Enhanced Vehicle-to-Everything (V2X) services require ultra-low latency and robust reliability in end-to-end (E2E) data delivery from producers (e.g., vehicles and roadside units) to consumers (e.g., in-car V2X service applications). One of the candidate data delivery architectures that can meet the stringent requirements is restricting the data delivery path within the network edge by letting an edge server relay data between sources and destinations in its coverage. To that end, edge servers need to equip two important functionalities: 1) a reliable and lightweight data transport protocol to collect data from numerous data sources, and 2) an efficient data streaming platform that delivers collected data to consumers with high throughput and low latency. In this work, we design and implement Kafka- Thor, a Kafka-based high-performance data streaming platform, specifically designed for edge servers to collect and deliver timecritical data for enhanced V2X services. The platform reduces E2E data delivery latency by optimizing the data delivery architecture in Kafka using two novel technologies, the singlepoller, multi-worker (SPMW) architecture and service-specific dynamic batching (SS-batching). Experimental results show that Kafka-Thor significantly improves latency and reliability in E2E data delivery, making enhanced V2X services feasible.

Multiversion concurrency control (MVCC) and design principles thereof are ingrained in modern database management systems, thus promoting remarkable progress in managing online transaction processing (OLTP) workloads for decades. However, MVCC systems would battle two vital concerns when facing hybrid transactional/analytical processing (HTAP). The first concern is to ensure rapid version searching for analytic queries with less I/O, and the second concern is to reclaim garbage data versions promptly for easing the strain on storage footprint. These are often tightly coupled since many MVCC systems rely on unified version storage that poses a space-time tradeoff in HTAP, giving rise to disappointing performance metrics that may negatively stereotype OLTP-friendly MVCC systems. This paper refutes the stereotype resulting from coupled design concerns and addresses the core problem by proposing Diva (Decoupling Index from Version dAta) that physically separates version index from version data; for decoupled concerns, we devise independent management policies: provisional version indexing and time interval-based version garbage collection. The separation of coupled concerns would render legacy disk-based MVCC systems more HTAP-friendly. We applied Diva to two full-fledged database systems---PostgreSQL and MySQL---and demonstrated that the systems with Diva escaped the space-time tradeoff under hybrid transactional/analytical workloads.

Edge computing has emerged as a viable solution to bridge the gap between distributed Internet of Things (IoT) devices and centralized distant clouds. In particular, small-scale servers are deployed at the edge of network (i.e., edge servers) to `help' cloud servers process data IoT devices constantly generate. However, these edge servers often struggle to deal with emerging applications that require real-time data processing in situ, such as real-time facial recognition. In this paper, we present iEdge as an IoT-assisted edge computing framework that enables the seamless execution of applications across an edge server and nearby IoT devices. The seamless execution in essence has been realized by transforming platform-dependent monolithic applications to cross-platform composite applications and offloading some tasks/functions of these composite applications to IoT devices considering device context. We have evaluated iEdge using a prototype implementation with a real-time facial recognition application. Experimental results show that iEdge effectively harnesses smart IoT devices as a consolidated edge computing execution environment and enables such an application to process more video streams than typical `edge-only' computing.

A scan is one of the fundamental operations in databases for retrieving tuples from tables, and research on access methods has been of importance to query optimization. However, our community is aware of the inconvenient truth that its performance may plummet amid steep increases in search costs when acting on MVCC databases since multi-versioning may forfeit all the benefits of using database indexes. An execution plan for a query on multi-versioned data often comprises a series of point lookup operations, of which each internally executes a linear traversal of record versions. Therefore, the generated plan is surprisingly worse than a full table (or version store) scan, mainly due to redundant access to database pages. To address such an all-or-nothing approach, we propose version weaver (vWeaver), a light-weight access method for record versions, that expedites a scan on record versions with each being augmented by just a few pointer fields. vWeaver incrementally constructs a version search structure over even an append-only version store (e.g., undo space) and allows a scan to traverse new version search structures for fast lookup. We applied vWeaver to in-memory and disk-based MVCC databases and demonstrated that the systems with vWeaver generally improved scan performance under various workloads with negligible space overhead.

