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Cloud computing promises customers the ondemand ability to scale in face of workload variations. There are different ways to accomplish scaling, one is vertical scaling and the other is horizontal scaling. The vertical scaling refers to buying more power (CPU, RAM), buying a more expensive and robust server, which is less challenging to implement but exponentially expensive. While, the horizontal scaling refers to adding more servers with less processor and RAM, which is usually cheaper overall and can scale very well. The majority of cloud providers prefer the horizontal scaling approach, and for them would be very important to know about the advantages and disadvantages of both technologies from the perspective of the application performance at scale. In this paper, we compare performance differences caused by scaling of the different virtualization technologies in terms of CPU utilization, latency, and the number of transactions per second. The workload is Apache Cassandra, which is a leading NoSQL distributed database for Big Data platforms. Our results show that running multiple instances of the Cassandra database concurrently, affected the performance of read and write operations differently; for both VMware and Docker, the maximum number of read operations was reduced when we ran several instances concurrently, whereas the maximum number of write operations increased when we ran instances concurrently.

Context. Cassandra is a NoSQL Database which handles large amount of data simultaneously and provides high availability for the data present. Compaction in Cassandra is a process of removing stale data and making data more available to the user. This thesis focusses on analyzing the impact of Cassandra compaction on Cassandra’s performance when running inside a Docker container. Objectives. In this thesis, we investigate the impact of Cassandra compaction on the database performance when it is used within a Docker based container platform. We further fine tune Cassandra’s compaction settings to arrive at a sub-optimal scenario which maximizes its performance while operating within a Docker. Methods. Literature review is performed to enlist different compaction related metrics and compaction related parameters which have an effect on Cassandra’s performance. Further, Experiments are conducted using different sets of mixed workload to estimate the impact of compaction over database performance when used within a Docker. Once these experiments are conducted, we modify compaction settings while operating under a write heavy workload and access database performance in each of these scenarios to identify a sub-optimal value of parameter for maximum database performance. Finally, we use these sub-optimal parameters to perform an experiment and access the database performance. Results. The Cassandra and Operating System related parameters and metrics which affect the Cassandra compaction are listed and their effect on Cassandra’s performance has been tested using some experiments. Based on these experiments, few sub-optimum values are proposed for the listed metrics. Conclusions. It can be concluded that, for better performance of Dockerized Cassandra, the proposed values for each of the parameters in the results (i.e. 5120 for Memtable_heap_size_in_mb, 24 for concurrent_compactors, 16 for compaction_throughput_mb_per_sec, 6 for Memtable_flush_writers and 0.14 for Memtable_cleaup _threshold) can be chosen separately but not the union of those proposed values (confirmed from the experiment performed). Also the metrics and parameters affecting Cassandra performance are listed in this thesis.