A PhD project by Dr. Thomas Fankhauser with the
University of the West of Scotland and Stuttgart Media University

Logo of Web Scaling Frameworks

Web Scaling Frameworks

Building scalable, high-performance, portable and interoperable Web Services for the Cloud


Through social traffic, smart health and the Internet of Things humans create increasingly more data. To process this data, complex cloud computing systems need to be set up manually by each customer and for each provider. In the project, a framework is proposed that automatically sets up, manages and scales cloud resources efficiently and in an optimised fashion.

The major optimisation schemes are an optimised request flow that distributes requests to different subsytems based on their HTTP verb. GET and HEAD requests go to the read subsystem, where all other requests go to a processing subsystem. Further, resource dependency processing tracks dependencies between all requestable resources and processes updates in a distributed fashion with optimised processing trees. All this functionality is put into a Web Scaling Framework so it can be reused by multiple web services.

Questions and Answers

What was your motivation for the project?

The amount of data to process continuously increases. At the same time, results of these processings are most valuable close to their collection. While cloud computing provides the resources for fast processing, state-of-the-art web applications need to be customised to utilise cloud offerings. Web Scaling Frameworks are proposed to reduce this costly customisations.

What are your key findings and contributions?

A complete storage of all requestable resources and tracking of their exact dependencies can enable a more efficient processing, access and scaling performance. In general, it makes sense to process time-critical actions when time is available: on write. Due to client logic, eventual consistency in the web is possible and allows for optimised processing in the background.

What is different to other work in the field?

The work proposes to not use cache eviction but an exact processing and definition of dependencies between all resources. This is close to a static site generation approach that recreates sites on updates. Thereby, all resources are ready to deliver at any point in time.

Major Findings

The following is a list of the major findings from the project. To put them in context, please refer to the full thesis.

Web Scaling Frameworks: Conceptual Architecture

Conceptual Architecture of Web Scaling Frameworks

The major design goal for Web Scaling Frameworks is to create an architecture that enables to build maintainable, automatable, scalable, resilient, portable and interoperable implementations of WSFs.

The cloud on the left side of the Figure presents the logical structure of a WSF, where the modules within this cloud implement the core functionality of a WSF. The right side of Figure shows the components that are managed by a WSF. The components provide services and functionalities needed to operate a full web application. One type of component is the worker component at (g), which hosts the application logic that is implemented with the help of a WAF. The worker component joins a WSF with a Web Application Framework, where the worker logic is implemented by the WSF and the application logic is implemented by the WAF.

Optimised Request Flow Scheme

Optimised Request Flow of Web Scaling Frameworks

The illustrated composition of components in (a-e) is created with two major design goals: optimised performance and enhanced scalability. The approach to optimising the performance is to minimise the request flow graph for every request.

The Figure at (a-e) illustrates the detailed flow of a request through the components: Requests enter the system through multiple load balancers LB at (a). The load balancers LB forward the request to one of the dispatchers D. The dispatcher D now decides whether the request is a read request RR or a processing request RP. Read requests RR are determined by the HTTP methods GET, HEAD and OPTIONS that do not have any side effects. Processing requests RP are requests with all other HTTP methods that by definition change content on the server. With this implementation, both subsystems can be scaled independently on a component level.

Resource Dependency Processing

Resource Dependency Processing used in Web Scaling Frameworks

Keeping all resources updated in the resource storage requires the declaration of dependencies between all resources as a directed acyclic graph (DAG). For an efficient processing of dependencies the problem is formulated as follows: Given a vertex v from a dependency graph DG, how long does it take to process all dependencies of v while ensuring correct processing order.

By topologically sorting a DAG, a linear ordering of vertices is generated guaranteeing a vertex v1 to come before a vertex v2 if an edge v1 → v2 exists. If dependencies are processed by the calculated order, it is guaranteed all changes are reflected in all dependent resource vertices. The approach can be optimised further by finding branches of jobs eligible for parallel processing as their outputs do not depend on other resources. The project therefore develops a topological sort algorithm that applies dynamic programming to extract a forest of optimal processing trees from a dependency graph.

Performance Optimisation Triangle

Performance Optimisation Triangle

The overall performance of a web application integrated into a WSF is a trade-off among optimised processing cost, processing duration and storage space. The Figure shows a SPD (read speedy) performance optimisation triangle in the style of the CAP theorem. The triangle illustrates how a system can only be optimised for two out of three goals simultaneously.