Many systems use snapshot isolation, or something similar, as defaults, and multi-version concurrency control (MVCC) remains essential to offering such point-in-time consistency. One major issue in MVCC is the timely removal of unnecessary versions of data items, especially in the presence of long-lived transactions (LLTs). We have observed that the latest versions of MySQL and PostgreSQL are still vulnerable to LLTs. Our analysis of existing proposals suggests that new solutions to this matter must provide rigorous rules for completely identifying unnecessary versions, and elaborate designs for version cleaning lest old versions required for LLTs should suspend garbage collection. In this paper, we formalize such rules into our version pruning theorem and version classification, of which all form theoretical foundations for our new version management system, vDriver, that bases its record versioning on a new principle: Single In-row Remaining Off-row (SIRO) versioning. We implemented a prototype of vDriver and integrated it with MySQL-8.0 and PostgreSQL-12.0. The experimental evaluation demonstrated that the engines with Driver continue to perform the reclamation of dead versions in the face of LLTs while retaining transaction throughput with reduced space consumption.

The object storage systems are used to store and manage unstructured data. Most object storage systems provide the replication policy (REP) or erasure code policy (EC) to ensure the reliability and availability of data. In this paper, we study the trade-off between performance and storage efficiency of these policies with respect to different data sizes of user requests. To this end, we present a hybrid policy management system that takes advantage of both policies by automatically changing policy based on data size. We have implemented the hybrid system in OpenStack Swift. Our evaluation results show the throughput of GET request increases up to 36% while improving storage efficiency by up to 53% compared to that using the REP policy.

Actions changing the state of databases are all logged with proper ordering being imposed. Database engines obeying this golden rule of logging enforce total ordering on all events, and this poses challenges in addressing the scalability bottlenecks of database logging on multicore hardware. We reexamined the problem of database logging and realized that in any given log history, obtaining an upper bound on the size of a set that preserves the happen-before relation is the essence of the matter. Based on our understanding, we propose Border-Collie, a wait-free and read-optimal algorithm for database logging that finds such an upper bound even with some worker threads often being idle. We show that (1) Border-Collie always finds the largest set of logged events satisfying the condition in a finite number of steps (i.e., wait-free), (2) the number of logged events to be read is also minimal (i.e., read-optimal), and (3) both properties hold even with threads being in intermittent work. Experimental results demonstrated that Border-Collie proves our claims under various workloads; Border-Collie outperforms the state-of-the-art centralized logging techniques (i.e., Eleda and ERMIA) by up to ~2X and exhibits almost the same throughput with much shorter commit latency than the state-of-the-art decentralized logging techniques (i.e., Silo and FOEDUS).

The collective use of ever connected and pervasive mobile devices has been increasingly sought for in mobile collaboration, such as multiplayer mobile gaming and distributed processing. The current model of mobile collaboration requires each device to install a particular, `full' mobile app for a respective collaboration. Besides, collaboration functionalities are typically implemented at application level. In this paper, we present Single Application Multiple Device (SAMD) as a platform-level mobile collaboration framework. A mobile app developed using SAMD is capable of fine-grained application sharing. In particular, SAMD enables devices, agreed to participate in collaboration, to get portions of the app on-the-fly and run them without the prior installation. To achieve this, we have developed three solutions as core functionalities of SAMD: 1) Controller packaging, 2) lookahead transfer and 3) code adaptation. We have implemented SAMD on Android as a proof-of-concept prototype. Our experimental results demonstrate SAMD can provide fine-grained sharing of latency-insensitive applications.

