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Control Theory for Computing Systems: Application to big-data cloud services & location privacy protection

Abstract : This thesis aims at investigating techniques to build and control efficient, dependableand privacy-preserving computing systems. Ad-hoc service configuration require a high level ofexpertise which could benefit from automation in many ways. A control algorithm can handle biggerand more complex systems, even when they are extremely sensitive to variations in their environment.However, applying control to computing systems raises several challenges, e.g. no physics governsthe applications. On one hand, the mathematical framework provided by control theory can be used toimprove automation and robustness of computing systems. Moreover, the control theory provides bydefinition mathematical guarantees that its objectives will be fulfilled. On the other hand, the specificchallenges of such use cases enable to expand the control theory itself. The approach taken in thiswork is to explore in details two application computing systems: location privacy and cloud services.A third use-case on the use of control for machine learning algorithm is presented in appendix. Thoseuse-cases are complementary in the nature of their technologies, scale and end-users.The widespread of mobile devices has fostered the broadcasting and collection of users’ locationdata. It enables users to benefit from a personalized service and service providers or any other thirdparty to derive useful information from the mobility databases, whereas it also exposes highly sensi-tive personal data. To overcome this privacy breach, algorithms have been developed that modify theuser’s mobility data, hopefully to hide some sensitive information, called Location Privacy ProtectionMechanisms (LPPMs). However, those tools are not easily configurable by non experts and are staticprocesses that do not adapt to the user’s mobility. We develop two tools, one for already collecteddatabases and one for online usage, that, by tuning the LPPMs, guarantee to the users objective-drivenlevels of privacy protection and of service utility preservation. First, we present an automated toolable to choose and configure LPPMs to protect already collected databases while ensuring a trade-offbetween privacy protection and database processing quality. Second, we present the first formulationof the location privacy challenge in control theory terms (plant and control, disturbance and per-formance signals), and a feedback controller to serve as a proof of concept. In both cases, design,implementation and validation has been done through experiments using data of real users.The surge in data generation of the last decades, the so-called bigdata, has lead to the developmentof frameworks able to analyze them, such as the well known MapReduce. Advances in computingpractices have also settled the cloud paradigms (online ready-to-use resources to rent) as premiumsolution for all kind of users. In this work, we focus on performance of MapReduce jobs running onclouds and thus develop advanced monitoring techniques of the jobs execution time and the platformavailability; by tuning the resource cluster size and realizing admission control, in spite of the unpre-dictable client workload. In order to deal with the non linearities of the MapReduce system, a robustadaptive feedback controller has been designed. To reduce the cluster utilization and costs, we presenta new event-based triggering mechanism formulation combined with an optimal predictive controller.Evaluation is done on a MapReduce benchmark suite running on a large-scale cluster, and using realjobs workloads.Learning algorithms are now prevalent in both the research and industry worlds. While they showimpressive results in terms of performance, other aspects has been neglected so far, such as automa-tion, robustness or privacy. Machine learning algorithms control is investigated in two complementaryways: robustness regarding noise in the dataset, and the parametrization of the algorithms, with theintroduction of feedback action. Results are validated using classic datasets and task-specific ones.
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Submitted on : Tuesday, August 27, 2019 - 3:58:07 PM
Last modification on : Tuesday, April 19, 2022 - 10:11:02 AM


  • HAL Id : tel-02272258, version 1


Sophie Cerf. Control Theory for Computing Systems: Application to big-data cloud services & location privacy protection. Systems and Control [cs.SY]. UNIVERSITÉ GRENOBLE ALPES, 2019. English. ⟨tel-02272258⟩



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