Axel Tenschert

Monitoring and Assessing Energy Consumption and CO2 Emissions in Cloud-Based Systems

The development and maintenance of cloud sites are often characterized by energy waste and high CO2 emissions. Energy efficiency and the decrease of the CO2 emissions in cloud-based systems can be only obtained by adopting suitable actions and techniques (e.g., utilization of green energy sources, reduction of the number of physical and virtual machines, usage of the greener machines). In order to evaluate the suitability of these different actions, it is necessary to define a measure for greenness of the whole system. For this reason, this paper defines a set of metrics to assess the greenness of a cloud infrastructure. In order to provide a detailed view of the behaviour of the system and to facilitate the identification of the causes of the energy waste, metrics have been defined at different layers of the system (i.e., application, virtualization, infrastructure layers). The monitoring infrastructure that is necessary to retrieve all the data required for the assessment of the identified set of metrics is also described.

Improving Energy Efficiency in Data Centers and Federated Cloud Environments: Comparison of CoolEmAll and Eco2Clouds Approaches and Metrics

Significant data centers energy footprints and the increase in energy prices have stimulated investigations into possible metrics and methods to define, quantify and improve the energy efficiency of data centers and federated cloud environments. Studies include metrics and analyses from various points of views, that address both design and operation phases. In this paper we present two complementary energy-efficiency optimization approaches covered in the scope of EU projects: CoolEmAll - with focus on building energy efficient data centers, and Eco2Clouds - with focus on energy-efficient cloud-application deployment in federated cloud-environments, and describe metrics applied in these projects to assess and optimize energy-efficiency. Both approaches make use of metrics to assess energy-efficiency of data center- and cloud resources, and energy-costs of application/workload execution for various data center granularity levels and federation-sites.

Introducing a New Scalable Data-as-a-Service Cloud Platform for Enriching Traditional Text Mining Techniques by Integrating Ontology Modelling and Natural Language Processing

A good deal of digital data produced in academia, commerce and industry is made up of a raw, unstructured text, such as Word documents, Excel tables, emails, web pages, etc., which are also often represented in a natural language. An important analytical task in a number of scientific and technological domains is to retrieve information from text data, aiming to get a deeper insight into the content represented by the data in order to obtain some useful, often not explicitly stated knowledge and facts, related to a particular domain of interest. The major challenge is the size, structural complexity, and frequency of the analysed text sets’ updates (i.e., the ‘big data’ aspect), which makes the use of traditional analysis techniques and tools impossible. We introduce an innovative approach to analyse unstructured text data. This allows for improving traditional data mining techniques by adopting algorithms from ontological domain modelling, natural language processing, and machine learning. The technique is inherently designed with parallelism in mind, which allows for high performance on large-scale Cloud computing infrastructures.

Text-Content-Analysis based on the Syntactic Correlations between Ontologies

The work presented in this chapter is concerned with the analysis of semantic knowledge structures, represented in the form of Ontologies, through which Service Level Agreements (SLAs) are enriched with new semantic data. The objective of the enrichment process is to enable SLA negotiation in a way that is much more convenient for a Service Users. For this purpose the deployment of an SLA-Management-System as well as the development of an analyzing procedure for Ontologies is required. This chapter will refer to the BREIN, the FinGrid and the LarKC projects. The analyzing procedure examines the syntactic correlations of several Ontologies whose focus lies in the field of mechanical engineering. A method of analyzing text and content is developed as part of this procedure. In order to so, we introduce a formalism as well as a method for understanding content. The analysis and methods are integrated to an SLA Management System which enables a Service User to interact with the system as a service by negotiating the user requests and including the semantic knowledge. Through negotiation between Service User and Service Provider the analysis procedure considers the user requests by extending the SLAs with semantic knowledge. Through this the economic use of an SLA-Management-System is increased by the enhancement of SLAs with semantic knowledge structures. The main focus of this chapter is the analyzing procedure, respectively the Text-Content-Analysis, which provides the mentioned semantic knowledge structures.

Evaluation of Service Level Agreement Approaches for Portfolio Management in the Financial Industry

The idea of service-oriented Grid computing seems to have the potential for fundamental paradigm change and a new architectural alignment concerning the design of IT infrastructures. There is a wide range of technical approaches from scientific communities which describe basic infrastructures and middlewares for integrating Grid resources in order that by now Grid applications are technically realizable. Hence, Grid computing needs viable business models and enhanced infrastructures to move from academic application right up to commercial application. For a commercial usage of these evolutions service level agreements are needed. The developed approaches are primary of academic interest and mostly have not been put into practice. Based on a business use case of the financial industry, five service level agreement approaches have been evaluated in this paper. Based on the evaluation, a management architecture has been designed and implemented as a prototype.