Predictable timing on multicore systems requires careful management of shared resources such as the last-level cache and memory bandwidth. This paper presents MPORA, an uncertainty-aware dynamic resource allocation framework for multi-path, input-dependent real-time tasks on multicore platforms. MPORA models each job as a discrete-time dynamical system that captures execution dynamics and resource-dependent performance indicators. At runtime, MPORA monitors job execution states and predicts short-term instruction rates and remaining execution times under candidate allocations using predictive models trained offline. It then solves a receding-horizon optimization problem to compute resource allocations that maximize system-wide progress while meeting job deadlines. To address prediction uncertainty, MPORA integrates weighted conformal prediction into the optimization formulation, enabling uncertainty-aware deadline constraints. We implement MPORA as a Linux kernel module with microsecond-scale inference overhead. Experimental results on SPEC CPU benchmarks show that MPORA delivers accurate predictions under unseen inputs and distribution shifts with low overhead, while improving schedulability and response times over existing methods.

Byzantine Fault Tolerance (BFT) is a classic technique for defending distributed systems against a wide range of faults and attacks. However, existing solutions are designed for systems where nodes can interact only by exchanging messages. They are not directly applicable to systems where nodes have sensors and actuators and can also interact in the physical world - perhaps by blocking each other's path or by crashing into each other.

As multicore hardware becomes increasingly prevalent in real-time embedded systems, traditional scheduling techniques that assume a single worst-case execution time for each task are no longer adequate, as they fail to account for the impact of shared resources—such as cache and memory bandwidth—on execution time. When tasks execute concurrently on different cores, their execution times can vary substantially with their allocated resources. Moreover, the instruction rate of a task during a job execution varies with time, and this variation pattern differs across tasks. Therefore, to improve performance it is crucial to incorporate the relationship between the resource budget allocated to each task and its time-varying instruction rate in task modeling, resource allocation, and scheduling algorithm design. Yet, no prior work has considered the fine-grained dynamic resource allocation and scheduling problems jointly while also providing hard real-time guarantees. In this article, we introduce a resource-dependent multi-phase timing model that captures the time-varying instruction rates of a task under different resource allocations and that enables worst-case analysis under dynamic allocation. We present a method for constructing estimates of such a model based on task execution profiles, which can be obtained through measurements. We then present Rasco , a co-design technique for multicore resource allocation and scheduling of real-time DAG applications with end-to-end deadlines. Rasco leverages the resource-dependent multi-phase model of each task to simultaneously allocate resources at a fine granularity and assign task deadlines. This approach maximizes execution progress under resource constraints while providing hard real-time schedulability guarantees. Our evaluation shows that Rasco substantially enhances schedulability and reduces end-to-end latency compared to the state of the art.

In contemporary database applications, the demand for memory resources is intensively high. To enhance adaptability to varying resource needs and improve cost efficiency, the integration of diverse storage technologies within heterogeneous memory architectures emerges as a promising solution. Despite the potential advantages, there exists a significant gap in research related to the security of data within these complex systems. This paper endeavors to fill this void by exploring the intricacies and challenges of ensuring data security in object-oriented heterogeneous memory systems. We introduce the concept of Unified Encrypted Memory (UEM) management, a novel approach that provides unified object references essential for data management platforms, while simultaneously concealing the complexities of physical scheduling from developers. At the heart of UEM lies the seamless and efficient integration of data encryption techniques, which are designed to ensure data integrity and guarantee the freshness of data upon access. Our research meticulously examines the security deficiencies present in existing heterogeneous memory system designs. By advancing centralized security enforcement strategies, we aim to achieve efficient object-centric data protection. Through extensive evaluations conducted across a variety of memory configurations and tasks, our findings highlight the effectiveness of UEM. The security features of UEM introduce low and acceptable overheads, and UEM outperforms conventional security measures in terms of speed and space efficiency.

The emergence of trusted hardwares (such as Intel SGX) provides a new avenue towards verifiable database. Such trust hardwares act as an additional trust anchor, allowing great simplification and, in turn, performance improvement in the design of verifiable databases. In this paper, we introduce the design and implementation of VeriDB, an SGX-based verifiable database that supports relational tables, multiple access methods and general SQL queries. Built on top of write-read consistent memory, VeriDB provides verifiable page-structured storage, where results of storage operations can be efficiently verified with low, constant overhead. VeriDB further provides verifiable query execution that supports general SQL queries. Through a series of evaluation using practical workload, we demonstrate that VeriDB incurs low overhead for achieving verifiability: an overhead of 1-2 microseconds for read/write operations, and a 9% - 39% overhead for representative analytical workloads.

Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs can rely on their own internal resource management. We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance.