Cloud Data Management 

Abstract: Cloud-based data management system is emerging as a scalable, fault tolerant and efficient solution to large scale data management. More and more companies are moving their data management applications from expensive, high-end ser-vers to the cloud which is composed of cheaper, commodity machines. The implementations of existing cloud-based data management systems represent a wide range of approaches, including storage architectures, data models, tradeoffs in consistency and availability, etc. Several benchmarks have been proposed to evaluate the performance. However, there were no reported studies about these benchmark results which provide users with insights on the impacts of different implementation approaches on the performance. We conducted comprehensive experiments of several representative cloud-based data management systems to explore relative performance of different implementation approaches the results are valuable for further research and development of cloud-based data management systems.

Abstract: Cloud security issues have recently gained traction in the research community, with much of the focus primarily concentrated on securing the operating systems and virtual machines on which the services are deployed. In this paper, we take an alternative perspective and propose a data-centric view of cloud security. In particular, we explore the security properties of secure data sharing between applications hosted in the cloud. We discuss data management challenges in the areas of secure distributed query processing, system analysis and forensics, and query correctness assurance, and describe our current efforts towards meeting these challenges using our Declarative Secure Distributed Systems (DS2) platform.

Abstract: Benchmarking is a key activity in building and tuning data management systems, but the lack of reference workloads and a common platform makes it a time consuming and painful task. The need for such a tool is heightened with the advent of cloud computing--with its pay-per-use cost models, shared multi-tenant infrastructures, and lack of control on system configuration. Benchmarking is the only avenue for users to validate the quality of service they receive and to optimize their deployments for performance and resource utilization. In this talk, we present our experience in building several adhoc benchmarking infrastructures for various research projects targeting several OLTP DBMSs, ranging from traditional relational databases, main-memory distributed systems, and cloud-based scalable architectures. We also discuss our struggle to build meaningful micro-benchmarks and gather workloads representative of real-world applications to stress-test our systems. This experience motivates the OLTP-Bench project, a batteries-included benchmarking infrastructure designed for and tested on several relational DBMSs and cloud-based database-as-a-service (DBaaS) offerings. OLTP-Bench is capable of controlling transaction rate, mixture, and workload skew dynamically during the execution of an experiment, thus allowing the user to simulate a multitude of practical scenarios that are typically hard to test (e.g., time-evolving access skew). Moreover, the infrastructure provides an easy way to monitor performance and resource consumption of the database under test. We also introduce the ten included workloads, derived from either synthetic micro benchmarks, popular benchmarks, and real world applications, and how they can be used to investigate various performance and resource-consumption characteristics of a data management system. We showcase the effectiveness of our benchmarking infrastructure and the usefulness of the workloads we selected by reporting sample results from hundreds of side-byside comparisons on popular DBMSs and DBaaS offerings.

Abstract: Cloud Computing as a service on demand architecture has become a topic of interest in the last few years. The outsourcing of duties and infrastructure to external parties enables new services to be established quickly and with low financial risk. These services also can be scaled on demand. Nevertheless, several issues such as security and legality should be considered before entering the cloud. The financial benefits of cloud services conflict with the need to secure and control the access to outsourced information. Companies have to comply with diverse laws across jurisdictions and are accountable to various national regulators. Security requirements may not be compatible with those offered by existing providers. In this paper, we propose an architecture to facilitate the integration of these security requirements in the cloud environment and to address the legal issues attached. Our approach customizes the selection of a service provider based on the companies preference. We also define a trusted third party to handle the monitoring and auditing processes over different service providers.