As data-centric applications proliferate, mitigating Write Amplification Factor (WAF) in SSDs has become critical for sustaining performance and longevity. Flexible Data Placement (FDP), a recently ratified NVMe standard, addresses this by allowing hosts to guide data placement while retaining block interface compatibility. However, research on FDP is constrained by the scarcity of prototype device and the lack of emulation tools supporting its multi-stream architecture. In this paper, we propose FDPEmu, a high-fidelity FDP emulator extended from FEMU. FDPEmu addresses the architectural limitations of legacy emulators by implementing core FDP data structures, per-RUH write pointers, and isolation-aware Garbage Collection (GC). We also introduce Striding, a dynamic channel offset allocation technique, to mitigate channel contention in multi-stream environments. Validation against prototype FDP SSDs demonstrates high fidelity ($r \gt 0.89$ for skewed workloads) in capturing WAF trends, confirming the emulator as a credible research platform. Furthermore, our case studies on RocksDB and F2FS reveal that strictly separating data is not universally beneficial. We demonstrate that the effectiveness of isolation policies relies heavily on workload patterns, indicating that optimal FDP strategies must carefully balance data lifetime separation with effective resource utilization.

By embedding the distribution of keys in indexing structure, learned indexes can minimize the index size and maximize the lookup performance. Yet, one of the problems in the present learned index is the long index-building time. The conventional learned index requires a complete traversal of the entire dataset, which makes it less practical than traditional index. This paper challenges the efficiency of build time to make the learned index practical. Our approach for a build time-efficient learned index is to employ sampled learning. In this paper, we present two error-bounded sampling schemes: Sample EB-PLA, and Sample EB-Histogram. Although sampling is a simple idea, there are several considerations to make it practical. For example, sampling interval, error-boundness, and index hyper-parameters are inter-related each other, presenting complicated trade-offs between build-time, index size, accuracy and lookup latency. Throughout the extensive experiments over six real-world datasets, we show that the index-building time can be efficiently reduced over an order of magnitude by our sampling schemes. The results reveal that the sampling expands the design space of learned indexes, including the build-time as well as lookup performance and index size. Our Pareto analysis shows that a learned index can be built more efficiently than a traditional index through sampling.

Abstract This paper presents enCloud, a new aspect‐oriented trusted service migration with SGX‐enabled cloud VM. Addressing the challenge of reconciling end‐to‐end security with VM migration, enCloud incorporates two key aspects: (1) end‐to‐end security for enclave context migration, and (2) VM abstraction for conventional VM context migration. This paper provides a practical guideline with applicable APIs for trusted service migration. In a case study, enCloud demonstrates effective trusted DB service migration on a cloud VM, achieving end‐to‐end security with minimal trust boundaries. The framework supports pre‐copy live VM migration to minimize service downtime. This paper contributes a concise and practical solution in the form of the enCloud framework for secure service migration.

The ZNS (Zoned NameSpace) interface shifts much of the storage maintenance responsibility to the host from the underlying SSDs (Solid-State Drives). In addition, it opens a new opportunity to exploit the internal parallelism of SSDs at both hardware and software levels. By orchestrating the mapping between zones and SSD-internal resources and by controlling zone allocation among threads, ZNS SSDs provide a distinct performance trade-off between parallelism and isolation. To understand and explore the design space of ZNS SSDs, we present ConfZNS (Configurable ZNS), an easy-to-configure and timing-accurate emulator based on QEMU. ConfZNS allows users to investigate a variety of ZNS SSD's internal architecture and how it performs with existing host software. We validate the accuracy of ConfZNS using real ZNS SSDs and explore performance characteristics of different ZNS SSD designs with real-world applications such as RocksDB, F2FS, and Docker environment.

For secure and flexible utilize of computing resources, virtual SGX and EPC migration schemes have been proposed. This paper analyzes the proposed EPC migration schemes under live VM context. According to our analysis, each of VM migration schemes has advantages and disadvantages in terms of VM downtime and security aspects.

Service level agreements (SLAs) for storage performance in virtualized systems are difficult to guarantee, because different consolidated virtual machines have their own performance requirements. Moreover, hard disk drives (HDDs) in virtualized systems are being replaced by solid-state drives (SSDs). SSDs have higher throughput and lower latency than HDDs; however, they pose new challenges in terms of SLAs. In this paper, we determine that existing I/O schedulers working with SSDs fail to guarantee SLAs among virtualmachines, and do not effectively utilize the high performance of SSDs. To address this issue, we propose the opportunistic I/O scheduler (OIOS), a novel I/O scheduler for SSDs. OIOS guarantees SLAs and fully utilizes the high performance of SSDs. To support realistic SLAs, OIOS provides diverse SLA support functions, including reservations, limitations, and proportional sharing. In addition, OIOS accepts SLAs that are specified in four measurement types: bandwidth, I/Os per second (IOPS), latency, and utilization. Experimental results show that OIOS increases the aggregated bandwidth of VMs by 80 percent compared to mClock, while achieving a similar level of fairness. In addition, we evaluate the proposed scheduler with realistic benchmarks, such as Filebench and the Yahoo CloudServing Benchmark. OIOS successfully guarantees the requirements of diverse SLAs with different metrics.

