Carlog

Jiang, Yurong; Qiu, Hang; McCartney, Matthew; Halfond, William G. J.; Bai, Fan; Grimm, Donald; Govindan, Ramesh

doi:10.1145/2668332.2668350

Cited by 19 publications

(3 citation statements)

References 39 publications

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“…Declarative programming [8] is used to express the behaviors of mobile events in the context of wireless sensor networks [9]. Recently, Carlog [10] has developed a programming framework to harness a large number of networked, embedded automotive sensors, and take advantage of a datalog query optimization technique to access both cloud-based virtual sensors together with vehicle sensors.…”

Section: Related Workmentioning

confidence: 99%

NORA: Towards Large-Scale Vehicular Analytics for Driving Environment Monitoring/Assessment

Grimm,

Bai,

Chen

et al. 2023

IEEE Open J. Veh. Technol.

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Research in vehicular analytics has explored two different approaches to infer properties about a vehicle's surroundings: (a) using sensors on smartphone devices to infer properties about surroundings, or (b) to use in-vehicle sensors. The latter approach was less studied. In this paper, we take a first step beyond research to understand, using a pilot study, how to design vehicular analytics at scale. Our pilot prototype NORA (Network Oriented Road Applications) contains novel algorithms to detect roadside phenomena (such as potholes, rough road, and slippery surfaces). These leverage multiple in-vehicle sensors to disambiguate these phenomena from other conflating factors. NORA reliably detects, on a cloud service, roadside events from multiple individual in-vehicle detections. To do this, it uses careful clustering techniques to assess the spatial scale of the event, and belief decay techniques to match event duration. It also employs aggressive fleetwide suppression of detections to minimize communication cost. Through a 50-vehicle deployment of NORA pilot over 9 months, it is shown that the results obtained from NORA in-vehicle detection methods match very well with ground truth measurements, and NORA cloud is effective at aggregating road events in an accurate and efficient fashion.

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Section: Related Workmentioning

confidence: 99%

NORA: Towards Large-Scale Vehicular Analytics for Driving Environment Monitoring/Assessment

Grimm,

Bai,

Chen

et al. 2023

IEEE Open J. Veh. Technol.

Self Cite

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“…Datalog has already been used in cloud‐related research and industry activities,() but none of them were designed to address the data aggregation in NFV environments. The OpenStack policy framework Congress uses Datalog, in this case, as the language to describe the policy rules in an OpenStack environment.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by research in declarative programming,() we propose to use Datalog, a declarative logic programming language with recursive query capability, as the base for the performance data query language. The query language aims to help the service developer, operator, and other users to monitor the performance from a high‐level layer.…”

Section: Introductionmentioning

confidence: 99%

Automatic data aggregation for recursively modeled NFV services

2017

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Summary Network function virtualization (NFV) allows to model network services as graphs interconnecting virtual network functions (VNFs), which may include nested VNFs, modeled as subgraphs of VNFs or end points. To query the performance data of network services modeled as abstract and high‐level graphs is challenging because of the recursivity of the NFV architecture and the elasticity and dynamicity provided by the NFV infrastructure. We propose to use Datalog, a declarative logic programming language, to build a framework that supports efficient data aggregation for performance metrics of recursively modeled network services. We present our recursive query language for automatic and flexible decomposition and aggregation of NFV performance metrics and describe example use cases for both compute and network metrics. We also describe the design and implementation of a proof‐of‐concept query engine using the language. Our performance evaluation shows that the total query latency is dominated by retrieval times of performance metrics from infrastructure databases, and the time for execution and automatic decomposition of high‐level queries by the query engine itself increases linearly with the size of the service graph up to 1000 nodes. Hence, our evaluations show that the query engine scales well, bounded mainly by the limited execution capabilities of our test environment. Furthermore, it can handle multiple concurrent queries up to the concurrency limits of the backend database in use. The proposed query language and engine are thus effective in recursively retrieving performance metrics of NFV environments supporting large‐scale service graphs and large numbers of query requests.

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