With the increasing advancements in NVMe storage devices, there is a focus on enhancing their intelligence and optimizing performance. Recently, Flexible Data Placement (FDP) has emerged as a key topic in maximizing SSD performance by reducing the write amount to flash memory. However, obtaining testable devices equipped with FDP functionality poses a significant challenge due to limited availability and inherent feature limitations of prototype devices. We propose a realistic FDP emulator, FDPVirt, based on NVMeVirt, allowing researchers to explore various data placement strategies without physical hardware. Our proposed design addresses the feature limitations of actual prototype devices by adding functionality to accumulate both external and internal write amounts, enabling the calculation of the Write Amplification Factor (WAF). By evaluation, the results reveal that the performance of FDPVirt successfully emulates a real FDP prototype SSD, with average differences of 11.1% for read and 4.1% for write performance. Furthermore, WAF is improved by up to 26.3% by utilizing fine-grained data placement of FDPVirt.

Recently, flash-based solid-state drives (SSDs) are widely used in industry and academia due to their higher bandwidth and lower latency compared with traditional hard disk drives (HDDs). Furthermore, SSDs with the Non-Volatile Memory Express (NVMe) interface can provide higher performance and ultra-low latency compared with the Serial AT Attachment (SATA) SSDs. Due to their high performance, NVMe SSDs are adopted in many systems as fast storage devices. However, the performance of NVMe SSDs can be negatively affected by I/O access patterns. For example, random write access patterns can have negative impacts on performance due to the unique characteristics of SSDs such as out-of-place update and garbage collection. In this paper, we propose an address remapping scheme to improve the I/O performance of NVMe SSDs. Our proposed scheme transforms random access patterns into sequential access patterns in the NVMe device driver. This allows our scheme to improve the I/O performance of NVMe SSDs while supporting widely used file systems such as EXT4, XFS, BTRFS, and F2FS without any modification to the device. Experimental results show that our proposed scheme can improve the performance of NVMe SSD by up to 64.1% compared with the existing scheme.

Cloud computing is being widely adopted in the industry due to improve resource utilization and provide more computation power. In cloud computing systems, many numbers of users execute various types of applications that produce a large amount of data. To store large data, distributed file systems are used in many cloud computing systems. Recently, to improve the performance of distributed file systems, emerging flash-based solid-state drives (SSDs) are widely adopted since they provide high performance compared with existing hard disk drives (HDDs). However, due to the characteristics of SSDs, the performance of distributed file systems is greatly impacted by the access patterns from the applications. For example, the performance of random writes in SSDs is lower than that of sequential writes. In this article, we propose an address reshaping technique for SSDs in distributed file systems to improve the performance of random writes by transforming the access patterns. Our scheme reshapes random write requests into sequential write requests in the distributed file systems. This enables the distributed file systems to issue the sequential write requests to SSDs. The experimental results show that the proposed scheme improves performance by up to 46.26% compared with the existing scheme.

A blockchain is designed to make consistent and reliable agreement in an unreliable and decentralized environment. It also permits processing transactions, making smart contracts, which allows end users to perform the contracts without any intermediate entities. However, there are some challenges in retrieving the state in a smart contract on the blockchain. For example, an external database or user-defined data structures can be used to retrieve the data from a smart contract in a range, which can increase the management overhead and decrease the overall performance of the blockchain system. In this paper, we propose a scheme that enables SQL query operations in a blockchain system. In our proposed scheme, the register and query managers provide fast retrieval of range data without any user-defined data structure, and management at low cost without any external database, respectively. We have implemented our scheme on quorum which is an Ethereum-based blockchain system and evaluated it using a synthetic benchmark. The experimental results show that our system can improve the search performance up to about 22x compared with the existing system with low memory usage.