Computing resources including mobile devices at the edge of a network are increasingly connected and capable of collaboratively processing what's believed to be too complex to them. Collaboration possibilities with today's feature-rich mobile devices go far beyond simple media content sharing, traditional video conferencing and cloud-based software as a services. The realization of these possibilities for mobile edge computing (MEC) requires non-trivial amounts of efforts in enabling multi-device resource sharing. The current practice of mobile collaborative application development remains largely at the application level. In this paper, we present CollaboRoid, a platform-level solution that provides a set of system services for mobile collaboration. CollaboRoid's platform-level design significantly eases the development of mobile collaborative applications promoting MEC. In particular, it abstracts the sharing of not only hardware resources, but also software resources and multimedia contents between multiple heterogeneous mobile devices. We implement CollaboRoid in the application framework layer of the Android stack and evaluate it with several collaboration scenarios on Nexus 5 and 7 devices. Our experimental results show the feasibility of the platform-level collaboration using CollaboRoid in terms of the latency and energy consumption.

As hardware vendors provision more cores and faster storage devices, attaining fast data durability for concurrent file writes is demanding to high-performance storage systems in cluster systems. We approach the challenge by proposing a system that uses a small amount of fast persistent memory for buffering concurrent file writes while preserving data durability. The main issue in designing a durable file buffer is allowing concurrent file writes to store data in a shared and limited space of persistent memory without incurring lock or resource contention. This paper addresses such issue and presents AUTOBAHN, a durable file buffer that expedites file I/O operations.

Modern databases, guaranteeing atomicity and durability, store transaction logs in a volatile, central log buffer and then flush the log buffer to non-volatile storage by the write-ahead logging principle. Buffering logs in central log store has recently faced a severe multicore scalability problem, and log flushing has been challenged by synchronous I/O delay. We have designed and implemented a fast and scalable logging method, E leda , that can migrate a surge of transaction logs from volatile memory to stable storage without risking durable transaction atomicity. Our efficient implementation of E leda is enabled by a highly concurrent data structure, G rasshopper , that eliminates a multicore scalability problem of centralized logging and enhances system utilization in the presence of synchronous I/O delay. We implemented E leda and plugged it to WiredTiger and Shore-MT by replacing their log managers. Our evaluation showed that E leda -based transaction systems improve performance up to 71 x, thus showing the applicability of E leda.

Recovery from sudden power-off (SPO) is one of the primary concerns among practitioners which bars the quick and wide deployment of flash storage devices. In this work, we propose Metadata Embedded Write (MEW), a novel scheme for handling the sudden power-off recovery in modern flash storage devices. Given that a large fraction of commercial SSDs employ compression technology, MEW exploits the compression-induced internal fragmentation in the data area to store rich metadata for fast and complete recovery. MEW consists of (i) a metadata embedding scheme to harbor SSD metadata in a physical page together with multiple compressed logical pages, (ii) an allocation chain based fast recovery scheme, and (iii) a light-weight metadata logging scheme which enables MEW to maintain the metadata for incompressible data, too. We performed extensive experiments to examine the performance of MEW. The performance overhead of MEW is 3 percent in the worst case, in terms of the write amplification factor, compared to the pure compression-based FTL that does not have any recovery scheme.

In this work, we studied the energy consumption characteristics of various SSD design parameters. We developed an accurate energy consumption model for SSDs that computes aggregate, as well as component-specific, energy consumption of SSDs in sub-msec time scale. In our study, we used five different FTLs (page mapping, DFTL, block mapping, and two different hybrid mappings) and four different channel configurations (two, four, eight, and 16 channels) under seven different workloads (from large-scale enterprise systems to small-scale desktop applications) in a combinatorial manner. For each combination of the aforementioned parameters, we examined the energy consumption for individual hardware components of an SSD (microcontroller, DRAM, NAND flash, and host interface). The following are some of our findings. First, DFTL is the most energy-efficient address-mapping scheme among the five FTLs we tested due to its good write amplification and small DRAM footprint. Second, a significant fraction of energy is being consumed by idle flash chips waiting for the completion of NAND operations in the other channels. FTL should be designed to fully exploit the internal parallelism so that energy consumption by idle chips is minimized. Third, as a means to increase the internal parallelism, increasing way parallelism (the number of flash chips in a channel) is more effective than increasing channel parallelism in terms of peak energy consumption, performance, and hardware complexity. Fourth, in designing high-performance and energy-efficient SSDs, channel switching delay, way switching delay, and page write latency need to be incorporated in an integrated manner to determine the optimal configuration of internal parallelism.

