TRACON: Interference-Aware Schedulingfor Data-Intensive Applicationsin Virtualized Environments

Chiang, Ron C.; Huang, H. Howie

doi:10.1109/tpds.2013.82

Cited by 57 publications

(69 citation statements)

References 48 publications

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“…Weighted Mean Method [16] also uses Euclidean distances to determine weights of each application according to the Principal Component Analysis (PCA). Some research works [16,26] use the similarity between workload characteristic vectors of applications and account for noise, redundancy, and similarity in applications characteristics by using the principal components of a vector.…”

Section: Comparing the Proposed Methods And The Weighted Mean Methods (mentioning

confidence: 99%

“…TRACON [16] also predicts interference in paravirtualized environments based on the I/O usage of the guest and native driver domains. Interference prediction based on three models, Weighted Mean Method (WMM), the Linear Model (LM), and the Nonlinear Model (NLM) is applied and the application runtime and I/O throughput of the various applications are compared, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…According to research [16,19,20], we consider interference as the change in the relative total execution time of an application due to the execution of concurrently running applications. We propose a two-phase interference-aware scheduling algorithm based on application clustering analysis in this section.…”

Section: Interference-aware Scheduling Based On Clustering Analysismentioning

confidence: 99%

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Mitigating Interference between Scientific Applications in OS-Level Virtualized Environments

Adufu

Kim

2018

Scientific Programming

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Recent research and production environments are deploying more container technologies for the execution of HPC applications and for reproducing scientific workflows or computing environments. Research works, however, have not accounted for performance interference when executing corunning applications in containers though the absence of an efficient performance isolation layer cannot guarantee the absence of performance interference among multiple corunning applications which share resources. In this research, we propose an interference-aware scheduling method that mitigates the problem of performance interference based on applications' I/O and CPU usage profiles. The proposed method estimates the amount of interference between various pairs of applications and coschedules them based on estimated interference. We evaluate the proposed method for both Bag-of-Tasks (BOT) scientific applications and scientific workflows and compare our method to the Weighted Mean Method. Our method improves the performance of the target scientific application by coscheduling applications with the least estimated interference ratios.

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Section: Comparing the Proposed Methods And The Weighted Mean Methods (mentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Interference-aware Scheduling Based On Clustering Analysismentioning

confidence: 99%

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Mitigating Interference between Scientific Applications in OS-Level Virtualized Environments

Adufu

Kim

2018

Scientific Programming

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“…While most live migration efforts concentrate on how to transfer the memory from source to destination during the migration process, comparatively little attention has been devoted to the transfer of storage. This problem is gaining increasing importance: due to performance reasons, VMs that run large-scale, data-intensive applications (Chiang and Huang, 2014) tend to rely on local storage, which poses a difficult challenge on live migration: it needs to handle storage transfer in addition to memory transfer. Furthermore, local storage presents availability, performance, security (AlZain et al, 2012;Goettelmann et al, 2014), and privacy advantages, and is used in practice in a variety of cases.…”

Section: Literature Surveymentioning

confidence: 99%

Live migration of virtual machines with their local persistent storage in a data intensive cloud

Modi

Achar

Thilagam

2017

IJHPCN

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Abstract:Processing large volumes of data to drive their core business has been the primary objective of many firms and scientific applications in these days. Cloud computing being a large-scale distributed computing paradigm can be used to cater for the needs of data intensive applications. There are various approaches for managing the workload on a data intensive cloud. Live migration of a virtual machine is the most prominent paradigm. Existing approaches to live migration use network attached storage where just the run time state needs to be transferred. Live migration of virtual machines with local persistent storage has been shown to have performance advantages like security, availability and privacy. This paper presents an optimised approach for migration of a virtual machine along with its local storage by considering the locality of storage access. Count map combined with a restricted block transfer mechanism is used to minimise the downtime and overhead. The solution proposed is tested by various parameters like bandwidth, write access patterns and threshold. Results show the improvement in downtime and reduction in overhead.

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“…Chiang and Huang (2011) proposed TRACON, using modeling and control theory and machine learning techniques. Pu et al (2010) present an analysis of performance interference in virtualized environments with a focus on contentions in input-output storage device usage.…”

Section: Jcsmentioning

confidence: 99%

Energy Efficient and Interference Aware Provisioning in Virtualized Server Cluster Environment

Kumar¹,

Raghunathan²

2014

Journal of Computer Science

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IT service providers, employ server virtualization as a main building block to improve system utilization, improve system manageability, reduce operational costs which includes energy consumption driving economies of scale with shared resources. Virtualization enables co-locating and efficient assignments of virtual servers within the bounds of limited number of heterogeneous physical servers, with Virtual Machines (VM) sharing the limited physical server resources between them. Though virtualization technologies point to the fact that each virtual server has its very own isolated environment, but in reality, perfect isolation is not possible. Primary measure to achieve assignment efficiency is to ensure that system resources are utilized effectively and performance of VM (and application workloads) is consistent within the desired bounds. Interference or contention on the limited shared resources among VMs leads to performance degradation and is referred to as performance interference. This affects (a) application Quality of Service (QOS) and (b) server cluster or data centers' energy-efficiency. In this work, we analyze the performance degradation using (a) energy efficiency heterogeneity measure and (b) interference aware measure, with the aim to reduce energy consumption in our environment. Experimental results on different scenarios with our energy efficiency and interference aware approach shows a reduction in energy consumption to the tune of 8 to 58% and 10× improvement in per request average response time in contrast to a default energy efficiency and interference oblivious approach.

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TRACON: Interference-Aware Schedulingfor Data-Intensive Applicationsin Virtualized Environments

Cited by 57 publications

References 48 publications

Mitigating Interference between Scientific Applications in OS-Level Virtualized Environments

Mitigating Interference between Scientific Applications in OS-Level Virtualized Environments

Live migration of virtual machines with their local persistent storage in a data intensive cloud

Energy Efficient and Interference Aware Provisioning in Virtualized Server Cluster Environment

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