Daniel Moldovan

Learn More
Fine-grained elasticity control of cloud services has to deal with multiple elasticity perspectives (quality, cost, and resources). We propose a cloud services elasticity control mechanism that considers the service structure for controlling the cloud service elasticity at multiple levels, by firstly defining an abstract composition model for cloud services(More)
Elasticity in cloud computing is a complex problem, regarding not only resource elasticity but also quality and cost elasticity, and most importantly, the relations among the three. Therefore, existing support for controlling elasticity in complex applications, focusing solely on resource scaling, is not adequate. In this paper we present SYBL - a novel(More)
Cloud computing has enabled a wide array of applications to be exposed as elastic cloud services. While the number of such services has rapidly increased, there is a lack of techniques for supporting cross-layered multi-level monitoring and analysis of elastic service behavior. In this paper we introduce novel concepts, namely elasticity space and(More)
This paper presents a new technique to recover structure and motion from a large number of images acquired by an intrinsically calibrated perspective camera. We describe a method for computing reliable camera motion parameters that combines a camera–dependency graph, which describes the set of camera locations and the feasibility of pairwise motion(More)
Complex cloud services rely on different elasticity control processes to deal with dynamic requirement changes and workloads. However, enforcing an elasticity control process to a cloud service does not always lead to an optimal gain in terms of quality or cost, due to the complexity of service structures, deployment strategies, and underlying(More)
Platform-as-a-Service (PaaS) should support the design, deployment, execution, test and monitoring of native elastic systems constructed from elastic service units based on multi-dimensional elasticity requirements. In this paper, we discuss fundamental building blocks for enabling multi-dimensional elasticity programming of software-defined elastic(More)
In this paper we propose an energy aware dynamic consolidation algorithm for virtualized service centers based on reinforcement learning. The energy awareness is enacted by using the Energy Aware Context Model (EACM) to programmatically represent the current service center context situation by means of ontologies. We have defined the EACM model entropy(More)
This paper addresses the problem of run-time management of a service centre energy efficiency by using a context aware self-adapting algorithm. The algorithm considers the service centre current context situation and predefined Green/Key Performance Indicators (GPI/KPI) to adapt and optimize the service centre energy consumption to the incoming workload.(More)
This paper approaches the problem of improving the service center server CPU's energy efficiency by executing dynamic frequency scaling actions and performing tradeoffs between CPU's computational performance and its power consumption. Two different algorithms are designed and implemented: an immune inspired algorithm and a fuzzy logic based algorithm. The(More)
This paper addresses the problem of run-time management of a service center energy efficiency by using a context aware self-adapting algorithm. The algorithm adapts the service center energy consumption to the incoming workload by considering service center predefined Green Performance Indicators (GPI) and Key Performance Indicators (KPI). The service(More)