No abstract available.

Energy management has become a significant concern in data centers to reduce operational costs and maintain systems' reliability. Using virtualization allows server consolidation, which increases server utilization and reduces energy consumption by turning off unused servers. However, server consolidation and turning off servers can cause also consequences if they are not exploited efficiently. For instance, many researchers consider a deterministic demand for capacity planning, but the demand is always subject to uncertainty. This uncertainty is an outcome of the workload prediction and the workload fluctuation. This paper presents a robust optimization for proactive capacity planning. We do not presume that the demand of VMs is deterministic. Thus, we implement a range prediction approach instead of a single point prediction. Then, we implement a robust optimization model exploiting the range-based prediction to determine the number of active servers for each capacity planning period. The results of the simulation show that our approach can mitigate undesirable changes in the power-state of the servers. Additionally, the results indicate an increase in the servers' availability for hosting new VMs and reliability against a system failure during power-state changes. As future work, we intend to apply our approach to dynamic workload such as a web application. We plan to investigate applying our approach to other resources, where we consider only the CPU demand of VMs. Finally, we compare our approach against the approaches using stochastic optimization.

Online transaction processing (OLTP) and online analytical processing (OLAP) are thought of as two separate domains, despite sharing the same business data to operate on. This is the result of performance impairments encountered in the past when running on the same system, the workloads becoming ever more sophisticated, leading to contradictory optimization in database design. Recent developments in hardware and database systems are bringing forth research prototypes supporting mixed OLTP and OLAP workloads, challenging this separation. At the same time new benchmarks are proposed to assess these mixed workload systems. In the demonstration, we show an interactive performance monitor and benchmark driver developed for the Composite Benchmark for Transaction Processing and Reporting. The performance monitor allows us to directly determine the impact of changing shares within the workload and to interactively assess behavioral characteristics of different database systems under changing mixed workload conditions.

Event-based systems (EBS) are increasingly used as underlying technology in many mission critical areas and large-scale environments, such as environmental monitoring and location-based services [3]. Moreover, novel event-based applications are typically highly distributed and data intensive with stringent requirements for performance and scalability. Since their reliability is crucial for the whole IT infrastructure, a certain Quality-of-Service (QoS) level has to be ensured. The motivation for our work was to support the development and maintenance of EBS that meet their QoS requirements. Given that EBS differ from traditional software in fundamental aspects such as their underlying communications paradigm, specific solutions and concepts are needed. System architects and deployers need tools and methodologies, which allow them to evaluate and forecast system performance and behavior in certain situations to identify potential performance problems and bottlenecks. Common approaches are benchmarking and performance modeling. However, no general performance modeling methodologies focusing on EBS had been published. Furthermore, there was a lack of test harnesses and benchmarks using representative workloads for EBS. Consequently, we focused on the development of a performance modeling methodology of EBS as well as on approaches to benchmark them. We summarize now our main contributions and proposed approaches.

Current database benchmarks are either focusing on online transaction processing (OLTP) or on online analytical pro- cessing (OLAP) systems. This traditional separation has to be reevaluated to reflect current trends in the design of database systems. We see a need for a realistic benchmark workload taking both aspects into account. Therefore, we defined a mixed workload and illustrate ways to apply our workload to evaluate the influence of database design on system performance.

Distributed event-based systems (DEBS) are gaining increasing attention in new application areas such as transport information monitoring, event-driven supply-chain management and ubiquitous sensor-rich environments. However, as DEBS increasingly enter the enterprise and commercial domains, performance and quality of service issues are becoming a major concern. While numerous approaches to performance modeling and evaluation of conventional request/reply-based distributed systems are available in the literature, no general approach exists for DEBS. This paper is the first to provide a comprehensive methodology for workload characterization and performance modeling of DEBS. A workload model of a generic DEBS is developed and operational analysis techniques are used to characterize the system traffic and derive an approximation for the mean event delivery latency. Following this, a modeling technique is presented that can be used for accurate performance prediction. The paper is concluded with a case study of a real life system demonstrating the effectiveness and practicality of the proposed approach.

The usage of RFID and sensing technologies in supply chain management applications requires the automatic conversion of large amounts of raw data into manageable business process information. This has led to many performance and scalability issues in existing RFID infrastructures. We present an approach to alleviate these shortcomings based on a flexible system architecture that partially migrates business data processing towards the periphery.