A Framework for Requirements Decomposition, SLA Management and Dynamic System Reconfiguration

To meet user requirements, systems can be built from Commercial-Off-The-Shelf (COTS) components, potentially from different vendors. However, the gap between the requirements referring to the overall system and the components to build the system from can be large. To close the gap, it is required to decompose the requirements to a level where they can be mapped to components. When the designed system is deployed and ready for operations, its services are sold and pro-vided to customers. One important goal for service providers is to optimize system resource utilization while ensuring the quality of service expressed in the Service Level Agreements (SLAs). For this purpose, the system can be reconfigured dynamically according to the cur-rent workload to satisfy the SLAs while using only necessary resources. To manage the re-configuration of the system at runtime, a set of previously defined patterns called elasticity rules can be used. In elasticity rules, the actions that need to be taken to reconfigure the sys-tem are specified. An elasticity rule is generally invoked by a trigger, which is generated in reaction to a monitoring event. In this thesis, we propose a model-driven management framework which aims at user re-quirements satisfaction, SLA compliance management and enabling dynamic reconfiguration by reusing the design information at runtime. An approach has been developed to derive automatically a valid configuration starting from low level requirements called service configurations. However, the service configurations are far from requirements a user would express. To generate a system configuration from user requirements and alleviate the work of designer, we generate service configurations by de-composing functional user requirements to the level where components can be selected and put together to satisfy the user requirements. We integrated our service configurations gen-erator with the previous configuration generator. In our framework, we reuse the information acquired from system configuration and dimen-sioning to generate elasticity rules offline. We propose a model driven approach to check the compliance of SLAs and generate triggers for invoking applicable elasticity rules when system reconfiguration is required. For handling multiple triggers generated at the same time, we propose a solution to automatically correlate the actions of invoked elasticity rules, when re-quired. The framework consists of a number of metamodels and a set of model transfor-mations. We use the Unified Modeling Language (UML) and its profiling mechanism to de-scribe all the artifacts in the proposed framework. We implement the profiles using Eclipse Modeling Framework (EMF) and Papyrus. To implement the processes, we use the Atlas Transformation Language (ATL). We also use the APIs of the Object Constraint Language (OCL) in the Eclipse environment to develop a tool for checking constraints and generating triggers

A Model-based Framework for System Configuration Management

A system can be viewed from different perspectives, each focusing on a specific aspect such as availability, performance, security. Configurations reflect the manageable resources of the system, their attributes and organization which are necessary for the management of the system for each aspect. Thus, for management purposes a system is generally described through various partial configurations (also known as configuration fragments). To form a consistent system con-figuration, these independently developed configuration fragments need to be integrated together. The integration of configuration fragments is a challenging task. This is mainly due to over-lapping entities (different logical representations of the same system resource) in the configuration fragments and/or complex relationships among the entities of the different configuration fragments. At runtime the system may be reconfigured to meet certain/new requirements or in response to performance degradations. These changes may lead to inconsistency as some changes may violate the constraints between entities. Maintaining the consistency and adjusting the system configuration at runtime is another challenging task. In our research, we propose to handle these two important issues in an integrated manner. We define a model-based framework for configuration management. We use the Unified Modeling Language (UML) and its profiling mechanism for representing the configuration fragments. Using model weaving and model trans-formation techniques, we propose a solution for the integration of configuration fragments targeting specific system properties. To handle runtime changes, we propose a configuration validation and adjustment solution to check and preserve the consistency of the system configuration. We introduce a partial validation technique in which the runtime reconfigurations are checked against a reduced set of consistency rules instead of the complete set of rules and the reconfigurations are applied only if they are safe, i.e. they preserve the configuration consistency. For handling the changes that violate the consistency rules, we propose an adjustment technique to automatically resolve (if possible) the inconsistencies. This is achieved by propagating the changes in the configuration according to the system constraints following the possible impacts of the configuration entities on each other. Some heuristics are used to reduce the complementary changes and to limit the propagation. We evaluate the complexity of our adjustment technique and conduct experiments to evaluate its efficiency. The Service Availability Forum middleware is used as an application domain in the examples throughout this thesis; however the proposed solutions are applicable in more general settings. We present proofs of concepts using different technologies. We use the Eclipse Modeling Framework (EMF) and Papyrus for implementing the UML profiles. The Atlas Model Weaver (AMW) and Atlas Transformation Language (ATL) are used to integrate the configuration fragments, and we also use the APIs of the Object Constraint Language (OCL) in the Eclipse environment and the Microsoft Z3 constraint solver to develop a prototype tool of our partial validation and adjustment agents

