Serverless with Kubernetes

Aug 20, 2017Download as pptx, pdf3 likes751 views

This document discusses how Kubernetes can be used as a platform for serverless applications. It introduces the Kubeless, Funktion, and Fission projects, which allow running serverless functions on Kubernetes. Kubeless extends the Kubernetes API to deploy and manage functions as third-party resources. Funktion uses Kubernetes primitives like deployments and config maps to run functions as containers with a flow-based programming model. Fission launches function containers in response to events using a pool manager. Kubernetes is a good fit for serverless as it provides infrastructure without needing to rebuild components.

Serverless with Kubernetes
Malepati Bala Siva Sai Akhil
Huawei

 What is Serverless?
 What is Kubernetes?
 How Kubernetes fits with Serverless?
 Kubeless
 Funktion
 Fission
 Broad Comparison
 Conclusion
Contents

 Everything Remote API Driven
 Deployments
 Secrets and Config Maps
 …
 Reuse the whole Kubernetes (Almost )
 Main Focus is on Application Code
How Kubernetes fits in Serverless?

 Developed by Skipbox (Recently bought by Bitnami)
 Kubernetes-native Serverless Framework
 Uses many Kubernetes primitives like Deployments,
Services, ConfigMaps, etc.
 Has a mobile app named “Cabin” (IOS and Android)

 Extends Kubernetes API using 3rd Party Resource
Function
 Uses Kubernetes Deployment for running a Controller
for watching Function endpoints and launching runtimes
containing Function Code
 Functions are injected into default runtimes using
Config Maps
 The runtimes are pre-built Docker images that wrap the
functions in an HTTP server
Kubeless Architecture

funktion
 By Fabric 8
 Event Driven (Flow Based)
 Supports over 200 of different event sources and
connectors (Apache Camel Connectors)

 By Platform 9 Systems
 Pooling Concept
 Provides a way to watch for Kubernetes Events

 Kubeless uses 3rd Party Resource of Kubernetes
 Funktion gives a lot of connectors and provides a flow
language for multi level connectors
 Fission supports pooling which optimizes the start time
Broad Comparison

 Kubernetes fits in well for Serverless
 No need of reinventing the wheel
 Less maintenance of the code for Serverless
 As kubernetes improves these serverless platforms improve
inherently 
Conclusion

 kubernetes.io
 kubeless.io
 funktion.fabric8.io
 fission.io
References

Kubeless is a Kubernetes-native serverless solution that allows deploying and managing serverless functions on Kubernetes. It uses custom resource definitions and a controller to create deployments, services, and ingress for functions. Kubeless supports instrumented runtimes with Prometheus client and provides a UI. Serverless Framework now supports deploying functions to Kubeless, allowing developers to write functions once and deploy them to multiple serverless platforms including AWS Lambda, Azure Functions, Google Cloud Functions, and Kubeless.

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この資料は、Roy FieldingのREST論文（第5章）を振り返り、現代Webで誤解されがちなRESTの本質を解説しています。特に、ハイパーメディア制御やアプリケーション状態の管理に関する重要なポイントをわかりやすく紹介しています。 This presentation revisits Chapter 5 of Roy Fielding's PhD dissertation on REST, clarifying concepts that are often misunderstood in modern web design—such as hypermedia controls within representations and the role of hypermedia in managing application state.

Mode-Wise Corridor Level Travel-Time Estimation Using Machine Learning ModelsJournal of Soft Computing in Civil Engineering

This research is oriented towards exploring mode-wise corridor level travel-time estimation using Machine learning techniques such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). Authors have considered buses (equipped with in-vehicle GPS) as the probe vehicles and attempted to calculate the travel-time of other modes such as cars along a stretch of arterial roads. The proposed study considers various influential factors that affect travel time such as road geometry, traffic parameters, location information from the GPS receiver and other spatiotemporal parameters that affect the travel-time. The study used a segment modeling method for segregating the data based on identified bus stop locations. A k-fold cross-validation technique was used for determining the optimum model parameters to be used in the ANN and SVM models. The developed models were tested on a study corridor of 59.48 km stretch in Mumbai, India. The data for this study were collected for a period of five days (Monday-Friday) during the morning peak period (from 8.00 am to 11.00 am). Evaluation scores such as MAPE (mean absolute percentage error), MAD (mean absolute deviation) and RMSE (root mean square error) were used for testing the performance of the models. The MAPE values for ANN and SVM models are 11.65 and 10.78 respectively. The developed model is further statistically validated using the Kolmogorov-Smirnov test. The results obtained from these tests proved that the proposed model is statistically valid.