With the advent of HPC (High-Performance Computing) Cloud, storage systems based on parallel file systems such as Lustre have become one of important components to support various types of workloads from many users having different storage access patterns. One of the solutions to adapt to such various I/O access patterns is Progressive File Layout (PFL) which can adjust the chunk size and stripe pattern dynamically according to the I/O traffics. However, PFL still has a performance limitation to the small file I/O due to the HDD specific hardware characteristics. In this paper, we propose an optimized Progressive File Layout (PFL) method which can effectively leverage a combination of SSDs and HDDs to support mixed sizes of I/O workloads. By dynamically directing relatively small file I/O operations into the SSD based storage devices, our framework can significantly improve the performance. We have performed a comparative analysis of our PFL on SSDs in Lustre file system with PFL on HDDs and vanilla Lustre configuration with HDDs. Experimental results from various workloads with mixed sizes of I/O threads on our testbed show that the proposed PFL on SSDs can achieve enhanced total I/O throughput by more than six times compared to conventional solutions.

Storage area network (SAN) is one of the most popular solutions for constructing server environments these days. In these kinds of server environments, HDD-based storage usually becomes the bottleneck of the overall system, but it is not enough to merely replace the devices with faster ones in order to exploit their high performance. In other words, proper optimizations are needed to fully utilize their performance gains. In this work, we first adopted a DRAM-based SSD as a fast backend-storage in the existing SAN environment, and found significant performance degradation compared to its own capabilities, especially in the case of small-sized random I/O pattern, even though a high-speed network was used. We have proposed three optimizations to solve this problem: (1) removing software overhead in the SAN I/O path; (2) increasing parallelism in the procedures for handling I/O requests; and (3) adopting the temporal merge mechanism to reduce network overheads. We have implemented them as a prototype and found that our approaches make substantial performance improvements by up to 39% and 280% in terms of both the latency and bandwidth, respectively.

Multiple trust sources may be needed for broadcast in wireless ad hoc networks. For example, multiple base stations may be employed in some wireless sensor networks, or multiple trusts may be distributed among multiple routing nodes in multi-hop routing protocol. Most of the previously proposed multicast/broadcast authentication protocols assume a single source of trust. With multiple trust sources, it becomes difficult to design resource-efficient authentication protocols for multicast/broadcast services. Simply extending traditional approaches will result in increased bandwidth and memory consumptions in proportion to the number of trust sources. In this paper, we propose a new scheme utilizing Combined Key Chains. If there are m trust sources, our scheme generates m+1 key chains, where m of them are distributed among the m source nodes and the last one is used as a Verification Key Chain in all the receiver nodes. The communication overhead is small and constant, and the memory requirement at a verifier node is also minimal. Copyright (c) 2014 John Wiley & Sons, Ltd.

Recently, the demand for fast storages is rapidly increasing in HPC environments such as cloud platforms, social network services and desktop users. But HDD-based storages cannot satisfy these demands, and a variety of high performance storages providing lower I/O latency and higher I/O bandwidth have been eagerly developed. Although merely adopting these fast devices in the storage system can take some advantages, it cannot fully utilize their high performance. So, proper optimizations are needed. In this work, we focus on the granularity of the I/O request from the application layer to the block layer. We found that the I/O operation at the page granularity causes huge performance degradation in the case of small size random I/O patterns that are often observed during the execution of mail servers, DB servers, etc. This is because it allows non-requested data to be transferred. Therefore, we propose new file system design that contains two optimizations; 1) an extended I/O interface maintaining user requested data size over all the layers in I/O subsystem, and 2) sub-page mechanism for minimizing non-requested data transfer effectively. We have implemented our approach in the Linux file system. The experimental results show that our solution achieves 1.6 to 6.3 times performance gains.

As the cloud markets grow, the cloud providers are faced with new challenges such as reduction of power consumption and guaranteeing service level agreements (SLAs). One reason for these problems is the use of server consolidation policy based on virtualization technologies for maximizing the efficiency of resource usage. Because current virtualization technologies do not ensure performance isolation among active virtual machines (VMs), it is required to consider resource usage pattern of VMs to improve total throughput and quality of service. In this paper, we propose a virtual machine scheduler for multicore processors, which exploits the last-level cache (LLC) reference ratio. Specifically, we focus on the performance impact of contention in a shared LLC. We have found that the ratio of the number of LLC references to that of instructions (LLC reference ratio) is highly associated with the amount of cache demand, and a Performance-Maximizing VM (PMV) scheduling algorithm can be devised by using the ratio. We show that our PMV scheduler is effective by evaluation for various workloads.

