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Model-Driven Online Capacity Management for Component-Based Software Systems
Capacity management is a core activity when designing and operating distributed software systems. Particularly, enterprise application systems are exposed to highly varying workloads. Employing static capacity management, this leads to unnecessarily high total cost of ownership due to poor resource usage efficiency. This thesis introduces a model-driven online capacity management approach for distributed component-based software systems, called SLAstic. The core contributions of this approach are a) modeling languages to capture relevant architectural information about a controlled software system, b) an architecture-based online capacity management framework based on the common MAPE-K control loop architecture, c) model-driven techniques supporting the automation of the approach, d) architectural runtime reconfiguration operations for controlling a system’s capacity, as well as e) an integration of the Palladio Component Model. A qualitative and quantitative evaluation of the approach is performed by case studies, lab experiments, and simulation.
HPI Future SOC Lab – Proceedings 2018
The “HPI Future SOC Lab” is a cooperation of the Hasso Plattner Institute (HPI) and industry partners. Its mission is to enable and promote exchange and interaction between the research community and the industry partners. The HPI Future SOC Lab provides researchers with free of charge access to a complete infrastructure of state of the art hard and software. This infrastructure includes components, which might be too expensive for an ordinary research environment, such as servers with up to 64 cores and 2 TB main memory. The offerings address researchers particularly from but not limited to the areas of computer science and business information systems. Main areas of research include cloud computing, parallelization, and In-Memory technologies. This technical report presents results of research projects executed in 2018. Selected projects have presented their results on April 17th and November 14th 2017 at the Future SOC Lab Day events.
Simulating Cloud Deployment Options for Software Migration Support
Cloud computing is emerging as a promising new paradigm that aims at delivering computing resources and services on demand. To cope with the frequently found over- and under-provisioning of resources in conventional data centers, cloud computing technologies enable to rapidly scale up and down according to varying workload patterns. However, most software systems are not built for utilizing this so called elasticity and therefore must be adapted during the migration process into the cloud. A challenge during migration is the high number of different possibilities for the deployment to cloud computing resources. For example, there exist a plethora of potential cloud provider candidates. Here,...
Multinational Enterprises, Markets and Institutional Diversity
This research volume in honour of the late Daniel Van Den Bulcke, one of the founding fathers of the European Business Academy (EIBA) and a core institution builder of the Academy of International Business (AIB), focuses on conceptual innovations in assessing the impact of institutions on multinational enterprise (MNE) strategies.
HPI Future SOC Lab
Das Future SOC Lab am HPI ist eine Kooperation des Hasso-Plattner-Instituts mit verschiedenen Industriepartnern. Seine Aufgabe ist die Ermöglichung und Förderung des Austausches zwischen Forschungsgemeinschaft und Industrie. Am Lab wird interessierten Wissenschaftlern eine Infrastruktur von neuester Hard- und Software kostenfrei für Forschungszwecke zur Verfügung gestellt. Dazu zählen teilweise noch nicht am Markt verfügbare Technologien, die im normalen Hochschulbereich in der Regel nicht zu finanzieren wären, bspw. Server mit bis zu 64 Cores und 2 TB Hauptspeicher. Diese Angebote richten sich insbesondere an Wissenschaftler in den Gebieten Informatik und Wirtschaftsinformatik. Einig...
Performance Benchmarking of Application Monitoring Frameworks
Application-level monitoring of continuously operating software systems provides insights into their dynamic behavior, helping to maintain their performance and availability during runtime. Such monitoring may cause a significant runtime overhead to the monitored system, depending on the number and location of used instrumentation probes. In order to improve a system’s instrumentation and to reduce the caused monitoring overhead, it is necessary to know the performance impact of each probe. While many monitoring frameworks are claiming to have minimal impact on the performance, these claims are often not backed up with a detailed performance evaluation determining the actual cost of monito...
