Combining Locality Analysis with Online Proactive Job Co-scheduling in Chip Multiprocessors

Jiang, Yunlian; Tian, Kai; Shen, Xipeng

doi:10.1007/978-3-642-11515-8_16

Cited by 49 publications

(35 citation statements)

References 28 publications

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“…System software also does not have control over hardware resources such as caches and memory bandwidth, which renders responding to contention and managing applications' QoS quite challenging. As a result, despite the amount of research attention given to contention problems on multicore platforms [28,45,10,19,44,51,52,27,32,15,29,24,63,23,24,7,4,60], mitigating the impact of contention on an application's performance and QoS, enforcing the relative QoS priorities of co-running applications, while maximizing machine utilization, remains key challenges in modern warehouse scale computers.…”

Section: Mitigating Contentionmentioning

confidence: 99%

“…[Contention Aware Scheduling] A research direction that recently attracted a wealth of attention is contention aware scheduling [15,29,24,63,23,24,7,4,60]. Contention aware scheduling techniques use predictors or models to decide what applications should be co-running together to minimize the performance degradation or to improve performance isolation.…”

Section: Software Runtime and Os Approaches To Mitigating Contentionmentioning

confidence: 99%

“…Approaches that use prediction models are also proposed. Jiang et al [24] estimate an application's contentiousness by estimating the extra cache misses caused by co-location based on the application's reuse distance profile. This dissertation presents a more accurate prediction model than the state-of-the-art approaches.…”

Section: Software Runtime and Os Approaches To Mitigating Contentionmentioning

confidence: 99%

“…An application can be 1) contentious and sensitive; 2) not contentious and insensitive; 3) contentious but not sensitive; and 4) not contentious but sensitive. Among these four outcomes, Jiang et al [24,35] conclude that typical applications' contentiousness and sensitivity are strongly correlated and should be classified as either contentious and sensitive, or not contentious and insensitive. Xie et al [59] on the other hand, argue the existence of applications that are not contentious but sensitive.…”

Section: Contentiousness and Sensitivitymentioning

confidence: 99%

“…As demonstrated in previous work [24], an application A's sensitivity is formally defined using the following formula,…”

Section: Contentiousness and Sensitivitymentioning

confidence: 99%

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Mitigating Memory Resource Contention in Warehouse Scale Computers

Tang¹

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The class of modern datacenters hosting large-scale Internet services such as web-search, mail, and social networking has gained significant momentum in today's computing environments. However, these datacenters, recently coined as warehouse scale computers (WSCs), are extremely expensive to construct and operate. Improving software performance and server utilization is key to improving the efficiency and reducing the enormous cost in WSCs.Modern WSCs are constructed using commodity multicore processors, on which part of the memory subsystem is shared. When multiple applications are co-located on a multicore machine, contention for the shared memory resources, such as caches and memory bandwidth, may occur. This contention can cause severe cross-core performance interference and significantly degrade application performance. Mitigating resource contention is critical for improving application performance. However, despite the wealth of research effort on contention management, little is known about how emerging large-scale web-service applications interact with the shared memory resources on commodity processors and how this contention can be mitigated to improve the performance of these applications.In addition to performance, mitigating contention is also critical for improving the server utilization in WSCs. As multicore processors with expanding core counts continue to dominate the server market, the overall utilization of WSCs depends heavily on the consolidation of workloads to take advantage of the total computing potential provided by modern processors. However, many of the applications running in WSCs are user-facing, latency-sensitive applications with quality of service (QoS) requirements. These QoS requirements can be violated by the performance interference that can occur when multiple applications are consolidated on a single machine. As a result, the current common practice in WSCs is iii to disallow the co-location of latency-sensitive applications with other applications. This approach is undesirable as it results in low machine utilization in WSCs and millions of dollars wasted.This dissertation presents novel compilation and runtime approaches to significantly mitigate contention and thus improve performance, QoS and machine utilization in datacenters. Specifically, the main contributions of this dissertation include: 1) comprehensive investigation and characterization of the impact of memory resource sharing on industrystrength large-scale datacenter workloads, which expose new characteristics and insights contrary to recent literature; 2) the design of a heuristic based system and a runtime system to intelligently map application threads to cores to promote positive resource sharing and mitigate resource contention to improve application performance; and 3) the design of novel compilation techniques and runtime systems that statically and dynamically manipulate applications' contentious nature to enable the co-location of applications with varying QoS requirements and as a result, gr...

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