The combination of edge and cloud in the fog computing paradigm enables a new breed of dataintensive applications. These applications, however, have to face a number of fog-specific challenges which developers have to repetitively address for every single application.In this paper, we propose a replication service specifically tailored to the needs of data-intensive fog applications that aims to ease or eliminate challenges caused by the highly distributed and heterogeneous environment fog applications operate in. Furthermore, we present our prototypical proof-of-concept implementation FBase that we have made available as open source. ABSTRACTThe combination of edge and cloud in the fog computing paradigm enables a new breed of data-intensive applications. These applications, however, have to face a number of fog-specific challenges which developers have to repetitively address for every single application.In this paper, we propose a replication service specifically tailored to the needs of data-intensive fog applications that aims to ease or eliminate challenges caused by the highly distributed and heterogeneous environment fog applications operate in. Furthermore, we present our prototypical proof-of-concept implementation FBase that we have made available as open source.
Due to the popularity of the FaaS programming model, there is now a wide variety of commercial and open-source FaaS systems. Hence, for comparison of different FaaS systems and their configuration options, FaaS application developers rely on FaaS benchmarking frameworks. Existing frameworks, however, tend to evaluate only single isolated aspects, a more holistic application-centric benchmarking framework is still missing.In previous work, we proposed BeFaaS, an extensible application-centric benchmarking framework for FaaS environments that focuses on the evaluation of FaaS platforms through realistic and typical examples of FaaS applications. In this extended paper, we (i) enhance our benchmarking framework with additional features for distributed FaaS setups, (ii) design application benchmarks reflecting typical FaaS use cases, and (iii) use them to run extensive experiments with commercial cloud FaaS platforms (AWS Lambda, Azure Functions, Google Cloud Functions) and the tinyFaaS edge serverless platform. BeFaaS now includes four FaaS application-centric benchmarks, is extensible for additional workload profiles and platforms, and supports federated benchmark runs in which the benchmark application is distributed over multiple FaaS systems while collecting fine-grained measurement results for drill-down analysis.Our experiment results show that (i) network transmission is a major contributor to response latency for function chains, (ii) this effect is exacer- * This work extends [1]. bated in hybrid edge-cloud deployments, (iii) the trigger delay between a published event and the start of the triggered function ranges from about 100ms for AWS Lambda to 800ms for Google Cloud Functions, and (iv) Azure Functions shows the best cold start behavior for our workloads.
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