Towards an Optimized, Cloud-Agnostic Deployment of Hybrid Applications

Serverless computing is currently taking a momentum due to the main benefits it introduces which include zero administration and reduced operation cost for applications. However, not all application components can be made serverless in sight also of certain limitations with respect to the deployment of such components in corresponding serverless platforms. In this respect, there is currently a great need for managing hybrid applications, i.e., applications comprising both normal and serverless components. Such a need is covered in this paper through extending the Melodic platform in order to support the deployment and adaptive provisioning of hybrid, cross-cloud applications. Apart from analysing the architecture of the extended platform, we also explain what are the relevant challenges for supporting the management of serverless components and how we intend to confront them. One use case is also utilised in order to showcase the main benefits of the proposed platform.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Josef Spillner,et al.  Transformation of Python Applications into Function-as-a-Service Deployments , 2017, ArXiv.

[3]  Kyriakos Kritikos,et al.  A Review of Serverless Frameworks , 2018, 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion).

[4]  Jungwon Lee,et al.  Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition , 2017, INTERSPEECH.

[5]  Dana Petcu,et al.  MODAClouds: A model-driven approach for the design and execution of applications on multiple Clouds , 2012, 2012 4th International Workshop on Modeling in Software Engineering (MISE).

[6]  Thomas S. Huang,et al.  Dilated Recurrent Neural Networks , 2017, NIPS.

[7]  Xinghui Zhao,et al.  Supporting Multi-Provider Serverless Computing on the Edge , 2018, ICPP Workshops.

[8]  Jun Yang,et al.  Data Management in Machine Learning: Challenges, Techniques, and Systems , 2017, SIGMOD Conference.

[9]  Jörg Domaschka,et al.  D2.1.2 - CloudML Implementation Documentation - First version , 2014 .

[10]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Geoffrey C. Fox,et al.  Status of Serverless Computing and Function-as-a-Service(FaaS) in Industry and Research , 2017, ArXiv.

[12]  Josef Spillner,et al.  Java Code Analysis and Transformation into AWS Lambda Functions , 2017, ArXiv.

[13]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[14]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[15]  Yi Zhu,et al.  DenseNet for dense flow , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[16]  Geir Horn,et al.  MELODIC: Utility Based Cross Cloud Deployment Optimisation , 2018, 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA).

[17]  Perry Cheng,et al.  Serverless Computing: Current Trends and Open Problems , 2017, Research Advances in Cloud Computing.

[18]  Josef Spillner,et al.  FaaSter, Better, Cheaper: The Prospect of Serverless Scientific Computing and HPC , 2017, CARLA.

[19]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .