Aash

Abstract

Asymmetric multicore processors (AMP) consist of cores exposing the same instruction-set architecture (ISA) but varying in size, frequency, power consumption and performance. AMPs were shown to be more power efficient than conventional symmetric multicore processors, and it is therefore likely that future multicore systems will include cores of different types. AMPs derive their efficiency from core specialization: instruction streams can be assigned to run on the cores best suited to their demands for architectural resources. System efficiency is improved as a result. To perform effective matching of threads to cores, the thread scheduler must be asymmetry-aware; and while asymmetry-aware schedulers for operating systems are a well studied topic, asymmetry-awareness in hypervisors has not been addressed. A hypervisor must be asymmetry-aware to enable proper functioning of asymmetry-aware guest operating systems; otherwise they will be ineffective in virtual environments. Furthermore, a hypervisor must ensure that asymmetric cores are shared among multiple guests in a fair fashion or in accordance with their priorities. This work for the first time implements simple changes to the hypervisor scheduler, required to make it asymmetry-aware, and evaluates the benefits and overheads of these asymmetry-aware mechanisms. Our evaluation was performed using an open source hypervisor Xen on a real multicore system where asymmetry was emulated via CPU frequency scaling. We compared the asymmetry-aware hypervisor to default Xen. Our results indicate that asymmetry support can be implemented with low overheads, and resulting performance improvements can be significant, reaching up to 36% in our experiments. Most performance improvements are derived from the fact that an asymmetry-aware hypervisor ensures that the fast cores do not go idle before slow cores and from the fact that it maps virtual cores to physical cores for asymmetry-aware guests according to the guest's expectations. Other benefits from asymmetry awareness are fairer sharing of computing resources among VMs and more stable execution times.

OctoFAS: A Two-Level Fair Scheduler That Increases Fairness in Network-Based Key-Value Storage

“…Load Balancing and Fairness Enhancement: Several strategies such as thread or data migration have been explored to balance CPU loads with NUMA architectures [40][41][42][43]. These approaches primarily address differences in memory access times incurred by interconnects between processors, e.g., NThread [40] and AASH [42] proposed thread migration as a means of avoiding contention in processor interconnects. There have been efforts directed toward load balancing in storage systems [44][45][46][47][48].…”

Dealing with Performance Unpredictability in an Asymmetric Multicore Processor Cloud

“…Hardware. The adopted experimental environment is similar to those used in prior work [15,26] in the domain of AMP. In those works, an AMP machine consists of two core types namely fast and slow cores.…”

“…Based on this, a thread that achieves only modest performance improvements from running on a fast core is executed on a slow core, and a thread that benefits significantly from running on a fast core is executed on the fast core. Researches conducted in virtualized systems [15,18,19,26] consist in translating native system solutions in virtualized systems (vCPUs are seen as threads). For instance, [15] proposes to realize a fair sharing of fast cores on AMP machines.…”

“…Researches conducted in virtualized systems [15,18,19,26] consist in translating native system solutions in virtualized systems (vCPUs are seen as threads). For instance, [15] proposes to realize a fair sharing of fast cores on AMP machines. They present a scheduling technique for hypervisors implemented in Xen.…”