Coexisting scheduling policies boosting I/O Virtual Machines

Abstract. Deploying multiple Virtual Machines (VMs) running various types of workloads on current many-core cloud computing infrastructures raises an important issue: The Virtual Machine Monitor (VMM) has to efficiently multiplex VM accesses to the hardware. We argue that altering the scheduling concept can optimize the system’s overall performance. Currently, the Xen VMM achieves near native performance multiplexing VMs with homogeneous workloads. Yet having a mixture of VMs with different types of workloads running concurrently, it leads to poor I/O performance. Taking into account the complexity of the design and implementation of a universal scheduler, let alone the probability of being fruitless, we focus on a system with multiple scheduling policies that coexist and service VMs according to their workload characteristics. Thus, VMs can benefit from various schedulers, either existing or new, that are optimal for each specific case. In this paper, we design a framework that provides three basic coexisting scheduling policies and implement it in the Xen paravirtualized environment. Evaluating our prototype we experience 2.3 times faster I/O service and link saturation, while the CPU-intensive VMs achieve more than 80 % of current performance.

SmartPQ: An Adaptive Concurrent Priority Queue for NUMA Architectures

Concurrent priority queues are widely used in important workloads, such as graph applications and discrete event simulations. However, designing scalable concurrent priority queues for NUMA architectures is challenging. Even though several NUMA-oblivious implementations can scale up to a high number of threads, exploiting the potential parallelism of insert operation, NUMA-oblivious implementations scale poorly in deleteMin-dominated workloads. This is because all threads compete for accessing the same memory locations, i.e., the highest-priority element of the queue, thus incurring excessive cache coherence traffic and non-uniform memory accesses between nodes of a NUMA system. In such scenarios, NUMA-aware implementations are typically used to improve system performance on a NUMA system. In this work, we propose an adaptive priority queue, called SmartPQ. SmartPQ tunes itself by switching between a NUMA-oblivious and a NUMA-aware algorithmic mode to achieve high performance under all various contention scenarios. SmartPQ has two key components. First, it is built on top of NUMA Node Delegation (Nuddle), a generic low-overhead technique to construct efficient NUMA-aware data structures using any arbitrary concurrent NUMA-oblivious implementation as its backbone. Second, SmartPQ integrates a lightweight decision making mechanism to decide when to switch between NUMA-oblivious and NUMA-aware algorithmic modes. Our evaluation shows that, in NUMA systems, SmartPQ performs best in all various contention scenarios with 87.9% success rate, and dynamically adapts between NUMA-aware and NUMA-oblivious algorithmic mode, with negligible performance overheads. SmartPQ improves performance by 1.87x on average over SprayList, the state-of-theart NUMA-oblivious priority queue

Design, Implementation and Evaluation of the SVNAPOT Extension on a RISC-V Processor

The RISC-V SVNAPOT Extension aims to remedy the performance overhead of the Memory Management Unit (MMU), under heavy memory loads. The Privileged Specification defines additional Natural-Power-of-Two (NAPOT) multiples of the 4KB base page size, with 64KB as the default candidate. In this paper we extend the MMU of the Rocket Chip Generator, in order to manage the collocation of 64KB pages along with 4KB pages in the L2 TLB. We present the design challenges we had to overcome and the trade-offs of our design choices. We conduct a preliminary sensitivity analysis of the L2 TLB with different configurations/page sizes. Finally, we summarize on techniques which could further improve memory management performance on RISC-V systems.Comment: Extended abstract accepted to the RISC-V EU Summit 2024 - June 24-28 Munich, German