Solid State Drives (SSDs) are quickly replacing HDDs not only in laptops but also in servers. Since SSD uses semiconductor, i.e., NAND flash memory, as its storage medium, it locates itself between memory and storage: it is faster but more expensive than HDD, and slower but cheaper than DRAM. Meanwhile, big data processing systems require both faster storage and larger memory. While lots of works have been conducted on evaluating the performance enhancement in big data processing systems by replacing HDD with SSD, the effect of SSD as a simple memory extension, i.e., Swap device, has not been studied, sufficiently. In this paper, we investigate the feasibility of using SSD as a swap device for big data processing systems through extensive experiments. Experimental results show that 1) incredibly large amount of IO's are issued to swap device when the physical memory cannot accommodate all data for processing, 2) SSD processes IO's to a swap device significantly faster than HDD, 3) using SSD as a storage device is a far better choice than using it as a swap device, and 4) enterprise SSD is not cost effective when it is used as a swap device.

Over the past few decades, IO workload characterization has been a critical issue for operating system and storage community. Even so, the issue still deserves investigation because of the continued introduction of novel storage devices such as solid-state drives (SSDs), which have different characteristics from traditional hard disks. We propose novel IO workload characterization and classification schemes, aiming at addressing three major issues: (i) deciding right mining algorithms for IO traffic analysis, (ii) determining a feature set to properly characterize IO workloads, and (iii) defining essential IO traffic classes state-of-the-art storage devices can exploit in their internal management. The proposed characterization scheme extracts basic attributes that can effectively represent the characteristics of IO workloads and, based on the attributes, finds representative access patterns in general workloads using various clustering algorithms. The proposed classification scheme finds a small number of representative patterns of a given workload that can be exploited for optimization either in the storage stack of the operating system or inside the storage device.

In this paper, we present a virtual machine based SSD Simulator, VSSIM (Virtual SSD Simulator). VSSIM intends to address the issues of the trace driven simulation, e.g. trace re-scaling, accurate replay, etc. VSSIM operates on top of QEMU/KVM with software based SSD module. VSSIM runs in realtime and allows the user to measure both the host performance and the SSD behavior under various design choices. VSSIM can flexibly model the various hardware components, e.g. the number of channels, the number of ways, block size, page size, planes per chip, program, erase, read latency of NAND cells, channel switch delay, and way switch delay. VSSIM can also facilitate the implementation of the SSD firmware algorithms. To demonstrate the capability of VSSIM, we performed a number of case studies. The results of the simulation study deliver an important guideline in the firmware and hardware designs of future NAND based storage devices. Followings are some of the findings: (i) as the page size increases, the performance benefit of increasing the channel parallelism against increasing the way parallelism becomes less significant, (ii) due to the bi-modality in IO size distribution, FTL should be designed to handle multiple mapping granularity, (iii) hybrid mapping does not work in four or more way SSD due to severe log block fragmentation, (iv) as a performance metric, the Write Amplification Factor can be misleading, (v) compared to sequential write, random write operation can be benefited more from the channel level parallelism and therefore in multi-channel environment, it is beneficial to categorize larger fraction of IO as random. VSSIM is validated against commodity SSD, Intel X25M SSD. VSSIM models the sequential IO performance of X25M within 3% offset.

In NAND Flash-based SSDs, deduplication can provide an effective resolution of three critical issues: cell lifetime, write performance, and garbage collection overhead. However, deduplication at SSD device level distinguishes itself from the one at enterprise storage systems in many aspects, whose success lies in proper exploitation of underlying very limited hardware resources and workload characteristics of SSDs. In this paper, we develop a novel deduplication framework elaborately tailored for SSDs. We first mathematically develop an analytical model that enables us to calculate the minimum required duplication rate in order to achieve performance gain given deduplication overhead. Then, we explore a number of design choices for implementing deduplication components by hardware or software. As a result, we propose two acceleration techniques: sampling-based filtering and recency-based fingerprint management. The former selectively applies deduplication based upon sampling and the latter effectively exploits limited controller memory while maximizing the deduplication ratio. We prototype the proposed deduplication framework in three physical hardware platforms and investigate deduplication efficiency according to various CPU capabilities and hardware/software alternatives. Experimental results have shown that we achieve the duplication rate ranging from 4% to 51%, with an average of 17%, for the nine workloads considered in this work. The response time of a write request can be improved by up to 48% with an average of 15%, while the lifespan of SSDs is expected to increase up to 4.1 times with an average of 2.4 times.

