This diploma thesis deals with creating a container for the migration components that are used on the Android operating system, how they operate and distribute, the life cycle of the container, and the ability to customize other components for this container. The work deals with the analysis of the Android operating system, existing solutions, the design of the own container and the way of distributing the components on the devices where the container is used. The next section describes the implementation of the server component that distributes components. It also deals with the implementation of container-containing library and a sample application that illustrates how it is possible to link all these elements together.

Context. As the number of organizations using cloud application platforms to host their applications increases, the priority of distributing physical resources within those platforms is increasing simultaneously. The goal is to host a higher quantity of applications per physical server, while at the same time retain a satisfying rate of performance combined with certain scalability. The modern needs of customers occasionally also imply an assurance of certain privacy for their applications. Objectives. In this study two types of instances for hosting applications in cloud application platforms, virtual machines and application containers, are comparatively analyzed. This investigation has the goal to expose advantages and disadvantages between the instances in order to determine which is more appropriate for being used in cloud application platforms, in terms of performance, scalability and user isolation. Methods. The comparison is done on a server running Linux Ubuntu 16.04. The virtual machine is created using Devstack, a development environment of Openstack, while the application container is hosted by Docker. Each instance is running an apache web server for handling HTTP requests. The comparison is done by using different benchmark tools for different key usage scenarios and simultaneously observing the resource usage in respective instance. Results. The results are produced by investigating the user isolation and resource occupation of respective instance, by examining the file system, active process handling and resource allocation after creation. Benchmark tools are executed locally on respective instance, for a performance comparison of the usage of physical resources. The amount of CPU operations executed within a given time is measured in order determine the processor performance, while the speed of read and write operations to the main memory is measured in order to determine the RAM performance. A file is also transmitted between host server and application in order to compare the network performance between respective instance, by examining the transfer speed of the file. Lastly a set of benchmark tools are executed on the host server to measure the HTTP server request handling performance and scalability of each instance. The amount of requests handled per second is observed, but also the resource usage for the request handling at an increasing rate of served requests and clients. Conclusions. The virtual machine is a better choice for applications where privacy is a higher priority, due to the complete isolation and abstraction from the rest of the physical server. Virtual machines perform better in handling a higher quantity of requests per second, while application containers is faster in transferring files through network. The container requires a significantly lower amount of resources than the virtual machine in order to run and execute tasks, such as responding to HTTP requests. When it comes to scalability the prefered type of instance depends on the priority of key usage scenarios. Virtual machines have quicker response time for HTTP requests but application containers occupy less physical resources, which makes it logically possible to run a higher quantity of containers than virtual machines simultaneously on the same physical server.