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Abstract-Microelectronic circuits exhibit increasing variations in performance, power consumption, and reliability parameters across the manufactured parts and across use of these parts over time in the field. These variations have led to increasing use of overdesign and guardbands in design and test to ensure yield and reliability with respect to a rigid set of datasheet specifications. This paper explores the possibility of constructing computing machines that purposely expose hardware variations to various layers of the system stack including software. This leads to the vision of underdesigned hardware that utilizes a software stack that opportunistically adapts to a sensed or modeled hardware. The envisioned underdesigned and opportunistic computing (UnO) machines face a number of challenges related to the sensing infrastructure and software interfaces that can effectively utilize the sensory data. In this paper, we outline specific sensing mechanisms that we have developed and their potential use in building UnO machines.

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Abstract-Modern software systems provide many configuration options which significantly influence their non-functional properties. To understand and predict the effect of configuration options, several sampling and learning strategies have been proposed, albeit often with significant cost to cover the highly dimensional configuration space. Recently, transfer learning has been applied to reduce the effort of constructing performance models by transferring knowledge about performance behavior across environments. While this line of research is promising to learn more accurate models at a lower cost, it is unclear why and when transfer learning works for performance modeling. To shed light on when it is beneficial to apply transfer learning, we conducted an empirical study on four popular software systems, varying software configurations and environmental conditions, such as hardware, workload, and software versions, to identify the key knowledge pieces that can be exploited for transfer learning. Our results show that in small environmental changes (e.g., homogeneous workload change), by applying a linear transformation to the performance model, we can understand the performance behavior of the target environment, while for severe environmental changes (e.g., drastic workload change) we can transfer only knowledge that makes sampling more efficient, e.g., by reducing the dimensionality of the configuration space.

Despite growing concerns about security and privacy of Internet of Things (IoT) devices, consumers generally do not have access to security and privacy information when purchasing these devices. We interviewed 24 participants about IoT devices they purchased. While most had not considered privacy and security prior to purchase, they reported becoming concerned later due to media reports, opinions shared by friends, or observing unexpected device behavior. Those who sought privacy and security information before purchase, reported that it was difficult or impossible to find. We asked interviewees to rank factors they would consider when purchasing IoT devices; after features and price, privacy and security were ranked among the most important. Finally, we showed interviewees our prototype privacy and security label. Almost all found it to be accessible and useful, encouraging them to incorporate privacy and security in their IoT purchase decisions. CCS CONCEPTS • Security and privacy → Usability in security and privacy; • Social and professional topics → Privacy policies;

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