TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes
Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications based on this model are often composed of several fine-grained and ephemeral Function-as-a-Service (Fa...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9684420/ |
| _version_ | 1818952704404226048 |
|---|---|
| author | Markus Steinbach Anshul Jindal Mohak Chadha Michael Gerndt Shajulin Benedict |
| author_facet | Markus Steinbach Anshul Jindal Mohak Chadha Michael Gerndt Shajulin Benedict |
| author_sort | Markus Steinbach |
| collection | DOAJ |
| description | Serverless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications based on this model are often composed of several fine-grained and ephemeral Function-as-a-Service (FaaS) functions that implement complex business processes via mutual interaction and interaction with Backend-as-a-Services (BaaS) such as databases. FaaS functions suffer from the cold start problem because of the scale to zero instances feature. In this work, we use neural Temporal Point Processes (TPPs) to model function invocations in FaaS compositions. A probability distribution over the time and class of the following invocations for a given history of invocations is predicted using these probabilistic models. The prediction can avoid cold starts by scaling functions in advance and reduce network load by optimizing the function-server assignment. In this regard, we developed a python-based tool called <italic>TppFaaS</italic> on top of OpenWhisk open-source serverless platform. TppFaaS uses the neural TPPs LogNormMix for modeling the time using a log-normal mixture distribution and TruncNorm for predicting a single value for the time. Furthermore, we built a custom trace data collector for OpenWhisk embedded into TppFaaS and created datasets for multiple FaaS compositions to train and test our models. For datasets without cold starts, the models achieved for most compositions a mean absolute error below 22ms and a percentage of correctly predicted function classes above 94%. |
| first_indexed | 2024-12-20T09:54:36Z |
| format | Article |
| id | doaj.art-62eb5020c5f446ecb2be382b0b2ea974 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T09:54:36Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-62eb5020c5f446ecb2be382b0b2ea9742022-12-21T19:44:30ZengIEEEIEEE Access2169-35362022-01-01109059908410.1109/ACCESS.2022.31440789684420TppFaaS: Modeling Serverless Functions Invocations via Temporal Point ProcessesMarkus Steinbach0Anshul Jindal1https://orcid.org/0000-0002-7773-5342Mohak Chadha2https://orcid.org/0000-0002-1995-7166Michael Gerndt3https://orcid.org/0000-0002-3210-5048Shajulin Benedict4https://orcid.org/0000-0002-2543-2710Chair of Computer Architecture and Parallel Systems, Technical University of Munich, Garching, GermanyChair of Computer Architecture and Parallel Systems, Technical University of Munich, Garching, GermanyChair of Computer Architecture and Parallel Systems, Technical University of Munich, Garching, GermanyChair of Computer Architecture and Parallel Systems, Technical University of Munich, Garching, GermanyDepartment of Computer Science and Engineering, Indian Institute of Information Technology, Kerala, Kottayam, IndiaServerless computing is a cloud computing paradigm that allows developers to focus exclusively on business logic as cloud service providers manage resource management tasks. Serverless applications based on this model are often composed of several fine-grained and ephemeral Function-as-a-Service (FaaS) functions that implement complex business processes via mutual interaction and interaction with Backend-as-a-Services (BaaS) such as databases. FaaS functions suffer from the cold start problem because of the scale to zero instances feature. In this work, we use neural Temporal Point Processes (TPPs) to model function invocations in FaaS compositions. A probability distribution over the time and class of the following invocations for a given history of invocations is predicted using these probabilistic models. The prediction can avoid cold starts by scaling functions in advance and reduce network load by optimizing the function-server assignment. In this regard, we developed a python-based tool called <italic>TppFaaS</italic> on top of OpenWhisk open-source serverless platform. TppFaaS uses the neural TPPs LogNormMix for modeling the time using a log-normal mixture distribution and TruncNorm for predicting a single value for the time. Furthermore, we built a custom trace data collector for OpenWhisk embedded into TppFaaS and created datasets for multiple FaaS compositions to train and test our models. For datasets without cold starts, the models achieved for most compositions a mean absolute error below 22ms and a percentage of correctly predicted function classes above 94%.https://ieeexplore.ieee.org/document/9684420/Cloud computingfaasfaas compositionfunction-as-a-servicemodelingserverless computing |
| spellingShingle | Markus Steinbach Anshul Jindal Mohak Chadha Michael Gerndt Shajulin Benedict TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes IEEE Access Cloud computing faas faas composition function-as-a-service modeling serverless computing |
| title | TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes |
| title_full | TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes |
| title_fullStr | TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes |
| title_full_unstemmed | TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes |
| title_short | TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes |
| title_sort | tppfaas modeling serverless functions invocations via temporal point processes |
| topic | Cloud computing faas faas composition function-as-a-service modeling serverless computing |
| url | https://ieeexplore.ieee.org/document/9684420/ |
| work_keys_str_mv | AT markussteinbach tppfaasmodelingserverlessfunctionsinvocationsviatemporalpointprocesses AT anshuljindal tppfaasmodelingserverlessfunctionsinvocationsviatemporalpointprocesses AT mohakchadha tppfaasmodelingserverlessfunctionsinvocationsviatemporalpointprocesses AT michaelgerndt tppfaasmodelingserverlessfunctionsinvocationsviatemporalpointprocesses AT shajulinbenedict tppfaasmodelingserverlessfunctionsinvocationsviatemporalpointprocesses |