Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This article presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

Sustainable and cost-effective long-term storage remains an unsolved problem. The most widely used storage technologies today are magnetic (hard disk drives and tape). They use media that degrades over time and has a limited lifetime, which leads to inefficient, wasteful, and costly solutions for long-lived data. This paper presents Silica: the first cloud storage system for archival data underpinned by quartz glass, an extremely resilient media that allows data to be left in situ indefinitely. The hardware and software of Silica have been co-designed and co-optimized from the media up to the service level with sustainability as a primary objective. The design follows a cloud-first, data-driven methodology underpinned by principles derived from analyzing the archival workload of a large public cloud service. Silica can support a wide range of archival storage workloads and ushers in a new era of sustainable, cost-effective storage.

In recent years, the epidemic of speculative side channels significantly increases the difficulty in enforcing domain isolation boundaries in a virtualized cloud environment. Although mitigations exist, the approach taken by the industry is neither a long-term nor a scalable solution, as we target each vulnerability with specific mitigations that add up to substantial performance penalties. We propose a different approach to secret isolation: guaranteeing that the hypervisor is Secret-Free (SF). A Secret-Free design partitions memory into secrets and non-secrets and reconstructs hypervisor isolation. It enforces that all domains have a minimal and secret-free view of the address space. In contrast to state-of-the-art, a Secret-Free hypervisor does not identify secrets to be hidden, but instead identifies non-secrets that can be shared, and only grants access necessary for the current operation, an allow-list approach. SF designs function with existing hardware and do not exhibit noticeable performance penalties in production workloads versus the unmitigated baseline, and outperform state-of-the-art techniques by allowing speculative execution where secrets are invisible. We implement SF in Xen (a Type-I hypervisor) to demonstrate that the design applies well to a commercial hypervisor. Evaluation shows performance comparable to baseline and up to 37% improvement in certain hypervisor paths compared with Xen default mitigations. Further, we demonstrate Secret-Free is a generic kernel isolation infrastructure for a variety of systems, not limited to Type-I hypervisors. We apply the same model in Hyper-V (Type-I), bhyve (Type-II) and FreeBSD (UNIX kernel) to evaluate its applicability and effectiveness. The successful implementations on these systems prove the generality of SF, and reveal the specific adaptations and optimizations required for each type of kernel.

The increasing gap between the growth of datacenter traffic and electrical switch capacity is expected to worsen due to the slowdown of Moore's law, motivating the need for a new switching technology for the post-Moore's law era that can meet the increasingly stringent requirements of hardware-driven cloud workloads. We propose Sirius, an optically-switched network for datacenters providing the abstraction of a single, high-radix switch that can connect thousands of nodes---racks or servers---in a datacenter while achieving nanosecond-granularity reconfiguration. At its core, Sirius uses a combination of tunable lasers and simple, passive gratings that route light based on its wavelength. Sirius' switching technology and topology is tightly codesigned with its routing and scheduling and with novel congestion-control and time-synchronization mechanisms to achieve a scalable yet flat network that can offer high bandwidth and very low end-to-end latency. Through a small-scale prototype using a custom tunable laser chip that can tune in less than 912 ps, we demonstrate 3.84 ns end-to-end reconfiguration atop 50 Gbps channels. Through large-scale simulations, we show that Sirius can approximate the performance of an ideal, electrically-switched non-blocking network with up to 74-77% lower power.

The low-level C++ programming language is ubiquitously used for its modularity and performance. Typecasting is a fundamental concept in C++ (and object-oriented programming in general) to convert a pointer from one object type into another. However, downcasting (converting a base class pointer to a derived class pointer) has critical security implications due to potentially different object memory layouts. Due to missing type safety in C++, a downcasted pointer can violate a programmer's intended pointer semantics, allowing an attacker to corrupt the underlying memory in a type-unsafe fashion. This vulnerability class is receiving increasing attention and is known as type confusion (or bad-casting). Several existing approaches detect different forms of type confusion, but these solutions are severely limited due to both high run-time performance overhead and low detection coverage.

Many existing techniques for reversing data structures in C/C ++ binaries are limited to low-level programming constructs, such as individual variables or structs. Unfortunately, without detailed information about a program's pointer structures, forensics and reverse engineering are exceedingly hard. To fill this gap, we propose MemPick, a tool that detects and classifies high-level data structures used in stripped binaries. By analyzing how links between memory objects evolve throughout the program execution, it distinguishes between many commonly used data structures, such as singly- or doubly-linked lists, many types of trees (e.g., AVL, red-black trees, B-trees), and graphs. We evaluate the technique on 10 real world applications, 4 file system implementations and 16 popular libraries. The results show that MemPick can identify the data structures with high accuracy.

