Simulating Cloud Deployment Options For Software Migration Support
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Simulating Cloud Deployment Options for Software Migration Support
Cloud computing is emerging as a promising new paradigm that aims at delivering computing resources and services on demand. To cope with the frequently found over- and under-provisioning of resources in conventional data centers, cloud computing technologies enable to rapidly scale up and down according to varying workload patterns. However, most software systems are not built for utilizing this so called elasticity and therefore must be adapted during the migration process into the cloud. Here, the selection of a specific cloud provider is the most obvious and basic cloud deployment option. Furthermore, the mapping between services and virtual machine instances must be considered when migrating to the cloud and the specific adaptation strategies, like allocating a new virtual machine instance if the CPU utilization is above a given threshold, have to be chosen and configured. The set of combinations of the given choices form a huge design space which is infeasible to test manually. The simulation of a cloud deployment option can assist in solving this problem. A simulation is often faster than executing real world experiments. Furthermore, the adaptation to the software system that shall be migrated requires less effort at a modeling layer. The simulation can be utilized by an automatic optimization algorithm to find the best ratio between high performance and low costs. Our main objective in this study is the implementation of a software that enables the simulation of cloud deployment options on a language independent basis.
Conformance Checking and Simulation-based Evolutionary Optimization for Deployment and Reconfiguration of Software in the Cloud
Many SaaS providers nowadays want to leverage the cloud’s capabilities also for their existing applications, for example, to enable sound scalability and cost-effectiveness. This thesis provides the approach CloudMIG that supports SaaS providers to migrate those applications to IaaS and PaaS-based cloud environments. CloudMIG consists of a step-by-step process and focuses on two core components. (1) Restrictions imposed by specific cloud environments (so-called cloud environment constraints (CECs)), such as a limited file system access or forbidden method calls, can be validated by an automatic conformance checking approach. (2) A cloud deployment option (CDO) determines which cloud environment, cloud resource types, deployment architecture, and runtime reconfiguration rules for exploiting a cloud’s elasticity should be used. The implied performance and costs can differ in orders of magnitude. CDOs can be automatically optimized with the help of our simulation-based genetic algorithm CDOXplorer. Extensive lab experiments and an experiment in an industrial context show CloudMIG’s applicability and the excellent performance of its two core components.
Live Trace Visualization for System and Program Comprehension in Large Software Landscapes
Author: Florian Fittkau
language: en
Publisher: BoD – Books on Demand
Release Date: 2015-12-03
In many enterprises, the number of deployed applications is constantly increasing. Those applications - often several hundreds - form large software landscapes. The comprehension of such landscapes is frequently impeded due to, for instance, architectural erosion, personnel turnover, or changing requirements. Furthermore, events such as performance anomalies can often only be understood in correlation with the states of the applications. Therefore, an efficient and effective way to comprehend such software landscapes in combination with the details of each application is required. In this thesis, we introduce a live trace visualization approach to support system and program comprehension in large software landscapes. It features two perspectives: a landscape-level perspective using UML elements and an application-level perspective following the 3D software city metaphor. Our main contributions are 1) an approach named ExplorViz for enabling live trace visualization of large software landscapes, 2) a monitoring and analysis approach capable of logging and processing the huge amount of conducted method calls in large software landscapes, and 3) display and interaction concepts for the software city metaphor beyond classical 2D displays and 2D pointing devices. Extensive lab experiments show that our monitoring and analysis approach elastically scales to large software landscapes while imposing only a low overhead on the productive systems. Furthermore, several controlled experiments demonstrate an increased efficiency and effectiveness for solving comprehension tasks when using our visualization. ExplorViz is available as open-source software on www.explorviz.net. Additionally, we provide extensive experimental packages of our evaluations to facilitate the verifiability and reproducibility of our results.