4,049 research outputs found
Managing Data Replication and Distribution in the Fog with FReD
The heterogeneous, geographically distributed infrastructure of fog computing
poses challenges in data replication, data distribution, and data mobility for
fog applications. Fog computing is still missing the necessary abstractions to
manage application data, and fog application developers need to re-implement
data management for every new piece of software. Proposed solutions are limited
to certain application domains, such as the IoT, are not flexible in regard to
network topology, or do not provide the means for applications to control the
movement of their data.
In this paper, we present FReD, a data replication middleware for the fog.
FReD serves as a building block for configurable fog data distribution and
enables low-latency, high-bandwidth, and privacy-sensitive applications. FReD
is a common data access interface across heterogeneous infrastructure and
network topologies, provides transparent and controllable data distribution,
and can be integrated with applications from different domains. To evaluate our
approach, we present a prototype implementation of FReD and show the benefits
of developing with FReD using three case studies of fog computing applications
Towards a Benchmark for Fog Data Processing
Fog data processing systems provide key abstractions to manage data and event
processing in the geo-distributed and heterogeneous fog environment. The lack
of standardized benchmarks for such systems, however, hinders their development
and deployment, as different approaches cannot be compared quantitatively.
Existing cloud data benchmarks are inadequate for fog computing, as their focus
on workload specification ignores the tight integration of application and
infrastructure inherent in fog computing.
In this paper, we outline an approach to a fog-native data processing
benchmark that combines workload specifications with infrastructure
specifications. This holistic approach allows researchers and engineers to
quantify how a software approach performs for a given workload on given
infrastructure. Further, by basing our benchmark in a realistic IoT sensor
network scenario, we can combine paradigms such as low-latency event
processing, machine learning inference, and offline data analytics, and analyze
the performance impact of their interplay in a fog data processing system
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