面向微内核操作系统的低延迟全局动态负载均衡机制

Qinyun Tan, Kun Xiao, Wen He, Pinyuan Lei, Lirong Chen
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引用次数: 0

摘要

随着物联网设备的智能化,对其计算能力提出了更高的要求。多核处理器广泛应用于物联网设备,因为它们在保证低功耗的同时提供更强大的计算能力。因此,需要物联网设备上的操作系统支持并优化多核处理器的调度算法。目前,基于微内核的操作系统,如QNX Neutrino RTOS、HUAWEI Harmony OS等,以其实时性和安全性被广泛应用于物联网设备中。然而,对于微内核操作系统的多核调度,特别是负载均衡机制的研究相对有限。相关研究仍主要集中在传统的单片操作系统上,如Linux。为此,本文提出了一种针对微内核操作系统的低延迟、高性能、高实时性的集中式全局动态多核负载均衡方法。它已经在我们自己的微内核操作系统Mginkgo上实现和测试。测试结果表明,当系统中存在负载不平衡时,可以自动执行负载平衡,使系统中的所有处理器都可以尝试实现最大的吞吐量和资源利用率。并且负载均衡给系统带来的延迟非常低,创建新任务触发的延迟约为4882个周期(约6.164us),定时触发的延迟约为6596个周期(约8.328us)。此外,我们还测试了系统吞吐量和CPU利用率的改进。结果表明,在预设情况下,负载均衡可以使CPU利用率提高20%,而负载均衡占用的CPU利用率低得可以忽略不计,约为0.0082%。
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A Global Dynamic Load Balancing Mechanism with Low Latency for Micokernel Operating System
As Internet of Things(IOT) devices become intelli-gent, more powerful computing capability is required. Multi-core processors are widely used in IoT devices because they provide more powerful computing capability while ensuring low power consumption. Therefore, it requires the operating system on IoT devices to support and optimize the scheduling algorithm for multi-core processors. Nowadays, microkernel-based operating systems, such as QNX Neutrino RTOS and HUAWEI Harmony OS, are widely used in IoT devices because of their real-time and security feature. However, research on multi-core scheduling for microkernel operating systems is relatively limited, especially for load balancing mechanisms. Related research is still mainly focused on the traditional monolithic operating systems, such as Linux. Therefore, this paper proposes a low-latency, high- performance, and high real-time centralized global dynamic multi-core load balancing method for the microkernel operating system. It has been implemented and tested on our own microkernel operating system named Mginkgo. The test results show that when there is load imbalance in the system, load balancing can be performed automatically so that all processors in the system can try to achieve the maximum throughput and resource utilization. And the latency brought by load balancing to the system is very low, about 4882 cycles (about 6.164us) triggered by new task creation and about 6596 cycles (about 8.328us) triggered by timing. In addition, we also tested the improvement of system throughput and CPU utilization. The results show that load balancing can improve the CPU utilization by 20% under the preset case, while the CPU utilization occupied by load balancing is negligibly low, about 0.0082%.
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