Combining SLA prediction and cross layer adaptation for preventing SLA violations

Abstract. Service-based Applications (SBA) are deployed in highly dy-namic and distributed settings, where various parts of the constituent components- services and their infrastructure- are controlled by dif-ferent third parties. In such a loosely coupled environment, adaptation capabilities are needed to manage deviations and unforeseen situations which might lead to negative consequences (e.g. contractual penalties). Current approaches either focus on cross-layer-adaptation or the pre-vention of SLA violations. In contrast to this, the approach presented in this paper combines both. The paper presents an architecture as a generic framework for the management of arising problems during ser-vice execution. Multiple adaptation mechanisms are available to react on adaptation needs, acting on different layers of the SBA (including e.g. the composition layer and the infrastructure layer). The final goal of the cross-layer adaptation capability is to avoid the violation of agreed Service Level (in SLAs) and thus ensure the benefits of SBAs for both customers and providers.

iObserve: Integrated Observation and Modeling Techniques to Support Adaptation and Evolution of Software Systems

The goal of iObserve is to develop methods and tools to support evolution and adaptation of long-lived software systems. Future long-living software systems will be engineered using third-party software services and infrastructures. Key challenges for such systems will be caused by dynamic changes of deployment options on cloud platforms. Third-party services and infrastructures are neither owned nor controlled by the users and developers of service-based systems. System users and developers are thus only able to observe third-party services and infrastructures via their interface, but are not able to look into the software and infrastructure that provides those services. In this technical report, we summarize our results of four activities to realize a complete tooling around Kieker, Palladio, and MAMBA, supporting performance and cost prediction, and the evaluation of data privacy in context of geo-locations. Furthermore, the report illustrates our efforts to extend Palladio

Run-time Architecture Models for Dynamic Adaptation and Evolution of Cloud Applications

Cloud applications are subject to continuous change due to modifications of the software application itself and, in particular, its environment. To manage changes, cloud-based systems provide diverse self-adaptation mechanisms based on run-time models. Observed run-time models are means for leveraging self- adaption, however, are hard to apply during software evolution as they are usually too detailed for comprehension by humans.In this paper, we propose iObserve, an approach to cloud-based system adaptation and evolution through run-time observation and continuous quality analysis. With iObserve, run-time adaptation and evolution are two mutual, interwoven activities that influence each other. Central to iObserve is (a) the specification of the correspondence between observation results and design models, and (b) their use in both adaptation and evolution. Run-time observation data is promoted to meaningful values mapped to design models, thereby continuously updating and calibrating those design models during run-time while keeping the models comprehendible by humans. This engineering approach allows for automated adaptation at run-time and simultaneously supports software evolution. Model-driven software engineering is employed for various purposes such as monitoring instrumentation and model transformation. We report on the experimental evaluation of this approach in lab experiments using the CoCoME benchmark deployed on an OpenStack cloud

A constraint-based approach to quality assurance in service choreographies.

Knowledge about the quality characteristics (QoS) of service com- positions is crucial for determining their usability and economic value. Ser- vice quality is usually regulated using Service Level Agreements (SLA). While end-to-end SLAs are well suited for request-reply interactions, more complex, decentralized, multiparticipant compositions (service choreographies) typ- ically involve multiple message exchanges between stateful parties and the corresponding SLAs thus encompass several cooperating parties with interde- pendent QoS. The usual approaches to determining QoS ranges structurally (which are by construction easily composable) are not applicable in this sce- nario. Additionally, the intervening SLAs may depend on the exchanged data. We present an approach to data-aware QoS assurance in choreographies through the automatic derivation of composable QoS models from partici- pant descriptions. Such models are based on a message typing system with size constraints and are derived using abstract interpretation. The models ob- tained have multiple uses including run-time prediction, adaptive participant selection, or design-time compliance checking. We also present an experimen- tal evaluation and discuss the benefits of the proposed approach

Automatic test case generation for WS-Agreements using combinatorial testing

In the scope of the applications developed under the service-based paradigm, Service Level Agreements (SLAs) are a standard mechanism used to flexibly specify the Quality of Service (QoS) that must be delivered. These agreements contain the conditions negotiated between the service provider and consumers as well as the potential penalties derived from the violation of such conditions. In this context, it is important to assure that the service based application (SBA) behaves as expected in order to avoid potential consequences like penalties or dissatisfaction between the stakeholders that have negotiated and signed the SLA. In this article we address the testing of SLAs specified using the WS-Agreement standard by means of applying testing techniques such as the Classification Tree Method and Combinatorial Testing to generate test cases. From the content of the individual terms of the SLA, we identify situations that need to be tested. We also obtain a set of constraints based on the SLA specification and the behavior of the SBA in order to guarantee the testability of the test cases. Furthermore, we define three different coverage strategies with the aim at grading the intensity of the tests. Finally, we have developed a tool named SLACT (SLA Combinatorial Testing) in order to automate the process and we have applied the whole approach to an eHealth case study

Combining SLA prediction and cross layer adaptation for preventing SLA violations

Service-based Applications (SBA) are deployed in highly dynamic and distributed settings, where various parts of the constituent components -services and their infrastructure- are controlled by different third parties. In such a loosely coupled environment, adaptation capabilities are needed to manage deviations and unforeseen situations which might lead to negative consequences (e.g. contractual penalties). Current approaches either focus on cross-layer adaptation or the prevention of SLA violations. In contrast to this, the approach presented in this paper combines both. The paper presents an architecture as a generic framework for the management of arising problems during service execution. Multiple adaptation mechanisms are available to react on adaptation needs, acting on different layers of the SBA (including e.g. the composition layer and the infrastructure layer). The nal goal of the cross-layer adaptation capability is to avoid the violation of agreed Service Level (in SLAs) and thus ensure the bene ts of SBAs for both customers and providers.Peer Reviewe