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High Performance Computing (HPC) systems are subject to dynamical variations occurring in, e.g., jobs execution duration, I/O quantity, network consumption. Adapting to these unpredictable variations requires using autonomic management in an online feedback loop. The introduction of control theory methods allows for the design of well-founded autonomic managers. Choosing the relevant approach is daunting due to the variety of existing controllers. The criteria are of different natures, involving performance and efficiency, but also required expertise in control theory, and reusability or portability between sub-systems. Therefore, there is a need for comparative studies to assist designers choices. We consider the problem of resource harvesting in HPC systems, where scheduling often leaves resources idle. Our approach controls—through a feedback loop—the injection of small jobs in order to maximize the resources’ usage. The control problem is to manage the tradeoff between harvesting and performance, in a reusable manner. We study how reusability relates to the adaptivity and robustness properties in control. We illustrate our approach with the classic Proportional-Integral-Derivative (PID) control, its upgrade as adaptive control, and Model-Free Control (MFC). We target CiGri , a system harvesting idle resources in a computing grid. We perform experimental evaluation and compare performance and reusability. Tradeoffs are found on different criteria: While adaptive control is largely portable, its design complexity is significant for non-experts; PID control has good nominal performance, yet its portability is limited; MFC requires few competences to be used, but cannot provide strong guarantees.

The increasing dependence on Location-Based Services (LBS) raises significant concerns about protecting user privacy while maintaining data utility. Among emerging solutions, synthetic trajectory generation offers a promising approach to enable data sharing without exposing sensitive location information. This paper introduces APU-TrajGen, an adaptive privacy and utility-preserving method for real-time synthetic trajectory generation. Built on an LSTM-based model, the approach dynamically generates synthetic points directly on the user’s device. The method ensures that the generated trajectories meet the targeted privacy-utility balance. Extensive experiments conducted on the Porto Taxi dataset demonstrate the model’s effectiveness in preserving global, trajectory-level, and semantic-level data utility while providing data privacy. Compared to state-of-the-art methods, APU-TrajGen provides a more flexible and balanced trade-of between privacy and utility, enabling the control over privacy levels without compromising analytical value. Furthermore, the decentralized, real-time approach reduces the risks associated with central data collection. Overall, APU-TrajGen advances the state of the art in privacy-preserving trajectory generation by supporting efficient point-by-point data release, reducing computational overhead, and removing the dependency on trusted third-party processors.

The widespread use of mobile devices and location-based services has generated a large number of mobility databases. While processing these data is highly valuable, privacy issues can occur if personal information is revealed. The prior art has investigated ways to protect mobility data by providing a wide range of Location Privacy Protection Mechanisms (LPPMs). However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful processing. To tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and automatically configures it for each user in order to achieve user-defined objectives in terms of both privacy and utility. PULP uses nonlinear models to capture the impact of each LPPM on data privacy and utility levels. Evaluation of our framework is carried out with two protection mechanisms from the literature and four real-world mobility datasets. Results show the efficiency of PULP, its robustness and adaptability. Comparisons between LPPMs' configurators and the state of the art further illustrate that PULP better realizes users' objectives, and its computation time is in orders of magnitude faster.

Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT and cloud, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the field can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this paper, we present a two-layer learning framework for robust anomaly detection (RAD) in the presence of unreliable anomaly labels. The first layer of quality model filters the suspicious data, where the second layer of classification model detects the anomaly types. We specifically focus on two use cases, (i) detecting 10 classes of IoT attacks and (ii) predicting 4 classes of task failures of big data jobs. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98% for IoT device attacks (i.e., +11%) and up to 83% for cloud task failures (i.e., +20%), under a significant percentage of altered anomaly labels.

Leveraging location information in location-based services leads to improving service utility through geocontextualization. However, this raises privacy concerns as new knowledge can be inferred from location records, such as user's home and work places, or personal habits. Although Location Privacy Protection Mechanisms (LPPMs) provide a means to tackle this problem, they often require manual configuration posing significant challenges to service providers and users. Moreover, their impact on data privacy and utility is seldom assessed. In this paper, we present PULP, a model-driven system which automatically provides user-specific privacy protection and contributes to service utility via choosing adequate LPPM and configuring it. At the heart of PULP is nonlinear models that can capture the complex dependency of data privacy and utility for each individual user under given LPPM considered, i.e., Geo-Indistinguishability and Promesse. According to users' preferences on privacy and utility, PULP efficiently recommends suitable LPPM and corresponding configuration. We evaluate the accuracy of PULP's models and its effectiveness to achieve the privacy-utility trade-off per user, using four real-world mobility traces of 770 users in total. Our extensive experimentation shows that PULP ensures the contribution to location service while adhering to privacy constraints for a great percentage of users, and is orders of magnitude faster than non-model based alternatives.