%0 Journal Article %1 KiHeKo-TAAS17-ModelExtractLoadProfiles %A von Kistowski, Jóakim %A Herbst, Nikolas %A Kounev, Samuel %A Groenda, Henning %A Stier, Christian %A Lehrig, Sebastian %C New York, NY, USA %D 2017 %I ACM %J ACM Transactions on Autonomous and Adaptive Systems (TAAS) %K %N 4 %P 23:1--23:28 %T Modeling and Extracting Load Intensity Profiles %U http://doi.acm.org/10.1145/3019596 %V 11 %X Today's system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Autonomic controllers, e.g., an advanced auto-scaling mechanism in a cloud computing context, can benefit from an abstracted load model as knowledge to reconfigure on time and precisely. Existing workload characterization approaches have limited support to capture variations the inter-arrival times of incoming work units over time (i.e., a variable load profile). For example, industrial and scientific benchmarks support constant or stepwise increasing load, or inter-arrival times defined by statistical distributions or recorded traces. These options show shortcomings either in representative character of load variation patterns or in abstraction and flexibility of their format. In this article, we present the Descartes Load Intensity Model (DLIM) approach addressing these issues. DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance is a compact formal description of a load intensity trace. DLIM-based tools provide features for benchmarking, performance and recorded load intensity trace analysis. As manually obtaining and maintaining DLIM instances becomes time consuming, we contribute three automated extraction methods and devised metrics for comparison and method selection. We discuss how these features are used to enhance system management approaches for adaptations during run-time, and how they are integrated into simulation contexts and enable benchmarking of elastic or adaptive behavior. We show that automatically extracted DLIM instances exhibit an average modeling error of 15.2\% over ten different real-world traces that cover between two weeks and seven months. These results underline DLIM model expressiveness. In terms of accuracy and processing speed, our proposed extraction methods for the descriptive models are comparable to existing time series decomposition methods. Additionally, we illustrate DLIM applicability by outlining approaches of workload modeling in systems engineering that employ or rely on our proposed load intensity modeling formalism.

@article{KiHeKo-TAAS17-ModelExtractLoadProfiles, abstract = {Today's system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Autonomic controllers, e.g., an advanced auto-scaling mechanism in a cloud computing context, can benefit from an abstracted load model as knowledge to reconfigure on time and precisely. Existing workload characterization approaches have limited support to capture variations the inter-arrival times of incoming work units over time (i.e., a variable load profile). For example, industrial and scientific benchmarks support constant or stepwise increasing load, or inter-arrival times defined by statistical distributions or recorded traces. These options show shortcomings either in representative character of load variation patterns or in abstraction and flexibility of their format. In this article, we present the Descartes Load Intensity Model (DLIM) approach addressing these issues. DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance is a compact formal description of a load intensity trace. DLIM-based tools provide features for benchmarking, performance and recorded load intensity trace analysis. As manually obtaining and maintaining DLIM instances becomes time consuming, we contribute three automated extraction methods and devised metrics for comparison and method selection. We discuss how these features are used to enhance system management approaches for adaptations during run-time, and how they are integrated into simulation contexts and enable benchmarking of elastic or adaptive behavior. We show that automatically extracted DLIM instances exhibit an average modeling error of 15.2\% over ten different real-world traces that cover between two weeks and seven months. These results underline DLIM model expressiveness. In terms of accuracy and processing speed, our proposed extraction methods for the descriptive models are comparable to existing time series decomposition methods. Additionally, we illustrate DLIM applicability by outlining approaches of workload modeling in systems engineering that employ or rely on our proposed load intensity modeling formalism.}, added-at = {2023-12-13T08:04:41.000+0100}, address = {New York, NY, USA}, author = {von Kistowski, J{\'o}akim and Herbst, Nikolas and Kounev, Samuel and Groenda, Henning and Stier, Christian and Lehrig, Sebastian}, biburl = {https://www.bibsonomy.org/bibtex/2834dd7adbdfd8c73274eac23f646d933/admin}, interhash = {c076c7df8919c7bf29940d6dd20ca3ef}, intrahash = {834dd7adbdfd8c73274eac23f646d933}, journal = {ACM Transactions on Autonomous and Adaptive Systems (TAAS)}, keywords = {}, month = {January}, number = 4, pages = {23:1--23:28}, publisher = {ACM}, timestamp = {2023-12-13T08:04:41.000+0100}, title = {{Modeling and Extracting Load Intensity Profiles}}, url = {http://doi.acm.org/10.1145/3019596}, volume = 11, year = 2017 }