Simulating Cloud Deployment Options for Software Migration Support
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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. Fu...
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.
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.
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