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Network resource reservation systems are being developed and deployed, driven by the demand and substantial benefits of providing performance predictability for modern distributed applications. However, existing systems suffer limitations: They either are inefficient in finding the optimal resource reservation, or cause private information (e.g., from the network infrastructure) to be exposed (e.g., to the user). In this paper, we design BoxOpt, a novel system that leverages efficient oracle construction techniques in optimization and learning theory to automatically, and swiftly learn the optimal resource reservations without exchanging any private information between the network and the user. We implement a prototype of BoxOpt and demonstrate its efficiency and efficacy via extensive experiments using real network topology and trace. Results show that (1) BoxOpt has a 100% correctness ratio, and (2) for 95% of requests, BoxOpt learns the optimal resource reservation within 13 seconds. 

Large-scale battery packs are commonly used in applications such as electric vehicles (EVs) and smart grids. Traditionally, to provide stable voltage to the loads, voltage regulators are used to convert battery packs’ output voltage to those of the loads’ required levels, causing power loss especially when the difference between the supplied and required voltages is large or when the load is light. In this article, we address this issue via a reconfiguration framework for the battery system. By abstracting the battery system as a cell graph, we develop an adaptive reconfiguration algorithm to identify the desired system configurations based on real-time load requirements. Our design is evaluated via both prototype-based experiments, EV driving trace-based emulations, and large-scale simulations. The results demonstrate an extended system operation time of up to 5×, especially when facing severe cell imbalance.