A Data Generator for Cloud-Scale Benchmarking. Rabl, Tilmann; Frank, Michael; Sergieh, Hatem Mousselly; Kosch, Harald (2010). 41–56.
In many fields of research and business data sizes are breaking the petabyte barrier. This imposes new problems and research possibilities for the database community. Usually, data of this size is stored in large clusters or clouds. Although clouds have become very popular in recent years, there is only little work on benchmarking cloud applications. In this paper we present a data generator for cloud sized applications. Its architecture makes the data generator easy to extend and to configure. A key feature is the high degree of parallelism that allows linear scaling for arbitrary numbers of nodes. We show how distributions, relationships and dependencies in data can be computed in parallel with linear speed up.
Introducing Scalileo: A Java Based Scaling Framework. Rabl, Tilmann; Dellwo, Christian; Kosch, Harald (2010). 205–214.
Scalability is a major concern of internet based applications. Access peaks that overload the application are a financial risk. Therefore, systems are built to scale. They are usually configured to be able to process peaks at any give moment. This can be very inefficient. Yet, there are various ways to improve efficiency. One reasonable approach is to scale applications according to their current workload. This requires the possibility to scale a system up and down. In this paper we present an scaling framework for Java applications. It allows not only autonomic scaling, but also migration of distributed applications. We will then show how energy efficiency can be increased by scaling applications. To present an example we have used our framework to autonomically scale a web server cluster.
