Towards providing lightweight access to legacy applications as cloud-based services

Software as a Service solutions are available for certain business applications. However, many companies still rely on complex legacy applications for business-critical tasks. They cannot easily be accessed via the Internet and integrated into service-oriented landscapes. Re-programming or adaption of these legacy applications is both time-consuming and expensive. Remote access does not allow deep integration with other services and relies on proprietary software. We therefore propose the generic black-box approach REFLECTION (Refurbishing legacy applications) to dynamically rebuild the user interface of applications using native Web technologies. Users can thereby access these applications on-demand as cloud-based services. We also discuss usage scenarios, design and architecture considerations, as well as technical challenges

A middleware guaranteeing client-centric consistency on top of eventually consistent datastores

Abstract-Applications often have consistency requirements beyond those guaranteed by the underlying eventually consistent storage system. In this work, we present an approach that guarantees monotonic read consistency and read your writes consistency by running a special middleware component on the same server as the application. We evaluate our approach using both simulation and real world experiments on Cloud storage systems

Benchmarking Scalability and Elasticity of Distributed Database Systems

Distributed database system performance benchmarks are an important source of information for decision makers who must select the right technology for their data management problems. Since important decisions rely on trustworthy experimental data, it is necessary to reproduce experiments and verify the results. We reproduce performance and scalability benchmarking experiments of HBase and Cassandra that have been conducted by previous research and compare the results. The scope of our reproduced experiments is extended with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations, and an evaluation of Cassandra and HBase elasticity by measuring scaling speed and performance impact while scaling. 1