The design of an end-to-end Internet congestion control protocol that could achieve high utilization, fair sharing of bottleneck bandwidth, and fast convergence while remaining TCP-friendly is an ongoing challenge that continues to attract considerable research attention. This paper presents ACP, an Adaptive end-to-end Congestion control Protocol that achieves the above goals in high bandwidth-delay product networks where TCP becomes inefficient. The main contribution of ACP is a new form of congestion window control, combining the estimation of the bottleneck queue size and a measure of fair sharing. Specifically, upon detecting congestion, ACP decreases the congestion window size by the exact amount required to empty the bottleneck queue while maintaining high utilization, while the increases of the congestion window are based on a “fairness ratio” metric of each flow, which ensures fast convergence to a fair equilibrium. We demonstrate the benefits of ACP using both ns-2 simulation and experimental measurements of a Linux prototype implementation. In particular, we show that the new protocol is TCP-friendly and allows TCP and ACP flows to coexist in various circumstances, and that ACP indeed behaves more fairly than other TCP variants under heterogeneous round-trip times (RTT).

As more and more data are generated in an electronic format, the necessity of data recovery service became larger and the development of more efficient data backup and recovery technology has been an important issue during the past decade. While lots of effective backup and recovery technologies, including data dedeplication and incremental backup, have been developed for enterprise level data backup service, few works have been done for efficient personal data recovery service. Since the privacy protection is a crucial issue for providing a personal data recovery service, a plain data backup-based recovery service is not adequate for public service. Users are not expected to upload their critical data to the internet backup server until they can fully trust the service provider in terms of the privacy protection. In this paper, we propose a novel data recovery service framework on cloud infrastructure, a Parity Cloud Service (PCS) that provides a privacy-protected personal data recovery service. The proposed framework does not require any user data to be uploaded to the server for data recovery. Also the necessary server-side resources for providing the service are within a reasonable bound.

This article presents Athanasia, a user-transparent and fault-tolerant system, for parallel applications running on large-scale cluster systems. Cluster systems have been regarded as a de facto standard to achieve multitera-flop computing power. These cluster systems, as we know, have an inherent failure factor that can cause computation failure. The reliability issue in parallel computing systems, therefore, has been studied for a relatively long time in the literature, and we have seen many theoretical promises arise from the extensive research. However, despite the rigorous studies, practical and easily deployable fault-tolerant systems have not been successfully adopted commercially. Athanasia is a user-transparent checkpointing system for a fault-tolerant Message Passing Interface (MPI) implementation that is primarily based on the sync-and-stop protocol. Athanasia supports three critical functionalities that are necessary for fault tolerance: a light-weight failure detection mechanism, dynamic process management that includes process migration, and a consistent checkpoint and recovery mechanism. The main features of Athanasia are that it does not require any modifications to the application code and that it preserves many of the high performance characteristics of high-speed networks. Experimental results show that Athanasia can be a good candidate for practically deployable fault-tolerant systems in very-large and high-performance clusters and that its protocol can be applied to a variety of parallel communication libraries easily.

