Ontology-Based Semantic Priority Scheduling for Multi-domain Active Measurements

Calyam, Prasad; Kumarasamy, Lakshmi; Lee, Chang-Gun; Özgüner, F.

doi:10.1007/s10922-013-9297-x

Cited by 8 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For these purposes, the GENI Rack was configured with three virtual machines provisioned using the ESXi hypervisor. Last but not the least, the entire application and network measurement data collection and aggregation as well as visualization are performed with the Narada Metrics software [1] that we have previously developed as a end-to-end measurement framework.…”

Section: Fig 2: Ptaas Interface Architecture and Componentsmentioning

confidence: 99%

“…Both active measurements (i.e., end-to-end TCP throughput, roundtrip time (RTT) delay, jitter and packet loss) and passive measurements (i.e., transfer rate on uplink of the local interface, download rate at remote interface) were collected using the Narada Metrics measurement framework. We scheduled conflict-free active measurement tests with Iperf and Ping tools using the Narada Metrics active measurement scheduler [1]. Iperf tool tests that are computation and channel intensive are performed offline in a periodic sampling manner with inter-sample times of 30 minutes to 1 hour, before the use of the PTaaS application on a network path.…”

Section: Fig 2: Ptaas Interface Architecture and Componentsmentioning

confidence: 99%

“…We utilize the openly available Kinect Application Programming Interface (API), C# language and Windows Presentation Foundation (WPF) technologies on .Net Framework 4.5 in our interactive interface development environment. The service hardware components in addition to the Kinect sensors include measurement point appliances for application and network monitoring services provided by Narada Metrics [1]. We also use a cloud-hosted storage environment provided by Global Environment for Network Innovations [2] for synchronous analytics of the data streams in the PTaaS session.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synchronous Big Data analytics for personalized and remote physical therapy

Calyam

Mishra

Antequera

et al. 2016

Pervasive and Mobile Computing

Self Cite

View full text Add to dashboard Cite

With gigabit networking becoming economically feasible and widely installed at homes, there are new opportunities to revisit in-home, personalized telehealth services. In this paper, we describe a novel telehealth eldercare service that we developed viz., "PhysicalTherapy-as-a-Service" (PTaaS) that connects a remote physical therapist at a clinic to a senior at home. The service leverages a high-speed, low-latency network connection through an interactive interface built on top of Microsoft Kinect motion sensing capabilities. The interface that is built using user-centered design principles for wellness coaching exercises is essentially a 'Synchronous Big Data' application due to its: (i) high data-in-motion velocity (i.e., peak data rate is ≈400 Mbps), (ii) considerable variety (i.e., measurements include 3D sensing, network health, user opinion surveys and video clips of RGB, skeletal and depth data), and (iii) large volume (i.e., several GB of measurement data for a simple exercise activity). The successful PTaaS delivery through this interface is dependent on the veracity analytics needed for correlation of the real-time Big Data streams within a session, in order to assess exercise balance of the senior without any bias due to network quality effects. Our experiments with PTaaS in an actual testbed involving senior homes in Kansas City with Google Fiber connections and our university clinic demonstrate the network configuration and time synchronization related challenges in order to perform online analytics. Our findings provide insights on how to: (a) enable suitable resource calibration and perform network troubleshooting for high user experience for both the therapist and the senior, and (b) realize a Big Data architecture for PTaaS and other similar personalized healthcare services to be remotely delivered at a large-scale in a reliable, secure and cost-effective manner.

show abstract

Section: Fig 2: Ptaas Interface Architecture and Componentsmentioning

confidence: 99%

Section: Fig 2: Ptaas Interface Architecture and Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synchronous Big Data analytics for personalized and remote physical therapy

Calyam

Mishra

Antequera

et al. 2016

Pervasive and Mobile Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to ensure consistent throughput, perfSONAR is used to create a persistent baseline of network performance across all segments of the paths traversed while exchanging this data. Prasad Calyam, et al in [91], [92] present an ontology-based semantic priority scheduling algorithm for active measurements. The algorithm uses an inference engine to dynamically prioritise measurement requests, mitigate oversampling under high loads and is conflictfree.…”

Section: B Perfsonarmentioning

confidence: 99%

“…[86], [87], [88], [89], [90], [91], [92], [93], [94], [14], [96], [97], [98], [100], [103] This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”

Section: Smartprobe Mda-traceroutementioning

confidence: 99%

A Survey on Internet Performance Measurement Platforms and Related Standardization Efforts

Bajpai

Schönwälder

2015

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

A number of Internet measurement platforms have emerged in the last few years. These platforms have deployed thousands of probes at strategic locations within access and backbone networks and behind residential gateways. In this paper we provide a taxonomy of these measurement platforms on the basis of their deployment use-case. We describe these platforms in detail by exploring their coverage, scale, lifetime, deployed metrics and measurement tools, architecture and overall research impact. We conclude the survey by describing current standardization efforts to make large-scale performance measurement platforms interoperable.

show abstract