AXaaS (Acceleration as a service): Can the Telecom Service Provider rent a cloudlet?

Powers, Nathaniel; Soyata, Tolga

doi:10.1109/cloudnet.2015.7335314

Cited by 9 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application scenarios for such services are diverse. Some examples are image processing [42] or sensor data processing [43]. The quality of service needs of these streams will require solutions to cope, for instance, with busy streams or real-time constraints [44,45].…”

Section: Related Work In Community Clouds As Open Commonsmentioning

confidence: 99%

Cloudy in guifi.net: Establishing and sustaining a community cloud as open commons

Baig¹,

Freitag²,

Navarro³

2018

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

Section: Related Work In Community Clouds As Open Commonsmentioning

confidence: 99%

Cloudy in guifi.net: Establishing and sustaining a community cloud as open commons

Baig¹,

Freitag²,

Navarro³

2018

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…While data center resources do provide elasticity, an edge devices cannot scale to workloads which exceed its capacity. One solution to this situation is trying to split larger task and outsource non-critical parts to data centers [2]. Another approach, which we present here, is to improve edge device elasticity by microclouds, which pool edge resources and, through an open software platform, allow service providers and end users to run edge-centric services.…”

Section: Introductionmentioning

confidence: 99%

Building Microclouds at the Network Edge with the Cloudy Platform

Freitag¹,

Pueyo²,

Navarro³

2017

2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2)

View full text Add to dashboard Cite

Edge computing enables new types of services which operate at the network edge. There are important use cases in pervasive computing, ambient intelligence and the Internet of Things (IoT) for edge computing. In this demo paper we present microclouds deployed at the networks edge in the Guifi.net community network leveraging an open extensible platform called Cloudy. The demonstration focuses on the following aspects: The usage of Cloudy for end users, the services of Cloudy to build microclouds, and the application scenarios of IoT data management within microclouds.

show abstract

“…Rather than a generalized infrastructure, a more specialized infrastructure provides much better business opportunities (Powers & Soyata, 2015). For example, the databases that store medical information could be optimized to handle medical data, potentially incorporating privacy preserving storage and data obfuscation methods as built-in features.…”

Section: Back End Business Opportunitiesmentioning

confidence: 99%

“…While this data concentration is a much higher workload than what the individual IoT devices can handle, concentrators are not necessarily the destinations where the pre-processing takes place. For pre-processing, cloudlets are used that are substantially more computationally capable than concentrators and have dedicated WAN links (Soyata, Ba, Heinzelman, Kwon, & Shi, 2013;Powers, et al, 2015). The pre-processing of the data turns raw data into a much more summarized format, such as the computation of the QT and heart rate information from raw ECG signals (Page, et al, 2015c).…”

Section: Front Endmentioning

confidence: 99%

Support Systems for Health Monitoring Using Internet-of-Things Driven Data Acquisition

Page¹,

Hijazi²,

Askan³

et al. 2016

STSC

Self Cite

View full text Add to dashboard Cite

The Digital Health (D-Health) era is expected to be the "next big thing" since the invention of the internet, characterized by inexpensive and widespread medical data acquisition devices, widespread availability of identityremoved health data, and analytics algorithms that provide remote health monitoring feedback to doctors in realtime. Recent years have brought incremental developments in three key technological areas towards the realization of the D-Health era: data acquisition, secure data transmission/storage, and data analytics. i) For data acquisition, the emerging Internet-of-Things (IoT) devices are becoming a viable technology to enable the acquisition of remote health monitoring data. ii) For data storage, emerging system-level and cryptographic mechanisms provide secure and privacy-preserving transmission, storage, and sharing of the acquired data. iii) For data analytics, emerging decision support algorithms provide a mechanism for healthcare professionals to base their clinical diagnoses partially on machine-suggested statistical inferences that rely on a wide corpus of accumulated data. The D-Health era will create new business opportunities in all of these areas. In this paper, we propose a generalized structure for a DHealth system that is capable of remote health monitoring and decision support. We formulate our proposed structure around potential business opportunities and conduct technical feasibility studies.

show abstract

AXaaS (Acceleration as a service): Can the Telecom Service Provider rent a cloudlet?

Cited by 9 publications

References 23 publications

Cloudy in guifi.net: Establishing and sustaining a community cloud as open commons

Cloudy in guifi.net: Establishing and sustaining a community cloud as open commons

Building Microclouds at the Network Edge with the Cloudy Platform

Support Systems for Health Monitoring Using Internet-of-Things Driven Data Acquisition

Contact Info

Product

Resources

About