%0 Thesis %1 Weber2014 %A Weber, Andreas %C Am Fasanengarten 5, 76131 Karlsruhe, Germany %D 2014 %K Metrics_and_benchmarking_methodologies Self-adaptive-systems LIMBO Performance Virtualization BUNGEE Supervised_by_Nikolas_Herbst Cloud descartes Thesis_supervised_by_SE_member Tool Elasticity se2_mastersthesis %T Resource Elasticity Benchmarking in Cloud Environments %X Auto-scaling features offered by today's cloud infrastructures provide increased flexibility, especially for customers that experience high variations in the load intensity over time. However, auto-scaling features introduce new system quality attributes when considering their accuracy and timing. Therefore, distinguishing between different offerings has become a complex task, as it is not yet supported by reliable metrics and measurement approaches. This thesis discusses the shortcomings of existing approaches for measuring and evaluating elastic behavior and proposes a novel benchmark methodology specifically designed for evaluating the elasticity aspects of modern cloud platforms. The benchmarking concept uses open workloads with realistic load intensity profiles in order to induce resource demand variations on the benchmarked system and compares them with the actual variation of the allocated resources. To ensure a fair elasticity comparison between systems with di erent underlying hardware performance, the load intensity profiles are adjusted to induce identical resource demand variations on all compared platforms. Furthermore, this thesis proposes new metrics that capture the accuracy of resource allocations and deallocations, as well as the timing aspects of an auto-scaling mechanism, explicitly. The benchmark concept comprises four activities: The System Analysis evaluates the load processing capabilities of the benchmarked platform for different scaling stages. The Benchmark Calibration then uses the analysis results and adjusts a given load intensity profile in a system specific manner. Within the Measurement activity, the evaluated platformis exposed to a load varying ccording to the adjusted intensity profile. The final Elasticity Evaluation measures the quality of the observed elastic behavior using the proposed elasticity metrics. A java based framework for benchmarking the elasticity of IaaS cloud platforms called BUNGEE implements this concept and automates benchmarking activities. At the moment, BUNGEE allows to analyze the elasticity of CloudStack and Amazon Web Service (AWS) based clouds that scale CPU-bound virtual machines horizontally. Within an extensive evaluation, this thesis demonstrates the ability of the proposed elasticity metrics to consistently rank elastic systems on an ordinal scale. A case study that uses a realistic load profile, consisting of several millions of request submissions, exhibits the applicability of the benchmarking methodology for realistic scenarios. The case study is conducted on a private as well as on a public cloud and uses eleven different elasticity rule configurations and four instance types assigned to resources with different levels of effciency.

@mastersthesis{Weber2014, abstract = {{Auto-scaling features offered by today's cloud infrastructures provide increased flexibility, especially for customers that experience high variations in the load intensity over time. However, auto-scaling features introduce new system quality attributes when considering their accuracy and timing. Therefore, distinguishing between different offerings has become a complex task, as it is not yet supported by reliable metrics and measurement approaches. This thesis discusses the shortcomings of existing approaches for measuring and evaluating elastic behavior and proposes a novel benchmark methodology specifically designed for evaluating the elasticity aspects of modern cloud platforms. The benchmarking concept uses open workloads with realistic load intensity profiles in order to induce resource demand variations on the benchmarked system and compares them with the actual variation of the allocated resources. To ensure a fair elasticity comparison between systems with di erent underlying hardware performance, the load intensity profiles are adjusted to induce identical resource demand variations on all compared platforms. Furthermore, this thesis proposes new metrics that capture the accuracy of resource allocations and deallocations, as well as the timing aspects of an auto-scaling mechanism, explicitly. The benchmark concept comprises four activities: The System Analysis evaluates the load processing capabilities of the benchmarked platform for different scaling stages. The Benchmark Calibration then uses the analysis results and adjusts a given load intensity profile in a system specific manner. Within the Measurement activity, the evaluated platformis exposed to a load varying ccording to the adjusted intensity profile. The final Elasticity Evaluation measures the quality of the observed elastic behavior using the proposed elasticity metrics. A java based framework for benchmarking the elasticity of IaaS cloud platforms called BUNGEE implements this concept and automates benchmarking activities. At the moment, BUNGEE allows to analyze the elasticity of CloudStack and Amazon Web Service (AWS) based clouds that scale CPU-bound virtual machines horizontally. Within an extensive evaluation, this thesis demonstrates the ability of the proposed elasticity metrics to consistently rank elastic systems on an ordinal scale. A case study that uses a realistic load profile, consisting of several millions of request submissions, exhibits the applicability of the benchmarking methodology for realistic scenarios. The case study is conducted on a private as well as on a public cloud and uses eleven different elasticity rule configurations and four instance types assigned to resources with different levels of effciency.}}, added-at = {2020-04-09T08:40:50.000+0200}, address = {Am Fasanengarten 5, 76131 Karlsruhe, Germany}, author = {Weber, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2799ac77d72666f2ac74b54da071209b6/se-group}, interhash = {bd3ac2ff72ba79114fa8abd4c6ce5548}, intrahash = {799ac77d72666f2ac74b54da071209b6}, keywords = {Metrics_and_benchmarking_methodologies Self-adaptive-systems LIMBO Performance Virtualization BUNGEE Supervised_by_Nikolas_Herbst Cloud descartes Thesis_supervised_by_SE_member Tool Elasticity se2_mastersthesis}, month = {August}, school = {{Karlsruhe Institute of Technology (KIT)}}, timestamp = {2020-05-13T11:57:04.000+0200}, title = {{Resource Elasticity Benchmarking in Cloud Environments}}, type = {{Master Thesis}}, year = 2014 }