In this paper, we report on the utilization of advanced distributed control algorithms, deployed on physical controllers, for an Integrated Power and Energy System (IPES) energy management system. In addition, we provide an evaluation of the management system. Results from a hardware-in-the-Ioop experiment will be analyzed to show the effectiveness of the realtime control system. The goal of this paper is to provide valuable insights from a practical system controls the application of reasonable size and system complexity.

One important aspect of privacy is the ability to securely delete sensitive data from electronic storage in such a way that it cannot be recovered; we call this action secure deletion . Short of physically destroying the entire storage medium, existing software secure-deletion solutions tend to be piecemeal at best -- they may only work for one type of storage or file system, may force the user to delete all files instead of selected ones, may require the added complexities of encryption and key storage, may require extensive changes and additions to the computer's operating system or storage firmware, and may not handle system crashes gracefully. We present TrueErase, a holistic secure-deletion framework for individual systems that contain sensitive data. Through design, implementation, verification, and evaluation on both a hard drive and NAND flash, TrueErase shows that it is possible to construct a per-file, secure-deletion framework that can accommodate different storage media and legacy file systems, require limited changes to legacy systems, and handle common crash scenarios. TrueErase can serve as a building block by cryptographic systems that securely delete information by erasing encryption keys. The overhead is dependent on spatial locality, number of sensitive files, and workload (computational- or I/O-bound).

The ability to securely delete sensitive data from electronic storage is becoming important. However, current per-file deletion solutions tend to be limited to a segment of the operating system's storage data path or specific to particular file systems or storage media.

The specification of the sporadic server real-time scheduling policy in the IEEE POSIX standard is defective, and needs to be corrected. Via experiments using a POSIX sporadic server implementation under Linux, as well as simulations, we have shown and confirmed previously unreported defects. We propose and demonstrate a corrected sporadic server formulation that eliminates these defects without changes to the syntax of the API or any significant increase in implementation complexity.

Many contemporary disk drives have built-in queues and schedulers. These features can improve I/O performance, by offloading work from the system's main processor, avoiding disk idle time, and taking advantage of vendor-specific disk characteristics. At the same time, they pose challenges for scheduling requests that have real-time requirements, since the operating system has less visibility and control over service times. While it may be possible for an operating system to obtain more predictable real-time performance by bypassing the on-disk queue and scheduler, the diversity and continuing evolution of disk drives make it difficult to extract the necessary detailed timing characteristics of a specific disk, and to generalize that approach to all hard drives. This paper demonstrates three techniques we developed in the Linux operating system to bound real-time request response times for disks with internal queues and schedulers. The first technique is to use the disk's built-in starvation prevention scheme. The second is to prevent requests from being sent to the disk when real-time requests are waiting to be served. The third is to limit the length of the on-disk queue in addition to the second technique. Our results show the ability to guarantee a wide range of desired response times while still allowing the disk to perform scheduling optimizations. These techniques can be generalized to disks from different vendors.

Device drivers are integral components of operating systems. The computational workloads imposed by device drivers tend to be aperiodic and unpredictable because they are triggered in response to events that occur in the device, and may arbitrarily block or preempt other time-critical tasks. This characteristic poses significant challenges in real-time systems, where schedulability analysis is essential to guarantee system-wide timing constraints. At the same time, device driver workloads cannot be ignored. Demand-based schedulability analysis is a technique that has been successful in validating the timing constraints in both single and multiprocessor systems. In this paper we present two approaches to demand-based schedulability analysis of systems that include device drivers. First, we derive load-bound functions using empirical measurement techniques. Second, we modify the scheduling of network device driver tasks in Linux to implement an algorithm for which a load-bound function can be derived analytically. We demonstrate the practicality of our approach through detailed experiments with a network device under Linux. Our results show that, even though the network device driver does not conform to conventional periodic or sporadic task models, it can be successfully modeled using hyperbolic load-bound functions that are fitted to empirical performance measurements