Analyzing Real Cluster Data for Formulating Allocation Algorithms in Cloud Platforms

Olivier Beaumont 1, 2 Lionel Eyraud-Dubois 2, 3 Juan-Angel Lorenzo-Del-Castillo 3
1 Realopt - Reformulations based algorithms for Combinatorial Optimization
LaBRI - Laboratoire Bordelais de Recherche en Informatique, IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
3 CEPAGE - Algorithmics for computationally intensive applications over wide scale distributed platforms
Université Sciences et Technologies - Bordeaux 1, Inria Bordeaux - Sud-Ouest, École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), CNRS - Centre National de la Recherche Scientifique : UMR5800
Abstract : A problem commonly faced in Computer Science research is the lack of real usage data that can be used for the validation of algorithms. This situation is particularly true and crucial in Cloud Computing. The privacy of data managed by commercial Cloud infrastructures, together with their massive scale, make them very uncommon to be available to the research community. Due to their scale, when designing resource allocation algorithms for Cloud infrastructures, many assumptions must be made in order to make the problem tractable. This paper provides deep analysis of a cluster data trace recently released by Google and focuses on a number of questions which have not been addressed in previous studies. In particular, we describe the characteristics of job resource usage in terms of dynamics (how it varies with time), of correlation between jobs (identify daily and/or weekly patterns), and correlation inside jobs between the different resources (dependence of memory usage on CPU usage). From this analysis, we propose a way to formalize the allocation problem on such platforms, which encompasses most job features from the trace with a small set of parameters.
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Olivier Beaumont, Lionel Eyraud-Dubois, Juan-Angel Lorenzo-Del-Castillo. Analyzing Real Cluster Data for Formulating Allocation Algorithms in Cloud Platforms. Proceedings of the IEEE 26th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Oct 2014, Paris, France. pp.302 - 309, ⟨10.1109/SBAC-PAD.2014.44⟩. ⟨hal-01094388⟩

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