8,340 research outputs found
MalStone: Towards A Benchmark for Analytics on Large Data Clouds
Developing data mining algorithms that are suitable for cloud computing
platforms is currently an active area of research, as is developing cloud
computing platforms appropriate for data mining. Currently, the most common
benchmark for cloud computing is the Terasort (and related) benchmarks.
Although the Terasort Benchmark is quite useful, it was not designed for data
mining per se. In this paper, we introduce a benchmark called MalStone that is
specifically designed to measure the performance of cloud computing middleware
that supports the type of data intensive computing common when building data
mining models. We also introduce MalGen, which is a utility for generating data
on clouds that can be used with MalStone
Single-Board-Computer Clusters for Cloudlet Computing in Internet of Things
The number of connected sensors and devices is expected to increase to billions in the near
future. However, centralised cloud-computing data centres present various challenges to meet the
requirements inherent to Internet of Things (IoT) workloads, such as low latency, high throughput
and bandwidth constraints. Edge computing is becoming the standard computing paradigm for
latency-sensitive real-time IoT workloads, since it addresses the aforementioned limitations related
to centralised cloud-computing models. Such a paradigm relies on bringing computation close to
the source of data, which presents serious operational challenges for large-scale cloud-computing
providers. In this work, we present an architecture composed of low-cost Single-Board-Computer
clusters near to data sources, and centralised cloud-computing data centres. The proposed
cost-efficient model may be employed as an alternative to fog computing to meet real-time IoT
workload requirements while keeping scalability. We include an extensive empirical analysis to
assess the suitability of single-board-computer clusters as cost-effective edge-computing micro data
centres. Additionally, we compare the proposed architecture with traditional cloudlet and cloud
architectures, and evaluate them through extensive simulation. We finally show that acquisition costs
can be drastically reduced while keeping performance levels in data-intensive IoT use cases.Ministerio de Economía y Competitividad TIN2017-82113-C2-1-RMinisterio de Economía y Competitividad RTI2018-098062-A-I00European Union’s Horizon 2020 No. 754489Science Foundation Ireland grant 13/RC/209
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