Big data is a developing term that describes any large amount of structured and unstructured data that has the potential to be mined for information. To store this type of large amounts of data, cloud storage systems are necessary. These cloud storage systems are developed such that they are capable of keeping the data accessible and available to the users over a network. To store big data new platforms are required. Some of the popular big data platforms are Mongo, Cassandra and Hadoop. In this thesis we used Cassandra database system because it is a distributed database and also open source. Cassandra’s architecture is master less ring design that is easy to setup and easy to maintain. Apache Cassandra is a highly scalable distributed database designed to handle big data management with linear scalable and seamless multiple data center deployment. It is a NoSQL database system which allow schema free tables so that a data item could have a variable set of columns unlike in relational databases. Cassandra provides with high scalability with no single point of failure. For the past few years’ container based virtualization has been evolving rapidly. Container based virtualization such as LXC have been focused here. Linux Containers (LXC) is an operating system level virtualization method for running multiple isolated Linux systems on a single control host. It does not resemble a virtual machine, but provides a virtual environment that has its own CPU, memory, network, etc. space and the resource control mechanism. In this thesis work performance of Apache Cassandra database has been analyzed between bare metal and Linux Containers(LXC). A three node Cassandra cluster has been created on both bare metal and Linux container. Assuming one node as seed and Cassandra stress utility tool has been used to test the load of Cassandra cluster. The performance of Cassandra cluster database has been evaluated in bare metal and Linux Container which is the goal of this thesis work. Linux containers (LXC) are deployed in all the servers. A three node Cassandra database cluster has been created in these servers and also in Linux Container(LXC). Port forwarding is the technique used here for making communication between Cassandra in LXC which is the goal of this thesis work. The performance metrics which determine the performance of Cassandra cluster database are selected according to it. The network configuration parameters are changed according to the behavior of Cassandra. By doing changes in these parameters Cassandra starts running according to the required configuration, after this Cassandra cluster performance will be analyzed. This is done with different write, read and mixed load operations and compared with Cassandra cluster performance on bare metal. The results of the thesis show an analysis of measurements of performance metrics like CPU utilization, Disk throughput and latency while running on Cassandra cluster in both bare metal and Linux Containers. A quantitative and statistical analysis of performance of Cassandra cluster is compared. The physical resources utilized by the Cassandra database on native bare metal and Linux Containers (LXC) is similar. According to the results, CPU utilization is more for Cassandra database in Linux Containers. Disk throughput is also more in Linux Containers except in the case of 66% load write operation. Bare metal has less latency compared to Linux Containers in all the scenarios.

In recent times Cloud Computing has become an accessible technology which makes it possible to provide online services to end user by the network of remote servers. With the increase in remote servers and resources allocated to these remote servers leads to performance degradation of service. In such a case, the environment on which service is made run plays a significant role in order to provide better performance and adds up to Quality of Service. This paper focuses on Bare metal and Linux container environments as request response time is one of the performance metrics to determine the QOS. To improve request response time platforms are customized using real-time kernel and compiler optimization flags to optimize the performance of a service. UDP packets are served to the service made run in these customized environments. From the experiments performed, it concludes that Bare metal using real-time kernel and level 3 Compiler optimization flag gives better performance of a service.

The Internet of Things (IoT) are small, sensing, network enabled computing devices which can extend smart behaviour into resource constrained domains. This thesis focus on evaluating the viability of Linux containers in relation to IoT devices. Three research questions are posed to investigate various aspects of this. (1) Can any guidelines and best practices be derived from creating a Linux container based security enhanced IoT platform? (2) Can the LiCShield project be extended to build dynamic, default deny seccomp configurations? (3) Are Linux containers viable on IoT platforms in regards to operational performance impact? To answer these questions, a literature review was conducted, research gaps identified and a research methodology selected. A Linux-based container platform was then created in which applications could be run. Experimentation was conducted on the platform and operational measurements collected. A number of interesting results was produced during the project. In relation to the first research question, it was discovered that the LXC templating code created could probably benefit other Linux container projects as well as the LXC project itself. Secondly, it was found that a robust, layered containerized security architecture could be created by utilizing basic container configurations and by drawing from best practices from LXC and docker. In relation to the second research question, a proof of concept system was created to profile and build dynamic, default deny seccomp configurations. Analysis of the system shows that the developed method is viable. In relation to the final research question; Container overhead with regards to CPU, memory, network I/O and storage was measured. In this project, there were no CPU overhead and only a slight performance decrease of 0.1 % on memory operations. With regards to network I/O, a speed decrease of 0.2 % was observed when a container received data and utilized NAT. On the other hand, while the container was sending data, a speed increase of 1.4 % was observed while the container was operating in bridge mode and an increase of 0.9 % was observed while utilizing NAT. Regarding storage overhead, a total of 508 KB base overhead was added to each container on creation. Due to these findings, the overhead containers introduce are considered negligible and thus deemed viable on IoT devices.