A traditional web application with vertical scaling typically requires low storage space S as it caches only parts of all resources. It further can achieve low processing durations D by scaling the system vertically, e.g. by adding a faster CPU or better network. However, it can not exhibit low processing cost P as vertical scaling is more expensive than horizontal scaling and cache misses need to be processed.

A traditional web application with horizontal scaling also requires low storage space S due to partial caching. It further can achieve low processing cost P by employing multiple, inexpensive machines with low hardware specifications. However, with these low hardware specifications it can not achieve low processing durations D, with the same number of machines than SD uses.

A web application using resource dependency processing can provide low processing durations D as all requestable resources are preprocessed and immediately available for delivery. It further can achieve low processing cost P as it processes only requests that require processing and does not evict and reprocess resources to save storage space. Consequently, it can not achieve the goal for low storage space S.


The following is a list of work that has been reviewed, presented and published at research conferences or in journals.

Web Scaling Frameworks

Fankhauser, T., 2016
University of the West of Scotland, PhD Thesis


Resource Dependency Processing in Web Scaling Frameworks

Fankhauser, T., Q. Wang, A. Gerlicher and C. Grecos, 2016
IEEE Transactions on Services Computing, Journal Paper

IEEE Xplore Article PDF

Web Scaling Frameworks for Web Services in the Cloud

Fankhauser, T., Q. Wang, A. Gerlicher, C. Grecos and Wang, X., 2015
IEEE Transactions on Services Computing, Journal Paper

IEEE Xplore Article PDF

Web Scaling Frameworks: A novel class of frameworks for scalable web services in cloud environments

Fankhauser, T., Q. Wang, A. Gerlicher, C. Grecos and Wang, X., 2014
IEEE International Conference on Communications, Sydney, Conference Paper

IEEE Xplore Article PDF

Animations and Videos

The following is a list of animations and videos created throughout the project.

Optimised Request Flow

See the request routing to different subsytems and scaling to an optimal number of machines in action.

Request Flow Animation

Resource Dependency Processing

See the resource dependency processing for a dependency graph in action.

Resource Dependency Processing Animation

Forest of Processing Trees Extraction Algorithm

Generate a random resource dependency graph and extract all optimised dependency processing trees.

TSDP Animation

Traditional vs. Resource Dependency Processing Prototype

Shows the differences between a traditional and a resource dependency processing approach with a Twitter-like web application named LinkR.

LinkR Video

Talks and Presentations

The following is a list of talks and presentations given in the course of the project.

PhD Viva

Viva presentation held at University of the West of Scotland in 2016.

PhD Viva Presentation

UWS Annual Research Conference

Presentation held at the Annual Research Conference at the University of the West of Scotland in 2015.

UWS Annual Research Conference Presentation

UWS Transfer Event

Presentation held for the Transfer Event at the University of the West of Scotland in 2014.

Transfer Event Presentation

IEEE International Conference on Communications

Presentation held at the IEEE International Conference on Communications 2015 in Sydney, Australia.

IEEE ICC14 Presentation

PhD Project Pitch

The initial research project pitch presentation that started the project in 2013.

PhD Pitch Presentation

Evaluation Data

The following data traces and samples were used to evaluate the models created throughout the project.

Traffic Traces

For the evaluation the following traffic traces were used: social.csv.zip trip.csv.zip

Resource Dependency Graphs

For the evaluation, the following resource dependency graphs were used: fuzzy-resources.tar.gz fuzzy-results-requestor.tar.gz fuzzy-results-sequencer.tar.gz service-based-resources.tar.gz service-based-results-requester.tar.gz service-based-results-sequencer.tar.gz service-based-traffic.tar.gz service-structure-graphs.tar.gz

Evaluation Platform

Most of the evaluations were performed on the Pi-One, a cluster of 42 Raspberry Pi computers.

Pi-One - a cluster of 42 Raspberry Pi computers

Contact and Imprint

If you are interested in the project or have an interesting performance problem you need to solve, do not hesitate to get in touch with me by mail tommy@system8.io.

Image of Dr. Thomas Fankhauser

Dr. Thomas Fankhauser
Karl-Rebmann-Str. 12
74189 Weinsberg

Big thanks to my supervisors Prof. Dr. Qi Wang, Prof. Dr. Ansgar Gerlicher and Prof. Dr. Christos Grecos for their fantastic support and to Prof. Dr. Dimitrios Pezaros and Prof. Dr. Feng for examining the thesis.

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