Evaluation of Network Topology Inference in Opaque Compute Clouds through End-to-End Measurements

Battré, Dominic; Frejnik, Natalia; Goel, Siddhant; Kao, Odej; Warneke, Daniel

doi:10.1109/cloud.2011.30

Cited by 21 publications

(13 citation statements)

References 21 publications

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“…We chose to use the latency as the basis for the VM selection algorithms because RTTs are a reliable metric for measuring network distance within clouds, as recent research has pointed out [3], [5], [8]- [10]. Additionally, RTTs are computationally cheap and straightforward to use and obtain.…”

Section: A Latency Matrixmentioning

confidence: 99%

End-to-end informed VM selection in compute clouds

Teixeira

Bestavros

2015

2015 IEEE International Conference on Communication Workshop (ICCW)

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Abstract-The selection of resources, particularly VMs, in current public IaaS clouds is usually done in a blind fashion, as cloud users do not have much information about resource consumption by co-tenant third-party tasks. In particular, communication patterns can play a significant part in cloud application performance and responsiveness, specially in the case of novel latencysensitive applications, increasingly common in today's clouds. Thus, herein we propose an end-to-end approach to the VM allocation problem using policies based uniquely on round-trip time measurements between VMs. Those become part of a userlevel 'Recommender Service' that receives VM allocation requests with certain network-related demands and matches them to a suitable subset of VMs available to the user within the cloud. We propose and implement end-to-end algorithms for VM selection that cover desirable profiles of communications between VMs in distributed applications in a cloud setting, such as profiles with prevailing pair-wise, hub-and-spokes, or clustered communication patterns between constituent VMs. We quantify the expected benefits from deploying our Recommender Service by comparing our informed VM allocation approaches to conventional, random allocation methods, based on real measurements of latencies between Amazon EC2 instances. We also show that our approach is completely independent from cloud architecture details, is adaptable to different types of applications and workloads, and is lightweight and transparent to cloud providers.

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Section: A Latency Matrixmentioning

confidence: 99%

End-to-end informed VM selection in compute clouds

Teixeira

Bestavros

2015

2015 IEEE International Conference on Communication Workshop (ICCW)

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“…Previous work to explore a public cloud network has been conducted by Battre et al [15]. Their work investigates methods to infer the network topology within opaque cloud infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Network Health and e-Science in Public Clouds

Chard

Bubendorfer

2014

2014 IEEE 10th International Conference on E-Science

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Abstract-Commercial cloud providers are increasingly offering high performance and GPU-enabled resources capable of facilitating e-Science applications. However, the limitations of a public cloud's internal network performance are well documented and can lead to the decision to use dedicated infrastructure over cloud resources for scientific applications. This paper explores the potential for improvement in the performance of eScience applications on public clouds through the examination of the network in more detail. We introduce health indicators and evaluate various tomographic techniques for their ability to infer information regarding the network connection between instances. We also propose and formulate a set of health markers and health metrics to efficiently assess the network over time in order to make informed deployment decisions. Finally, we evaluate our work through a real-world medical image reconstruction application.

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“…Approaches such as Sequoia [53] deduce underlying network topology by mapping application nodes onto leaves of a virtual tree. Unfortunately, even though these inference approaches work well for Internet-level topologies, state-of-the-art methods cannot infer public cloud environments accurately [10,16].…”

Section: Cost Functionsmentioning

confidence: 99%

ClouDiA: a deployment advisor for public clouds

et al. 2015

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An increasing number of distributed data-driven applications are moving into shared public clouds. By sharing resources and operating at scale, public clouds promise higher utilization and lower costs than private clusters. To achieve high utilization, however, cloud providers inevitably allocate virtual machine instances non-contiguously; i.e., instances of a given application may end-up in physically distant machines in the cloud. This allocation strategy can lead to large differences in average latency between instances. For a large class of applications, this difference can result in significant performance degradation, unless care is taken in how application components are mapped to instances. In this paper, we propose ClouDiA, a general deployment advisor that selects application node deployments minimizing either (i) the largest latency between application nodes, or (ii) the longest critical path among all application nodes. ClouDiA employs a number of algorithmic techniques, including mixed-integer programming and constraint programming techniques, to efficiently search the space of possible mappings of application nodes to instances. Through experiments with synthetic and real applications in Amazon EC2, we show that mean latency is a robust metric to model

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Evaluation of Network Topology Inference in Opaque Compute Clouds through End-to-End Measurements

Cited by 21 publications

References 21 publications

End-to-end informed VM selection in compute clouds

End-to-end informed VM selection in compute clouds

Network Health and e-Science in Public Clouds

ClouDiA: a deployment advisor for public clouds

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