Enterprise servers require customized solid-state drives (SSDs) to satisfy their specialized I/O performance and reliability requirements. For effective use of SSDs for enterprise purposes, SSDs must be designed considering requirements such as those related to performance, lifetime, and cost constraints. However, SSDs have numerous hardware and software design options, such as flash memory types and block allocation methods, which have not been well analyzed yet, but on which the SSD performance depends. Furthermore, there is no methodology for determining the optimal design for a particular I/O workload. This paper proposes SSD-Tailor, a customization tool for SSDs. SSD-Tailor determines a near-optimal set of design options for a given workload. SSD designers can use SSD-Tailor to customize SSDs in the early design stage to meet the customer requirements. We evaluate SSD-Tailor with nine I/O workload traces collected from real-world enterprise servers. We observe that SSD-Tailor finds near-optimal SSD designs for these workloads by exploring only about 1% of the entire set of design candidates. We also show that the near-optimal designs increase the average I/O operations per second by up to 17% and decrease the average response time by up to 163% as compared to an SSD with a general design.

This paper investigates the feasibility of real-time scheduling with mobile hypervisor, Xen-ARM. Particularly for mobile virtual machines, real-time support is in high demand. However, it is difficult to guarantee real-time scheduling with virtual machines because inter-VM and intra-VM schedulability have to be determined in multi-OS environments. To address the schedulability, first, this paper presents a definition of a real-time virtual machine. Second, this paper analyzes intra-VM schedulability, taking quantization overhead into account. Quantization overhead comes from tick-based scheduling of Xen-ARM, which requires integer presentation of scheduling period and execution slice. Third, to minimize quantization overhead, this paper provides a new algorithm, called SH-quantization that provides accurate and efficient parameterization of a real-time virtual machine. Fourth, this paper presents an inter-VM schedulability test for incorporating multiple real-time virtual machines. To evaluate the approach, we implement the SH-quantization algorithm in Xen-ARM and paravirtualize a real-time OS, called xeno-μC/OS-II. We ran extensive experiments with various configurations of real-time tasks on a real hardware platform in order to characterize the scheduling behavior of real-time virtual machine with quantization. The results show that quantization overhead consumes additional CPU bandwidth up to 90% and the proposed algorithm guarantees intra/inter-VM schedulability with minimal CPU bandwidth.

In recent consumer electronics devices, virtualization is widely adopted for diverse reasons such as enhanced reliability, stronger security and better user customizability. Recent CE virtual machines require not only diverse functionalities of GPOS, but also real-time performance of RTOS. However, the current hypervisor-based virtual machine monitors cannot sufficiently support both RTOS and GPOS at the same time. The reason is that the current hypervisor scheduler is biased to only one kind of guest OS and it cannot support heterogeneous schedulers. Therefore, this paper proposes a new scheduler architecture, PARFAIT for both RT and GP guest OSs. Our new scheduler enables to guarantee CPU bandwidth for RTOS and provides fairness among GPOSs using hierarchical structure.

Real-time is one of the unique requirements in embedded systems. In this paper, we perform a feasibility study on how to support real-time in an embedded virtual machine system. Firstly, we argue that the I/O model of the current virtual machine monitor like Xen is not suitable to support real-time applications because it lacks in predictability and it does not guarantee a deterministic I/O processing. We provide an alternative I/O model for virtualized embedded systems. Devices are categorized into four groups: dedicated, active, running, dynamic. Dedicated devices make a virtual machine simple because they do not need to be virtualized for isolation. However, dedication does not mean the performance isolation. Our experimental results with dedicated device show that traditional dedication cannot guarantee the timely responsiveness in heavy interrupt cases. Specifically, responsiveness of real-time OS degrades as interrupt load increases. Therefore, a proper interrupt control mechanism is required at virtual machine monitor level in order to support timely responsiveness. In addition, our result supports that (1) short and prioritized interrupt processing helps responsiveness in a virtual machine system; (2) smaller time quantum results in better responsiveness also.

It is well known that TCP shows performance degradation over wired/wireless networks since TCP regards packet loss as network congestion. TCP Veno has successfully addressed this fundamental problem by proposing an end-to-end loss differentiation algorithm, which distinguishes the cause of packet loss by the number of packets in the router buffer. Unfortunately, Veno's algorithm shows very low accuracy on small buffer routers that are emerging recently as a new challenge for Internet routers. In the small buffer networks, routers can overflow easily in a short time although Veno diagnoses wireless loss, and this leads to the failure in loss differentiation. Furthermore, when congestion loss is misdiagnosed as wireless loss, Veno shows poor Reno-friendliness since it can increase sending rate by setting ssthresh to a larger value than Reno. In this paper, we propose a more accurate loss differentiation algorithm for small buffer heterogeneous networks. Our algorithm accurately distinguishes wireless and wired packet loss by newly defining congestive rate. Through extensive network simulations, we confirm that our new TCP Feno achieves not only higher accuracy, but also better Reno-friendliness while not losing performance efficiency.