If we had the performance information of every application on every SSD, it would be very beneficial to both SSD users and SSD manufacturers. For SSD users, they can buy the SSD that is fastest for the most frequently using applications; for SSD manufacturers, they can figure out the strength and weakness of their SSD for every application. Toward this end, this article proposes a framework named${\sf FORESEE}$FORESEEthat estimates accurately the execution time of a given IO trace (i.e.,query IO trace) of a given application on a target SSDwithout its actual execution.${\sf FORESEE}$is developed based on the observation thatif two IO traces are similar to each other in their IO behavior, their execution times tend to be similar when they are executed on the same SSD. In${\sf FORESEE}$, the execution time of a query IO trace is estimated by using the execution times of the IO traces in a database similar to the query IO trace. Our technical contributions in${\sf FORESEE}$are as follows: (1) we propose a goodness function that efficiently evaluates the quality of sets of features that are used to measure the similarity of IO traces; (2) we propose a DB structure and a searching method for efficiently searching for similar IO traces to a query IO trace; (3) we propose an aggregation method that aggregates the execution times of similar IO traces to a query IO trace for accurately estimating the execution time of the query IO trace; and (4) we verify the effectiveness of${\sf FORESEE}$via extensive experiments by using real-world application IO traces. According to the results, the Pearson correlation coefficient (PCC) of the actual execution time and the estimated execution time by${\sf FORESEE}$is found to be 0.87, indicating${\sf FORESEE}$estimates the execution time accurately.

With the NAND flash memory technology of solid-state drives (SSDs), the usage of SSDs is expanded to various devices. Due to the cost and time limitations of measuring the actual execution time of each application on SSDs, it is difficult for users to determine the best SSD for their most commonly used applications. In this paper, we propose a framework of estimating the execution time of an application IO trace (i.e., a query IO trace) on a target SSD without its real execution. Our framework is based on the observation that if two IO traces are similar in their IO behavior, their execution times tend to be similar when executed on the same SSD. The performance of the framework is evaluated through extensive experiments on real applications. The results show that our framework is accurate in estimating the execution time of an IO trace on SSDs.

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.

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.

Many users want to author assessment items directly from web pages. Existing authoring tools usually use the clipboard to perform the `select-copy-paste' operation on web pages. Because web pages have complicated structures and can include invisible resources, the clipboard-based approach has several limitations: 1) it cannot keep the resource in its original form, 2) it cannot select invisible resources, and 3) it may have to select unnecessary resources. To overcome these limitations, this paper proposes dinsEditor as a browser-based approach for assessment item authoring tools. It is offered as a browser extension that allows the authoring environment to make full use of the resources of web pages and to utilize the exact resources user want. It enables web resources to be maintained in their original form during the assessment item authoring process. The assessment items created through dinsEditor observe the QTI standard by using the QTI Translator.

Nowadays, learners use not only PC but also their own mobile devices for e-learning. For this, author should make various versions of learning contents for device specification and learning management system provides suitable contents for device specification. To select suitable contents for learner's devices, previous researches propose contents selection method that describe suitability score for each attributes and it is hard to express author's intention. Also, it is hard to select learning contents that reflect changed user preference during learning time. In this research, we add user preference on the contents selection method to select suitable contents among various versions contents that has same learning context. Also we use ontology and rules for description of profiles and various intentions. We prove proposed method has similar search performance with previous method.

There are a lot of learning contents, that have the same contexts which are targeted specific devices, under circumstances in which diverse devices support a dasiaU-Learning environmentpsila. But the current methods selecting adequate contents have little flexibility. So, this paper we propose an analogy matched contents selection(AMCS) method which can find similar contents even though there are no exactly matched features of a learnerpsilas device. Also, our research shows that our technique is at most two times more efficient than existing techniques.

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.

In this paper we present a methodology for designing competency based Web search framework using ontology to acquire the learning contents that meets the individualspsila competency. We used ontology with OWL-DL to built competency ontology, content ontology and domain ontology to implement competency based contents search system. The basic ontology-based search system, however, lacks the method of ranking the search results. To present more appropriate learning contents for the learner, we applied competency clustering to rank the search results.

This paper describe the case study of developing and providing the ontology-based educational information search service to overcome the limitations of the existing keyword-based search service. We used the OWL-DL 1) to build the core ontology DB from the existing metadata DB represented by the LOM, 2) to build the domain ontology DB of middle school Mathematics contents DB, 3) to merge the core ontology DB and the domain ontology DB. And both the OWL-DL and the SWRL are used to support the educational information search service. The system for the above service was built using the OWL-based deduction engine and the merged ontology DB, that process and deduct user query with enhanced simple and intuitional interface. This service is being provided as the demonstrational service by the EDUNET - the largest nationwide learning contents search system in Korea.

An essential function of the multimedia DBMS is to present objects of the query result according to their temporal-spatial relations. These relations can be specified within the database schema on the class level and used for each object presentation. In the case of partial extraction, however, the retrieved objects may not belong to an existing class, but to a newly created class which does not have temporal-spatial relations. For the presentation of such objects, most systems make users perform authoring of the temporal-spatial relations of the objects. The paper proposes a scheme which generates presentation schedules for the query result whether the objects of the query result belong to existing classes or not. In this scheme, a TS-tree (temporal/spatial tree) is defined as the temporal-spatial structure of a class and the presentation schedules for objects of the class are generated from the tree. The TS-tree for objects which belong to a newly created class can be automatically generated from existing TS-trees. Consequently users are quite relieved from authoring efforts.