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.

Context. Cloud computing is growing fast and has established itself as the next generationsoftware infrastructure. A major role in cloud computing is the virtualization of hardware toisolate systems from each other. This virtualization is often done with Virtual Machines thatemulate both hardware and software, which in turn makes the process isolation expensive. Newtechniques, known as Microservices or containers, has been developed to deal with the overhead.The infrastructure is conjoint with storing, processing and serving vast and unstructureddata sets. The overall cloud system needs to have high performance while providing scalabilityand easy deployment. Microservices can be introduced for all kinds of applications in a cloudcomputing network, and be a better fit for certain products.Objectives. In this study we investigate how a small system consisting of a Cassandra clusterperform while encapsulated in LXC and Docker containers, compared to a non virtualizedstructure. A specific loader is built to stress the cluster to find the limits of the containers.Methods. We constructed an experiment on a three node Cassandra cluster. Test data is sentfrom the Cassandra-loader from another server in the network. The Cassandra processes are thendeployed in the different architectures and tested. During these tests the metrics CPU, disk I/O,network I/O are monitored on the four servers. The data from the metrics is used in statisticalanalysis to find significant deviations.Results. Three experiments are being conducted and monitored. The Cluster test pointed outthat isolated Docker container indicate major latency during disk reads. A local stress test furtherconfirmed those results. The step-wise test in turn, implied that disk read latencies happened dueto isolated Docker containers needs to read more data to handle these requests. All Microservicesprovide some overheads, but fall behind the most for read requests.Conclusions. The results in this study show that virtualization of Cassandra nodes in a clusterbring latency in comparison to a non virtualized solution for write operations. However, thoselatencies can be neglected if scalability in a system is the main focus. For read operationsall microservices had reduced performance and isolated Docker containers brought out thehighest overhead. This is due to the file system used in those containers, which makes disk I/Oslower compared to the other structures. If a Cassandra cluster is to be launched in a containerenvironment we recommend a Docker container with mounted disks to bypass Dockers filesystem or a LXC solution.

The focus of this work is to create a method for the selection of the concept of on-premise IT infrastructure. The document is divided into six chapters, where the first is dedicated to brief introduction and definition of objectives of the work. The second chapter focuses on current state of the problem, which includes research of similar works and definitions of used terms. The third chapter is devoted to describing conceptions as the basis for the fourth chapter, which deals with construction of the method. The fifth chapter is devoted to the verification of the method in a real world environment and last chapter summarizes the whole thesis.