PhilKyue Shin - Academia.edu (original) (raw)
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Papers by PhilKyue Shin
2018 31st IEEE International System-on-Chip Conference (SOCC)
With the advent of high-performance multicore processors that operate under a limited power budge... more With the advent of high-performance multicore processors that operate under a limited power budget, dedicated low-end microprocessors with different levels of criticality are rapidly consolidated into a mixed-criticality system. One of the major challenges in designing such a mixed-criticality system is to tightly control the amount of resource contention for a critical application by effectively limiting its performance interference incurred due to sharing resources with non-critical tasks. In this paper, we propose application-aware dynamic memory request throttling to reduce the memory interference latency of a critical application in a dual criticality system. Our approach carefully differentiates critical task instances from normal task instances and groups them into the critical and normal cgroup, respectively. It then predicts the occurrence of excessive memory contention under critical task execution and then throttles memory requests generated by the normal cgroup via the CPUFreq governor when necessary. We have implemented the approach on the NVIDIA Jetson TX2 with Linux kernel 4.4.38. Experimental results show that the proposed approach reduces the end-to-end latency of a critical application up to 9.49% while incurring only negligible overhead.
IEEE Access
Performance interference between QoS and best-effort applications is getting more aggravated as d... more Performance interference between QoS and best-effort applications is getting more aggravated as data-intensive applications are rapidly and widely spreading in recently emerging computing systems. While the completely fair scheduler (CFS) of the Linux kernel has been extensively used to support performance isolation in a multitasking environment, it falls short of addressing memory-related interference due to memory access contention and insufficient cache coverage. Though quite a few memory-aware performance isolation mechanisms have been proposed in the literature, many of them rely on hardware-based solutions, inflexible resource management or ineffective execution throttling, which makes it difficult for them to be used in widely deployed operating systems like Linux running on a COTS SoC platform. We propose a memory-aware fair-share scheduling algorithm that can make QoS applications less susceptible to memory-related interference from other co-running applications. Our algorithm carefully separates the genuine memory-related stall from a running task's CPU cycles and compensates the task for the memory-related interference so that the task gets the desired share of CPU before it is too late. The proposed approach is adaptive, effective and efficient in the sense that it does not rely on any static allocation or partitioning of memory hardware resources and improves the performance of QoS applications with only a negligible runtime overhead. Moreover, it is a software-only solution that can be easily integrated into the kernel scheduler with only minimal modification to the kernel. We implement our algorithm into the CFS of Linux and name the end result mCFS. We show the utility and effectiveness of the approach via extensive experiments. INDEX TERMS Memory-related interference, backend stall cycle, operating system, Linux, CFS.
2018 31st IEEE International System-on-Chip Conference (SOCC)
With the advent of high-performance multicore processors that operate under a limited power budge... more With the advent of high-performance multicore processors that operate under a limited power budget, dedicated low-end microprocessors with different levels of criticality are rapidly consolidated into a mixed-criticality system. One of the major challenges in designing such a mixed-criticality system is to tightly control the amount of resource contention for a critical application by effectively limiting its performance interference incurred due to sharing resources with non-critical tasks. In this paper, we propose application-aware dynamic memory request throttling to reduce the memory interference latency of a critical application in a dual criticality system. Our approach carefully differentiates critical task instances from normal task instances and groups them into the critical and normal cgroup, respectively. It then predicts the occurrence of excessive memory contention under critical task execution and then throttles memory requests generated by the normal cgroup via the CPUFreq governor when necessary. We have implemented the approach on the NVIDIA Jetson TX2 with Linux kernel 4.4.38. Experimental results show that the proposed approach reduces the end-to-end latency of a critical application up to 9.49% while incurring only negligible overhead.
IEEE Access
Performance interference between QoS and best-effort applications is getting more aggravated as d... more Performance interference between QoS and best-effort applications is getting more aggravated as data-intensive applications are rapidly and widely spreading in recently emerging computing systems. While the completely fair scheduler (CFS) of the Linux kernel has been extensively used to support performance isolation in a multitasking environment, it falls short of addressing memory-related interference due to memory access contention and insufficient cache coverage. Though quite a few memory-aware performance isolation mechanisms have been proposed in the literature, many of them rely on hardware-based solutions, inflexible resource management or ineffective execution throttling, which makes it difficult for them to be used in widely deployed operating systems like Linux running on a COTS SoC platform. We propose a memory-aware fair-share scheduling algorithm that can make QoS applications less susceptible to memory-related interference from other co-running applications. Our algorithm carefully separates the genuine memory-related stall from a running task's CPU cycles and compensates the task for the memory-related interference so that the task gets the desired share of CPU before it is too late. The proposed approach is adaptive, effective and efficient in the sense that it does not rely on any static allocation or partitioning of memory hardware resources and improves the performance of QoS applications with only a negligible runtime overhead. Moreover, it is a software-only solution that can be easily integrated into the kernel scheduler with only minimal modification to the kernel. We implement our algorithm into the CFS of Linux and name the end result mCFS. We show the utility and effectiveness of the approach via extensive experiments. INDEX TERMS Memory-related interference, backend stall cycle, operating system, Linux, CFS.