throughput (Tp). Based on it, we find that the range of the collision area has a remarkable effect on Tp. The optimal backoff parameter that maximizes Tpis also obtained, and we find that a long backoff time is needed when the pilot is insufficient or the packet traffic is heavy. Compared with the fully-loaded access, our optimal backoff mechanism can significantly improve Tp, especially for the system with crowded devices.