Scheduling optimization in multicore multithreaded microprocessors through dynamic modeling

Weng, Lichen; Liu, Chen; Gaudiot, Jean‐Luc

doi:10.1145/2482767.2482774

Cited by 9 publications

(8 citation statements)

References 22 publications

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“…Author explored the service policies and defined a service driven mechanism to improve the cloud service distribution in public environment with private constraints integration. J. McDermott [2] presented an application effective cloud environment for military application. Author defined a work on infrastructure driven analysis so that the processing in virtual environment will be done.…”

Section: IImentioning

confidence: 99%

A Study on Various Security Aspects in Cloud Policy Oriented Architecture

Sonia¹,

Bhatia²

2017

IJAERS

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Section: IImentioning

confidence: 99%

A Study on Various Security Aspects in Cloud Policy Oriented Architecture

Sonia¹,

Bhatia²

2017

IJAERS

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“…Thread Assignment: Several studies show that the performance of applications running on multithreaded processors depends on the interference in hardware resources, which, in turn, depends on thread assignment [4][18] [45][55] [59]. El-Moursy el al.…”

Section: Related Workmentioning

confidence: 99%

Thread Assignment in Multicore/Multithreaded Processors: A Statistical Approach

Radojković

Carpenter

Moretó

et al. 2016

IEEE Trans. Comput.

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Abstract-The introduction of multicore/multithreaded processors, comprised of a large number of hardware contexts (virtual CPUs) that share resources at multiple levels, has made process scheduling, in particular assignment of running threads to available hardware contexts, an important aspect of system performance. Nevertheless, thread assignment of applications running on state-of-the art processors is an NP-complete problem. Over the years, numerous studies have proposed heuristic-based algorithms for thread assignment. Since the thread assignment problem is intractable, it is in general impossible to know the performance of the optimal assignment, so the room for improvement of a given algorithm is also unknown. It is therefore hard to decide whether to invest more effort and time to improve an algorithm that may already be close to optimal. In this paper, we present a statistical approach to the thread assignment problem. First, we present a method that predicts the performance of the optimal thread assignment, based on the observed performance of each thread assignment in a random sample. The method is based on Extreme Value Theory (EVT), a branch of statistics that analyses extreme deviations from the population mean. We also propose sample pruning, a method that significantly reduces the time required to apply the statistical method by reducing the number of candidate solutions that need to be measured. Finally, we show that, if no suitable heuristicbased algorithm is available, a sample of several thousand random thread assignments is enough to obtain, with high confidence, an assignment with performance close to optimal. The presented approach is architecture and application independent, and it can be used to address the thread assignment problem in various domains. It is especially well suited for systems in which the workload seldom changes. An example is network systems, which typically provide a constant set of services that are known in advance, with network applications performing a similar processing algorithm for each packet in the system. In this paper, we validate our methods with an industrial case study for a set of multithreaded network applications on an UltraSPARC T2 processor. This article is an extension of our previous work [44], which was published in

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“…Weng and Liu (2010) and Weng, Liu, and Gaudiot (2013) introduced a scheme that aimed to support a dynamic, adaptive and scalable operating system scheduling policy for MMMP. They proposed architectural (hardware) strategies to construct linear models to capture workload behaviors and then schedule threads according to their resource demands.…”

Section: Related Workmentioning

confidence: 99%

“…Our work integrates several aspects mentioned from previous researches and applies them to the cloud environment. We follow the approach to schedule VMs ac-cording their different resource demand in order to mitigate the contention on shared processor resources between concurrent VMs (Weng et al, 2013;Arteaga et al, 2010). One significant aspect of our approach is that it acts online at runtime, which does not require a priori information about the VMs.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Virtual Machine Management in the Cloud

Torres

Liu

2014

International Journal of Systems and Service-Oriented Engineering

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The success of cloud computing technologies heavily depends on both the underlying hardware and system software support for virtualization. In this study, we propose to elevate the capability of the hypervisor to monitor and manage co-running virtual machines (VMs) by capturing their dynamic behavior at runtime and adaptively schedule and migrate VMs across cores to minimize contention on system resources hence maximize the system throughput. Implemented at the hypervisor level, our proposed scheme does not require any changes or adjustments to the VMs themselves or the applications running inside them, and minimal changes to the host OS. It also does not require any changes to existing hardware structures. These facts reduce the complexity of our approach and improve portability at the same time. The main intuition behind our approach is that because the host OS schedules entire virtual machines, it loses sight of the processes and threads that are running within the VMs; it only sees the averaged resource demands from the past time slice. In our design, we sought to recreate some of this low level information by using performance counters and simple virtual machine introspection techniques. We implemented an initial prototype on the Kernel Virtual Machine (KVM) and our experimental results show the presented approach is of great potential to improve the overall system throughput in the Cloud environment.

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Scheduling optimization in multicore multithreaded microprocessors through dynamic modeling

Cited by 9 publications

References 22 publications

A Study on Various Security Aspects in Cloud Policy Oriented Architecture

A Study on Various Security Aspects in Cloud Policy Oriented Architecture

Thread Assignment in Multicore/Multithreaded Processors: A Statistical Approach

Adaptive Virtual Machine Management in the Cloud

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