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Design Optimization of Reinforced Concrete Waffle Slab Using Genetic AlgorithmJournal of Soft Computing in Civil Engineering

This research presents the optimization techniques for reinforced concrete waffle slab design because the EC2 code cannot provide an efficient and optimum design. Waffle slab is mostly used where there is necessity to avoid column interfering the spaces or for a slab with large span or as an aesthetic purpose. Design optimization has been carried out here with MATLAB, using genetic algorithm. The objective function include the overall cost of reinforcement, concrete and formwork while the variables comprise of the depth of the rib including the topping thickness, rib width, and ribs spacing. The optimization constraints are the minimum and maximum areas of steel, flexural moment capacity, shear capacity and the geometry. The optimized cost and slab dimensions are obtained through genetic algorithm in MATLAB. The optimum steel ratio is 2.2% with minimum slab dimensions. The outcomes indicate that the design of reinforced concrete waffle slabs can be effectively carried out using the optimization process of genetic algorithm.

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The escalating energy crisis, heightened environmental awareness and the impacts of climate change have driven global efforts to reduce carbon emissions. A key strategy in this transition is the adoption of green energy technologies particularly for charging electric vehicles (EVs). According to the U.S. Department of Energy, EVs utilize approximately 60% of their input energy during operation, twice the efficiency of conventional fossil fuel vehicles. However, the environmental benefits of EVs are heavily dependent on the source of electricity used for charging. This study examines the potential of renewable energy (RE) as a sustainable alternative for electric vehicle (EV) charging by analyzing several critical dimensions. It explores the current RE sources used in EV infrastructure, highlighting global adoption trends, their advantages, limitations, and the leading nations in this transition. It also evaluates supporting technologies such as energy storage systems, charging technologies, power electronics, and smart grid integration that facilitate RE adoption. The study reviews RE-enabled smart charging strategies implemented across the industry to meet growing global EV energy demands. Finally, it discusses key challenges and prospects associated with grid integration, infrastructure upgrades, standardization, maintenance, cybersecurity, and the optimization of energy resources. This review aims to serve as a foundational reference for stakeholders and researchers seeking to advance the sustainable development of RE based EV charging systems.

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Modeling the Influence of Environmental Factors on Concrete Evaporation RateJournal of Soft Computing in Civil Engineering

hypermedia_system_revisit_roy_fielding .NABLAS株式会社

Mode-Wise Corridor Level Travel-Time Estimation Using Machine Learning ModelsJournal of Soft Computing in Civil Engineering

Smart City is the Future EN - 2024 Thailand Modify V1.0.pdfPawachMetharattanara

Slide share PPT of NOx control technologies.pptxvvsasane

Design Optimization of Reinforced Concrete Waffle Slab Using Genetic AlgorithmJournal of Soft Computing in Civil Engineering

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Empowering Electric Vehicle Charging Infrastructure with Renewable Energy Int...AI Publications

Serverless with Kubernetes

1. Serverless with Kubernetes Malepati Bala Siva Sai Akhil Huawei

2.  What is Serverless?  What is Kubernetes?  How Kubernetes fits with Serverless?  Kubeless  Funktion  Fission  Broad Comparison  Conclusion Contents

4. General Serverless Architecture

5. Kubernetes Architecture

6. Services and Rolling Updates

7. Config Maps and Secrets

8.  Everything Remote API Driven  Deployments  Secrets and Config Maps  …  Reuse the whole Kubernetes (Almost )  Main Focus is on Application Code How Kubernetes fits in Serverless?

9.  Developed by Skipbox (Recently bought by Bitnami)  Kubernetes-native Serverless Framework  Uses many Kubernetes primitives like Deployments, Services, ConfigMaps, etc.  Has a mobile app named “Cabin” (IOS and Android)

10.  Extends Kubernetes API using 3rd Party Resource Function  Uses Kubernetes Deployment for running a Controller for watching Function endpoints and launching runtimes containing Function Code  Functions are injected into default runtimes using Config Maps  The runtimes are pre-built Docker images that wrap the functions in an HTTP server Kubeless Architecture

11. funktion  By Fabric 8  Event Driven (Flow Based)  Supports over 200 of different event sources and connectors (Apache Camel Connectors)

12. funktion  Funktion  Flow  Connector

13.  By Platform 9 Systems  Pooling Concept  Provides a way to watch for Kubernetes Events

14. Fission Architecture

15. Fission Use Cases

16. Fission Use Cases

17. Fission Pool Manager

18. Fission Pool Manager

19.  Kubeless uses 3rd Party Resource of Kubernetes  Funktion gives a lot of connectors and provides a flow language for multi level connectors  Fission supports pooling which optimizes the start time Broad Comparison

20.  Kubernetes fits in well for Serverless  No need of reinventing the wheel  Less maintenance of the code for Serverless  As kubernetes improves these serverless platforms improve inherently  Conclusion

21.  kubernetes.io  kubeless.io  funktion.fabric8.io  fission.io References

Serverless with Kubernetes

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