79 research outputs found
An event-driven serverless ETL pipeline on AWS
This work presents an event-driven Extract, Transform, and Load (ETL) pipeline serverless architecture and provides an evaluation of its performance over a range of dataflow tasks of varying frequency, velocity, and payload size. We design an experiment while using generated tabular data throughout varying data volumes, event frequencies, and processing power in order to measure: (i) the consistency of pipeline executions; (ii) reliability on data delivery; (iii) maximum payload size per pipeline; and, (iv) economic scalability (cost of chargeable tasks). We run 92 parameterised experiments on a simple AWS architecture, thus avoiding any AWS-enhanced platform features, in order to allow for unbiased assessment of our model’s performance. Our results indicate that our reference architecture can achieve time-consistent data processing of event payloads of more than 100 MB, with a throughput of 750 KB/s across four event frequencies. It is also observed that, although the utilisation of an SQS queue for data transfer enables easy concurrency control and data slicing, it becomes a bottleneck on large sized event payloads. Finally, we develop and discuss a candidate pricing model for our reference architecture usage
Are Unikernels Ready for Serverless on the Edge?
Function-as-a-Service (FaaS) is a promising edge computing execution model
but requires secure sandboxing mechanisms to isolate workloads from multiple
tenants on constrained infrastructure. Although Docker containers are
lightweight and popular in open-source FaaS platforms, they are generally
considered insufficient for executing untrusted code and providing sandbox
isolation. Commercial cloud FaaS platforms thus rely on Linux microVMs or
hardened container runtimes, which are secure but come with a higher resource
footprint.
Unikernels combine application code and limited operating system primitives
into a single purpose appliance, reducing the footprint of an application and
its sandbox while providing full Linux compatibility. In this paper, we study
the suitability of unikernels as an edge FaaS execution environment using the
Nanos and OSv unikernel tool chains. We compare performance along several
metrics such as cold start overhead and idle footprint against sandboxes such
as Firecracker Linux microVMs, Docker containers, and secure gVisor containers.
We find that unikernels exhibit desirable cold start performance, yet lag
behind Linux microVMs in stability. Nevertheless, we show that unikernels are a
promising candidate for further research on Linux-compatible FaaS isolation
SoC-Cluster as an Edge Server: an Application-driven Measurement Study
Huge electricity consumption is a severe issue for edge data centers. To this
end, we propose a new form of edge server, namely SoC-Cluster, that
orchestrates many low-power mobile system-on-chips (SoCs) through an on-chip
network. For the first time, we have developed a concrete SoC-Cluster server
that consists of 60 Qualcomm Snapdragon 865 SoCs in a 2U rack. Such a server
has been commercialized successfully and deployed in large scale on edge
clouds. The current dominant workload on those deployed SoC-Clusters is cloud
gaming, as mobile SoCs can seamlessly run native mobile games.
The primary goal of this work is to demystify whether SoC-Cluster can
efficiently serve more general-purpose, edge-typical workloads. Therefore, we
built a benchmark suite that leverages state-of-the-art libraries for two
killer edge workloads, i.e., video transcoding and deep learning inference. The
benchmark comprehensively reports the performance, power consumption, and other
application-specific metrics. We then performed a thorough measurement study
and directly compared SoC-Cluster with traditional edge servers (with Intel CPU
and NVIDIA GPU) with respect to physical size, electricity, and billing. The
results reveal the advantages of SoC-Cluster, especially its high energy
efficiency and the ability to proportionally scale energy consumption with
various incoming loads, as well as its limitations. The results also provide
insightful implications and valuable guidance to further improve SoC-Cluster
and land it in broader edge scenarios
Characterizing Network Requirements for GPU API Remoting in AI Applications
GPU remoting is a promising technique for supporting AI applications.
Networking plays a key role in enabling remoting. However, for efficient
remoting, the network requirements in terms of latency and bandwidth are
unknown. In this paper, we take a GPU-centric approach to derive the minimum
latency and bandwidth requirements for GPU remoting, while ensuring no (or
little) performance degradation for AI applications. Our study including
theoretical model demonstrates that, with careful remoting design, unmodified
AI applications can run on the remoting setup using commodity networking
hardware without any overhead or even with better performance, with low network
demands
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