Abstract
Combining edge processing (at data capture site) with analysis carried out while data is enroute from the capture site to a data center offers a variety of different processing models. Such in-transit nodes include network data centers that have generally been used to support content distribution (providing support for data multicast and caching), but have recently started to offer user-defined programmability, through Software Defined Networks (SDN) capability, e.g., OpenFlow and Network Function Visualization (NFV). We demonstrate how this multi-site computational capability can be aggregated to support video analytics, with Quality of Service and cost constraints (e.g., latency-bound analysis). The use of SDN technology enables separation of the data path from the control path, enabling in-network processing capabilities to be supported as data is migrated across the network. We propose to leverage SDN capability to gain control over the data transport service with the purpose of dynamically establishing data routes such that we can opportunistically exploit the latent computational capabilities located along the network path. Using a number of scenarios, we demonstrate the benefits and limitations of this approach for video analysis, comparing this with the baseline scenario of undertaking all such analysis at a data center located at the core of the infrastructure.
Highlights
WITH the maturity of the Internet of Things (IoT) paradigm and associated devices, data sensing can be combined with data processing/analysis on the same device
We considered that each camera aggregator (Source) had three cameras capturing and sending video to them
We look at combining edge processing with analysis carried out while data is enroute from the capture site to a data center and different processing models, and unlike mentioned papers our target is considering networking resource and dealing with other computational and storage resources through the use of an Software Defined Networks (SDN) controller
Summary
WITH the maturity of the Internet of Things (IoT) paradigm and associated devices, data sensing can be combined with data processing/analysis on the same device. As IoT devices increase in function and capability, existing infrastructures such as monitoring/ storage and network capabilities can be combined to create a more general purpose data analysis and computational environment Such a perspective assumes that IoT devices and in-transit network nodes, over which such data is channeled, can be used to support data processing along with the data centres to which this data is sent, typically located at the core of the infrastructure. This comes with the recent interest in moving away from centralized, large-scale data centers to a more distributed multi-cloud setting (as demonstrated by significant interest in cloud federation and interoperability efforts). Combining IoT and Cloud computing capability enables the creation of smart environments that can respond to realtime events, by (a) combining services offered by multiple stakeholders
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