Automating the Upgrade of IaaS Cloud Systems

The different resources providing an Infrastructure as a Service (IaaS) cloud service may need to be upgraded several times throughout their life-cycle for different reasons, for instance to fix discovered bugs, to add new features, or to fix a security threat. An IaaS cloud provider is committed to each tenant by a service level agreement (SLA) which indicates the terms of commitment, e.g. the level of availability, that have to be respected even during upgrades. However, the service delivered by the IaaS cloud provider may be affected during the up-grade. Subsequently, this may violate the SLA, which in turn will impact other services rely-ing on the IaaS. Our goal in this thesis is to devise an approach and a framework for automat-ing the upgrade of IaaS cloud systems with minimal impact on the services and with respect to the SLAs. The upgrade of IaaS cloud systems under availability constraints inherits all the challenges of the upgrade of traditional clustered systems and faces other cloud specific challenges. Similar challenges as in clustered systems include the potential dependencies between resources, po-tential incompatibilities along dependencies during the upgrade, potential system configura-tion inconsistencies due to the upgrade failures and the minimization of the amount of used resources to complete the upgrade. Dependencies of the application layer on the IaaS layer is an added challenge that must be handled properly. In addition, the dynamic nature of the cloud environment poses a new challenge. A cloud system evolves, even during the upgrade, according to the workload changes by scaling in/out. This mechanism (referred to as autoscal-ing) may interfere with the upgrade process in different ways. In this thesis, we define an upgrade management framework for the upgrade of IaaS cloud systems under SLA constraints. This framework addresses all the aforementioned challenges in an integrated manner. The proposed framework automatically upgrades an IaaS cloud sys-tem from a current configuration to a desired one, according to the upgrade requests specified by the administrator. It consists of two distinct components, one to coordinate the upgrade, and the other one to execute the necessary upgrade actions on the infrastructure resources. For the coordination of the upgrade process, we propose a new approach to automatically identify and schedule the appropriate upgrade methods and actions for implementing the up-grade requests in an iterative manner taking into account the vendors’ descriptions of the in-frastructure components, the SLAs with the tenants, and the status of the system. This ap-proach is also capable of handling new upgrade requests even during ongoing upgrades, which makes it suitable for continuous delivery. In case of failures, the proposed approach automatically issues localized retry and undo recovery operations as appropriate for the failed upgrade actions to preserve the consistency of the system configuration. In this thesis, to demonstrate the feasibility of the proposed upgrade management framework we present a proof of concept (PoC) for the upgrade IaaS compute, and its application in an OpenStack cluster. In this PoC, we target the new challenge of upgrade of the IaaS cloud (i.e. unexpected interference between the autoscaling and the upgrade processes) compared to the clustered systems. In addition, the prototype of the proposed upgrade approach for coordinat-ing the upgrade of all kinds of IaaS resources has been implemented and discussed in this thesis. We also provide an informal validation and a rigorous analysis of the main properties of our approach. In addition, we conduct experiments to evaluate our approach with respect to SLA constraints of availability and elasticity. The results show that our approach avoids the outage at the application level and reduces SLA violations during the upgrade, compared to the traditional upgrade method used by cloud providers