QoS-based Self-Management for Business Processes

Business processes are commonly implemented as compositions of Web Services, using the Business Process Execution Language (BPEL) as an orchestration specification. Business processes do not only require an appropriate setup but also need to be monitored throughout their runtime, especially when Quality-of-service (QoS) constraints have to be met. Monitoring results may be used for the automated reconfiguration and optimization of business processes. We show how we achieve self-management based on QoS constraints within our system. The BPRules Language that we set up can be used to improve the QoS behavior of business processes by triggering appropriate management actions on the process. Also we propose a service selection strategy for the dynamic selection and replacement of services within business processes

A coordination protocol for user-customisable cloud policy monitoring

Cloud computing will see a increasing demand for end-user customisation and personalisation of multi-tenant cloud service offerings. Combined with an identified need to address QoS and governance aspects in cloud computing, a need to provide user-customised QoS and governance policy management and monitoring as part of an SLA management infrastructure for clouds arises. We propose a user-customisable policy definition solution that can be enforced in multi-tenant cloud offerings through an automated instrumentation and monitoring technique. We in particular allow service processes that are run by cloud and SaaS providers to be made policy-aware in a transparent way

Towards Autonomic Service Provisioning Systems

This paper discusses our experience in building SPIRE, an autonomic system for service provision. The architecture consists of a set of hosted Web Services subject to QoS constraints, and a certain number of servers used to run session-based traffic. Customers pay for having their jobs run, but require in turn certain quality guarantees: there are different SLAs specifying charges for running jobs and penalties for failing to meet promised performance metrics. The system is driven by an utility function, aiming at optimizing the average earned revenue per unit time. Demand and performance statistics are collected, while traffic parameters are estimated in order to make dynamic decisions concerning server allocation and admission control. Different utility functions are introduced and a number of experiments aiming at testing their performance are discussed. Results show that revenues can be dramatically improved by imposing suitable conditions for accepting incoming traffic; the proposed system performs well under different traffic settings, and it successfully adapts to changes in the operating environment.Comment: 11 pages, 9 Figures, http://www.wipo.int/pctdb/en/wo.jsp?WO=201002636