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This research presents software for empirically analyzing Java Virtual Machine (JVM) parameter configurations to enhance web application performance. Using tools like JMeter and cAdvisor in a controlled hardware environment, it collects and analyzes performance metrics. Tailored JVM settings for high request loads improved CPU efficiency by 20% and reduced memory usage by 15% compared to standard configurations. For I/O intensive operations with large files, optimized JVM configurations decreased response times by 30% and CPU usage by 25%. These findings highlight the impact of tailored JVM settings on application responsiveness and resource management, providing valuable guidance for developers and engineers.

NAND flash-based solid-state drives have revolutionized storage solutions in resource-restrictive electronics owing to their compact size, low power consumption, and high shock resistance. However, they face limitations such as asymmetrical read, write, and erase operations, leading to write amplification (WA), decreased performance, and shorter lifespan. This paper introduces clustering sequential writes and cold isolation (CSWCI), a novel data placement scheme that addresses two critical challenges: identifying sequential writes hidden within random input/output streams and efficiently migrating cold pages. CSWCI employs logical address-based clustering to detect sequential patterns, enhancing spatial locality and reducing random writes. Additionally, it accurately isolates frozen pages that have not been used recently and are not expected to be used again during garbage collection, thus minimizing unnecessary migrations and WA. Unlike conventional methods, CSWCI optimizes memory usage, aligning with the low power consumption requirement of mobile devices such as smartphone and tablet PC. Experimental results demonstrate a maximum 33% reduction in valid page copies, which typically worsens WA compared to existing schemes, indicating improved performance and durability.

Waste management has been identified as a critical problem underpinning the vision of sustainable cities. Despite existing efforts to tackle this challenge, traditional approaches still undermine resource allocation and utilization for the task. Most methods have overlooked the fairness problem found in the garbage-collection transportation units, producing noticeable varying active time differences among the participating units. The integration of Internet of Things (IoT) to waste management has recently been an emerging trend, which offers coordinated garbage collection benefiting from IoT networks. This work investigates utilization of LoRa technology for such a purpose and argues the importance of fairness perspective in actuating the truck agents for collecting the wastes in a given region. Through enabling reliable waste data exchange by a proper design and specification of the LoRa network, we can then cast the collection issue as a capacitated vehicle routing problem with a well-formulated objective function. To improve the fairness in the waste management, we propose an objective function that incorporates both the total distance covered by all the trucks and overall distance dispersion from individual trucks. Numerical simulation demonstrates the superiority and consistency of the fairness-based optimum solution in both minimizing the total distance and achieving the fairness.

The explosive growth of massive data makes shingled magnetic recording (SMR) disks a promising candidate for balancing capacity and cost. SMR disks are typically configured with a persistent buffer to reduce the read–modify–write (RMW) overhead introduced by non-sequential writes. Traditional SMR zones-based persistent buffers are subject to sequential-write constraints, and frequent cleanups cause disk performance degradation. Conventional magnetic recording (CMR) zones with in-place update capabilities enable less frequent cleanups and are gradually being used to construct persistent buffers in certain SMR disks. However, existing CMR zones-based persistent buffer designs fail to accurately capture hot blocks with long update periods and are limited by an inflexible data layout, resulting in inefficient cleanups. To address the above issues, we propose a strategy called Amphisbaena. First, a two-phase data block classification method is proposed to capture frequently updated blocks. Then, a locality-aware buffer space management scheme is developed to dynamically manage blocks with different update frequencies. Finally, a latency-sensitive garbage collection policy based on the above is designed to mitigate the impact of cleanup on user requests. Experimental results show that Amphisbaena reduces latency by an average of 29.9% and the number of RMWs by an average of 37% compared to current state-of-the-art strategies. [ABSTRACT FROM AUTHOR]

Message Queue (MQ) services play a vital role in modern distributed systems as they enable asynchronous communication between services and facilitate the decoupling of various components of the system. Among the many MQ services available, Kafka, Apache Pulsar, Artemis, and RocketMQ are popular choices, each offering unique features and capabilities. As the adoption of MQ services continues to grow, choosing the appropriate service that can meet the requirements of the system has become increasingly challenging. Therefore, a comprehensive comparison of these services is crucial to determine the most suitable one for a specific use-case. This research paper presents a thorough evaluation of these MQ services based on critical metrics such as CPU utilization, memory usage, garbage collection, latency, and throughput. Based on our extensive review, no other research has delved into such a detailed evaluation, thereby establishing our work as a cornerstone in this field. The results of our study offer valuable insights into the strengths and limitations of each service. Our findings indicate that each message queue behaves differently inside the Java Virtual Machine (JVM). This work aims to assist developers and researchers in strategically deploying and optimizing MQ services based on specific system and use-case requirements. In addition to providing machine metrics, our results demonstrate the performance of each message queue under different load scenarios, making it a valuable resource for those seeking to ensure the effective functioning of their MQ services. [ABSTRACT FROM AUTHOR]

