Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?
This addresses resource management inefficiencies in cloud environments for service providers, though it is incremental as it builds on existing ML scheduling approaches.
The paper tackles resource scheduling for co-located cloud services by proposing OSML+, an ML-based mechanism that intelligently schedules cache, memory bandwidth, and computing core resources simultaneously, resulting in higher loads, meeting QoS targets with lower overheads, and faster convergence than previous studies.
Making it intelligent is a promising way in System/OS design. This paper proposes OSML+, a new ML-based resource scheduling mechanism for co-located cloud services. OSML+ intelligently schedules the cache and main memory bandwidth resources at the memory hierarchy and the computing core resources simultaneously. OSML+ uses a multi-model collaborative learning approach during its scheduling and thus can handle complicated cases, e.g., avoiding resource cliffs, sharing resources among applications, enabling different scheduling policies for applications with different priorities, etc. OSML+ can converge faster using ML models than previous studies. Moreover, OSML+ can automatically learn on the fly and handle dynamically changing workloads accordingly. Using transfer learning technologies, we show our design can work well across various cloud servers, including the latest off-the-shelf large-scale servers. Our experimental results show that OSML+ supports higher loads and meets QoS targets with lower overheads than previous studies.