Benefiting from the low-latency RDMA network, far memory techniques that enable applications to leverage memory on remote machines have been proposed to improve job performance and resource utilization in datacenters. In a far memory-equipped datacenter, applications exhibit diverse characteristics when using far memory. However, existing datacenter schedulers are oblivious to the presence of far memory, resulting in suboptimal scheduling decisions that can compromise the performance guarantee of tasks. To this end, we present FaMAS, a far memory-aware datacenter scheduler that exploits the benefits of far memory to improve datacenter efficiency and avoid severe performance degradation of tasks. FaMAS tackles the far-memory scheduling problem for two objectives: reducing average job completion time and meeting deadlines. FaMAS introduces two novel far memory-aware algorithms: Job Completion Time First (JCTF) and Service Level Objective First (SLOF), tailored to the above two objectives respectively. JCTF formalizes the scheduling target with insights from the trade-off between the performance and the resource usage of individual tasks, while SLOF incorporates the growth rate of resource usage to represent tasks. The experimental results show that JCTF improves the average job completion time by up to$1.81\times $and SLOF achieves up to$3.95\times $lower deadline violations over the best traditional algorithm.

SoC-Cluster, a novel server architecture composed of massive mobile system-on-chips (SoCs), is gaining popularity in industrial edge computing due to its energy efficiency and compatibility with existing mobile applications. However, we observe that the deployed SoC-Cluster servers are not fully utilized, because the hosted workloads are mostly user-triggered and have significant tidal phenomena. To harvest the free cycles, we propose to co-locate deep learning tasks on them.

In the realm of industrial edge computing, a novel server architecture known as SoC-Cluster, characterized by its aggregation of numerous mobile systems-on-chips (SoCs), has emerged as a promising solution owing to its enhanced energy efficiency and seamless integration with prevalent mobile applications. Despite its advantages, the utilization of SoC-Cluster servers remains unsatisfactory, primarily attributed to the tidal patterns of user-initiated workloads. To address such inefficiency, we introduceSoCFlow+, a pioneering framework designed to facilitate the co-location of deep learning training tasks on SoC-Cluster servers, thereby optimizing resource utilization.SoCFlow+incorporates three novel techniques tailored to mitigate the inherent limitations of commercial SoC-Cluster servers. First, it employs group-wise parallelism complemented by delayed aggregation, a strategy engineered to enhance the training efficiency and scalability of deep learning models, effectively circumventing network bottlenecks. Second, it integrates a data-parallel mixed-precision training algorithm, optimized to exploit the heterogeneous processing capabilities inherent to mobile SoCs fully. Third,SoCFlow+employs an underclocking-aware workload re-balanacing mechanism to tackle the training performance degradation caused by the thermal control of mobile SoCs. Through rigorous experimental validation,SoCFlow+achieves a convergence speedup ranging from 1.6× to 740× across 32 SoCs, compared to conventional benchmarks. Furthermore, when juxtaposed with commodity GPU servers (e.g., NVIDIA V100) under identical power constraints,SoCFlow+not only exhibits comparable training speed but also achieves a remarkable reduction in energy consumption by a factor of 2.31× to 10.23×, all while preserving convergence accuracy.