As VTable hijacking becomes the primary mode of exploitation against modern browsers, protecting said VTables has recently become a prime research interest. While multiple source- and binary-based solutions for protecting VTables have been proposed already, we found that in practice they are too conservative, which allows determined attackers to circumvent them. In this paper we delve into the design of C++ VTables and match that knowledge against the now industry standard protection scheme of VTV. We propose an end-to-end design that significantly refines VTV, to offer a provably optimal protection scheme. As we build on top of VTV, we preserve all of its advantages in terms of software compatibility and overhead. Thus, our proposed design comes "for free" for any user today. Besides the design we propose a testing methodology, which can be used by future developers to validate their implementations. We evaluated our protection scheme on Google Chrome and show that no compatibility issues were introduced, while overhead is also unchanged compared to the baseline of VTV.

Automated program testing tools typically try to explore, and cover, as much of a tested program as possible, while attempting to trigger and detect bugs. An alternative and complementary approach can be to first select a specific part of a program that may be subject to a specific class of bug, and then narrowly focus exploration towards program paths that could trigger such a bug.

Traditionally, subpixel interpolation in stereo-vision systems was designed for the block-matching algorithm. During the evaluation of different interpolation strategies, a strong correlation was observed between the type of the stereo algorithm and the subpixel accuracy of the different solutions. Subpixel interpolation should be adapted to each stereo algorithm to achieve maximum accuracy. In consequence, it is more important to propose methodologies for interpolation function generation than specific function shapes. We propose two such methodologies based on data generated by the stereo algorithms. The first proposal uses a histogram to model the environment and applies histogram equalization to an existing solution adapting it to the data. The second proposal employs synthetic images of a known environment and applies function fitting to the resulted data. The resulting function matches the algorithm and the data as best as possible. An extensive evaluation set is used to validate the findings. Both real and synthetic test cases were employed in different scenarios. The test results are consistent and show significant improvements compared with traditional solutions.

The increase in computational power of consumer graphic cards has successfully motivated adaptation of stereo algorithms to this kind of hardware. In order to solve the stereo correspondence problem efficiently, the images need to be rectified and lens distortions need to be removed. This paper presents an efficient two step solution for rectifying and correcting lens distortions in images captured using a pair of stereo cameras. The first step consists of a one-time, off-line calculation of a look-up table, based on the calibration parameters, for each of the two cameras. The second step computes the final pixel intensities based on the pre-calculated mappings stored in the look-up table. The GPU implementation proposed makes use of the inherent parallelism in a cost-effective manner, making the method suitable for rectifying high resolution images in real-time. Results are compared against an optimized CPU-based implementation, written in assembly language using MMX instructions, for reference. The complete stereo reconstruction system was implemented and evaluated on a current generation GPU and offers a running time of 11ms for images with resolution 512×383.

High speed serial interfaces represent the new trend for device-to-device communication. These systems require clock recovery modules to avoid clock forwarding. In this paper we present a high-speed clock recovery method usable with low-cost FPGAs. Our proposed solution features increased speed and reduced size compared to existing designs. The method allows a maximum throughput of 400 Mbps compared to the vendor supplied solution capable of only 160 Mbps. The module was also integrated and tested within a serial transceiver system. Although the implementation is specific to a given vendor, the idea can also be applied to others devices because it uses only generally available components from most vendors.

In this work we focus on creating a real-time dense stereo reconstruction system with accurate sub-pixel estimation. We selected the Semi-Global Matching method as the basis of our system due to its high quality and possible real-time implementations. In our solution we use the Census transform as the matching metric because our results show that it can reduce the matching errors for traffic images compared to classical solutions. We also propose several modifications to the original Semi-Global algorithm to improve the sub-pixel accuracy and the execution time. One of these proposals is the reduction in the number of optimization directions without affecting the results. The second modification is a correction of the energy function to reduce the spread of depth values. Besides these improvements, the paper also introduces a new aggregation method used to reduce the spread of sub-pixel values. Finally we propose a new method to generate sub-pixel interpolation functions based on real-world data. The result of these enhancements is a significant improvement in sub-pixel accuracy. The system was implemented and evaluated on a current generation GPU with a running time of 19 ms for image having the resolution 512 × 383.