In this article, we present a skyline-based matchmaking framework. The current method of carrying out the matchmaking procedure identifies items based on users' specifications. We rethink matchmaking procedures in such a way that they can find items that can satisfy a specific computing demand from a user and recommend a collection of better candidates among the identified items. This endows a user with the right of choice on deciding the best-possible items. We approach the recommendation from the perspective of skyline computation and present an efficient skyline algorithm that gathers interesting item candidates efficiently. To devise an efficient sequential skyline algorithm, we adopt (i) lattice-based indexing using a lattice composition technique and (ii) an optimized dominance-check algorithm. Moreover, we parallelize the algorithm using breadth-first-search (BFS). Our extensive experimental results show that our algorithm outperforms current state-of-the-art algorithms, and the speedup factor of the parallelized algorithm is near-linear.

This paper proposes a scheme to enhance energy efficiency and QoS of real time network applications on smart phone. The scheme reduces energy consumption and increases the successful interaction rate between the client at smart phone and the busy server of real time network application by deploying a surrogate of the client at smart phone in cloud computing environment. All interactions among the client at smart phone, the application server and the surrogate in the cloud are controlled by tokens. The proposed scheme considers security as well as energy waste in the cloud.

A distributed file system (DFS) is any file system that allows access to files from multiple hosts via a shared computer network. Such a system allows multiple users on multiple machines to share files and storage resources. Increasing reliability and usability is a critical issue for distributed file systems. In this paper, we need to thoroughly analyze the architecture of the Parallel Virtual File System (PVFS), and further propose the possibility of enhancing the fault-tolerance and reliability of PVFS.

We propose a framework for sports prediction using Bayesian inference and rule-based reasoning, together with an in-game time-series approach. The framework is novel in three ways. The framework consists of two major components: a rule-based reasoner and a Bayesian network component. The two different approaches cooperate in predicting the results of sports matches. It is motivated by the observation that sports matches are highly stochastic, but at the same time, the strategies of a team can be approximated by crisp logic rules. Furthermore, because of the rule-based component, our framework can give reasonably good predictions even when statistical data is scanty: it can be used to predict results of matches between teams which have had few previous encounters. Machine learning techniques have great difficulty in handling such situations of insufficient data. Second, our framework is able to consider many factors, such as current scores, morale, fatigue, skills, etc. when it predicts the results of sports matches: most previous work considered only one factor, usually the score. Third, in contrast to most previous work on sports results prediction, we use a knowledge-based in-game time-series approach to predict sports matches. This approach enables our framework to reflect the tides/flows of a sports match, making our predictions certainly more realistic, and somewhat more accurate. We have implemented a football results predictor called FRES (Football Result Expert System) based on this framework, and show that it gives reasonable and stable predictions.

Many sports fans are interested in predicting the results of sports matches. But predicting the result of a match is difficult because there are many factors to be considered. Bayesian inference and rule-based reasoning are widely used scientific techniques to predict future information including sports results, stock prices, etc. In this paper we propose a novel framework for sports prediction using these techniques together with an in-game time-series approach. Our framework is novel and unique in three ways. Firstly, the framework consists of two major components: a rule-based reasoner and a Bayesian network component. This is a compound one in the sense that two different methods cooperate in predicting the results of sports matches. The concept of combining two different techniques into one framework is motivated by the fact that most sports results are highly stochastic, but at the same time, the strategies of a team (or a player) can be determined by certain crisp logic rules. Furthermore, because of the rule-based component, our framework can give reasonably good predictions even when statistical data are scanty, so it can be used to predict the result of a match between two teams (or players) which have had few or no previous encounters; while most machine learning techniques may not be used because they can suffer from insufficient data. Secondly, our framework considers many factors like current scores, morale, fatigue, skills, etc. when it predicts the results of sports matches; while most previous works considered only one factor, usually score, or few factors. Thirdly, contrary to most previous works on sports prediction we use a knowledge-based in-game time-series approach to predict sports matches. This approach enables our framework to reflect the tides/flows of a sports match and makes our prediction certainly more realistic, and somewhat more accurate. We have implemented a football results predictor called FRES (Football Result Expert System) based on our framework, and show that it gives reasonable and stable predictions.