While NAND flash memory is used in a variety of end-user devices, it has a few disadvantages, such as asymmetric speed of read and write operations, inability to in-place updates, among others. To overcome these problems, various flash-aware strategies have been suggested in terms of buffer cache, file system, FTL, and others. Also, the recent development of next-generation nonvolatile memory types such as MRAM, FeRAM, and PRAM provide higher commercial value to non-volatile RAM (NVRAM). At today's prices, however, they are not yet cost-effective. In this paper, we suggest the utilization of small-sized, next-generation NVRAM as a write buffer to improve the .overall performance of NAND flash memory-based storage systems. We propose various block-based NVRAM write buffer management policies and evaluate the performance improvement of NAND flash memory-based storage systems under each policy. Also, we propose a novel write buffer-aware flash translation layer algorithm, optimistic FTL, which is designed to harmonize well with NVRAM write buffers. Simulation results show that the proposed buffer management policies outperform the traditional page-based LRU algorithm and the proposed optimistic FTL outperforms previous log block-based FTL algorithms, such as BAST and FAST.

Scalable encoding scheme enables the player or streaming server to adaptively change the playback rate of multimedia content. However, in scalable streaming of layer encoded content, sequential playback of content does not necessarily coincide with the sequential scan of a file. This property introduces another dimension of complexity in the scheduling of data block retrieval. In this work, we develop a novel file organization strategy, harmonic interleaving, which can effectively handle the dynamically changing playback rate of multimedia data retrieval. The proposed scheme not only eliminates the retrieval of unnecessary blocks but also minimizes disk head movement. Via experiment, harmonic interleaving exhibits superior disk utilization on a moderately loaded network.

From file system's point of view, scalable streaming introduces another dimension of complexity in disk scheduling. This is particularly because when a subset of information is retrieved, the playback does not necessarily coincides with the sequential access. We propose a new file organization technique called Harmonic placement. The basic idea is to cluster the frequently accessed layers together. We develop elaborate analytical models for different file organization techniques: Progressive placement, Interleaved placement and Harmonic placement. We investigate the performance of file organization techniques under varying workload conditions. The models developed in this work enables us to predict the performance and efficiency of the storage system in scalable streaming environment. We find that the Harmonic placement outperforms other schemes in scalable streaming environment.

Peer-to-peer (P2P) networks are attracting considerable research interest because of their scalability and high performance relative to cost. One of the important services on a P2P network is the streaming service. However, because each node in the P2P network is autonomous, it is difficult to provide a stable streaming service on the network. Therefore, for a stable streaming service on the P2P network, a fault-tolerant scheme must be provided. In this paper, we propose two new node selection schemes, playback node first (PNF) and playback node first with prefetching (PNF-P), that can be used for a service migration-based fault-tolerant streaming service. The proposed schemes exploit the fact that the failure probability of a node currently being served is lower than that of a node not being served. Simulation results show that the proposed schemes outperform traditional node selection schemes.

Since multizone recording disks have different bandwidths and capacities depending on the zone in use, data placement schemes for traditional constant angular density disks are not suitable for multizone recording disks. In this paper, we propose a new block placement algorithm for multizone recording disks used for continuous media servers. The proposed scheme exploits the bandwidth-saving effect of smoothing variable bit rate data before storing them. The diversity of zone bandwidths in multizone recording disks enables it possible to achieve large smoothing effect using relatively small buffer space. Variable bit rate data blocks of an object are smoothed using multiple smoothing rates which are bandwidths of zones multiplied by the service time assigned to the object and are stored into the corresponding zones. This multirate smoothing technique decreases the buffer space required to provide deterministic service to clients. Simulation results show that a proper restructuring of blocks according to the smoothing algorithm results in dramatic performance enhancement in continuous media servers.

As MZR disks have different bandwidths and capacities depending on the zone in use and the performance of continuous media servers is strongly dependent on the block placement scheme for the disks, data placement schemes for traditional CAD disks are not suitable for MZR disks. In this paper, we propose a new block placement algorithm for MZR disk arrays used for continuous media servers. The proposed scheme exploits the bandwidth-saving effect of smoothing VBR data before storing them. To fully utilize characteristics of MZR disks, VBR data are smoothed using multiple smoothing rates (multiplicity is the number of zones of MZR disk). In addition, as the service time of an object in each round is fixed during the lifetime of the stream, it is easy to determine whether the system can provide deterministic service to the new request, which simplifies admission control. Simulation results show that the proposed scheme outperforms previous schemes such as CRT and NCTT. 1.