This paper describes the performance pitfalls when using tiered memory for a networked key-value store and our approach to avoiding them. We observe that when receiving data over the network, writing data to tiered memory results in multiple data stagings and repetitive user-kernel crossings. We also observe sudden bursts of I/O operations when allocating memory if the slower memory tier is backed by a DAX file system. We address these challenges through (1) PPF (packet peek and forward) that peeks at the packets in the kernel layer with eBPF and streamlines data placement decisions on tiered memory, and (2) OMA (opportune memory allocator) that moves zeroing off the critical path. Performance evaluation with the prototype shows that the adoption of our design improves IOPS by up to 128%.

In blockchain, proof-of-work (PoW) is a popular consensus mechanism in which block publishers secure block contents through competitive computation. This competition has led to the emergence of specialized computing devices, such as application-specific integrated circuits (ASICs). Consequently, block publishing has become monopolized by a small group of top publishers equipped with ASICs and benefiting from economy of scale. This monopoly undermines immutability and security that are derived from the decentralized structure of blockchains. In this paper, we introduce a type of blockchain consensus algorithm named error-correction code verifiable computation consensus (ECCVCC), which includes conventional PoW. After that, we propose a novel ECCVCC utilizing a syndrome decoding problem as its crypto puzzle. The ECCVCC algorithm suppresses the development of efficient ASICs by utilizing time-varying cryptographic puzzles. As a result, the decentralization of a blockchain with ECCVCC can be improved compared to the blockchains with other consensus algorithms. Our analysis and simulation demonstrate that ECCVCC achieves robust control over block-generation time and difficulty under practical scenarios. Finally, we discuss that ASIC-resistant consensus algorithms, such as ECCVCC, sustain a blockchain network decentralized for a significantly longer period compared to conventional hash-PoW.

This work presents a motion retargeting approach for legged robots, aimed at transferring the dynamic and agile movements to robots from source motions. In particular, we guide the imitation learning procedures by transferring motions from source to target, effectively bridging the morphological disparities while ensuring the physical feasibility of the target system. In the first stage, we focus on motion retargeting at the kinematic level by generating kinematically feasible whole-body motions from keypoint trajectories. Following this, we refine the motion at the dynamic level by adjusting it in the temporal domain while adhering to physical constraints. This process facilitates policy training via reinforcement learning, enabling precise and robust motion tracking. We demonstrate that our approach successfully transforms noisy motion sources, such as hand-held camera videos, into robot-specific motions that align with the morphology and physical properties of the target robots. Moreover, we demonstrate terrain-aware motion retargeting to perform BackFlip on top of a box. We successfully deployed these skills to four robots with different dimensions and physical properties in the real world through hardware experiments.

Flash-based NVMe storage devices dramatically improve I/O latency as well as I/O throughput. However, existing scale-out storage systems are designed for targeting hard disk drives and this design limitation causes significant performance degradation when they are used with NVMe devices. In this paper, we analyzed the performance of an existing scale-out storage system and identified lack of data locality and excessive lock contentions. To mitigate these problems, we present a new design based on locality aware lock contention avoidance (LALCA). LALCA proposes two techniques for scale-out storage system on high speed storage device: (1) locality-aware thread control to minimize processor context switching overhead and relevant performance degradation and (2) lock contention avoidance to remove locking problems in existing scale-out distributed storage system. With evaluation, LALCA shows not only up to 9 times performance improvement but also up to half CPU usage reduction when servicing small random I/Os.

The proliferation of the big data analysis and the wide spread usage of public/private cloud services make it important to expand the storage capacity as the demand is increased. The scale-out storage is gaining more attention since it can inherently provide scalable storage capacity. The flash SSD, on the other hand, is getting popular as the drop-in replacement of the slow HDD, which seems to boost the system performance somewhat at least. However, the performance of traditional scale-out storage system does not get much better even though its HDD is replaced with the flash based high performance SSD since the whole system is designed based on HDD as its underlying storage device. In this paper, we identify performance problems of a representative scale-out storage system, Ceph, and analyze that these problems are caused by 1) Coarse-grained lock, 2) Throttling logic, 3) Batching based operation latency and 4) Transaction Overhead. We propose some optimization techniques for flash-based Ceph. First, we minimize coarse-grained locking. Second, we introduce throttle policy and system tuning. Third, we develop non-blocking logging and light-weight transaction processing. We found that our optimized Ceph shows up to 20 times improvement in the case of small random writes and it also shows more than two times better performance in the case of small random read through our experiments. We also show that the system exhibits linear performance increase as we add more nodes.