Recycling is an important part of the solid waste management system. However, community engagement in this activity remains relatively low. The presence of a group of individuals engaged in collecting recyclable materials at landfill sites has contributed to improving solid waste management performance. This paper aims to identify the background and activities of scavengers in collecting recyclable items at landfills, as well as the environmental impact of the scavenging activity. This study was based on questionnaires distributed to 46 scavengers in the study area. The Life Cycle Assessment (LCA) method was used to assess the environmental impact of using recyclable materials in manufacturing products. The LCA analysis could identify the contribution of recycled materials to the total savings of carbon dioxide (CO2), methane (CH4), and nitrogen dioxide (N20) by using recycled materials in the production of a new product. According to the study, the majority of those involved in the scavenging activity are immigrants from Indonesia and the Philippines. Despite safety and health concerns, their efforts to earn a living through waste scavenging are extremely valuable. Furthermore, the findings show that their contribution to the collection of recyclable materials cannot be denied. It is because the presence of this group is essential in a country where recycling awareness is low. Furthermore, the indirect contribution to the environment is important, particularly in reducing the use of natural materials in producing new materials. As a result, the government must devise a more effective strategy for recycling programs by involving all stakeholders.

An algorithm for optimizing the routes of a set of vehicles used for the collection and removal of municipal solid waste in a metropolis is proposed. The algorithm eliminates the problem of applying heuristic methods for multi-agent optimization, which is NP non-deterministic polynomial-time-hard. The application of the algorithm leads to a guaranteed exact solution. Through the application of restrictions on the carrying capacity of vehicles, the size of the input matrix representing the transport network can be reduced to an adequate size. This process uses statistical information about the filling levels of container waste bins. The algorithm is applied to an example of two megacities. The shortest routes are built for different numbers of points (from 12 to 72) on the route. The dependence of the total mileage on the number of involved vehicles is studied.

Garbage collection algorithms are widely used in programming languages like Java. However, selecting the most suitable garbage collection (GC) algorithm for an application is a complex task since they behave differently regarding crucial performance metrics such as garbage collection pause time, application throughput, and memory usage. This challenge is particularly more complicated as there is currently no available tool to assist users/developers in this critical decision-making process. In this paper, we address this pressing need by conducting an extensive evaluation of four widely used GCs (G1, Parallel, Shenandoah, and ZGC) in OpenJDK, considering application throughput, GC pause time, and various heap sizes. Building upon this evaluation, we present BestGC, a novel system that suggests the most suitable GC solution based on user-defined performance goals in terms of application throughput and GC pause time. Our evaluation of BestGC using multiple workloads demonstrates its effectiveness in suggesting the most suitable GC category (concurrent or generational/non-fully concurrent GC) in approximately 86% of the experiments on average. Additionally, BestGC accurately identifies the best GC in approximately 52% of the cases on average. Even in situations where BestGC failed to suggest the exact best GC or GC category, the suggested GC still outperforms the default GC (G1) in the JDK, exhibiting an average improvement of 1.75%. Notably, BestGC is designed to be easily extensible, facilitating its compatibility with other JDK versions, as well as new GCs and heap sizes. By addressing the lack of a practical tool to aid in GC selection, our research makes a significant contribution to the field of performance optimization in Java applications.

Because of their numerous benefits, solid-state drives (SSDs) are increasingly being used in a wide range of applications, including data centers, cloud computing, and high-performance computing. The growing demand for SSDs has led to a continuous improvement in their technology and a reduction in their cost, making them a more accessible storage solution for a wide range of users. Garbage collection (GC) is a process that reclaims wasted storage space in NAND flash memories, which are used as the memory devices for SSDs. However, the GC process can cause performance degradation and lifetime reduction. This paper proposes an efficient garbage collection (GC) scheme that minimizes overhead by invoking GC operations only when necessary. Each GC operation is executed in a specific order based on the expected storage gain and the execution cost, ensuring that the storage space requirement is met while minimizing the frequency of GC invocation. This approach not only reduces the overhead due to GC, but also improves the overall performance of SSDs, including the latency and write amplification factor (WAF) which is an important indicator of the longevity of SSDs. [ABSTRACT FROM AUTHOR]

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In terms of technological advancement, digital circuit design plays a vital role. Every application needs efficient designs for high-speed and low-power consumption devices. The need for resource constraint devices is overgrowing because of the increased adaptation of IoT and Wireless Sensor Networks for home and industrial automation. For any digital components for such devices, multiplier modules are essential in the digital circuit design. Effective multiplier designs have achieved the digital components' speed, delay, and power efficiency. The Vedic multiplier is considered the most efficient among the various multipliers due to its ability to process inputs in parallel. Another major challenge in digital circuit design is reducing power consumption and avoiding information loss. In terms of irreversible logic, heat is wasted during computing due to information destruction, which is not concerned with processing. To overcome this situation, reversible logic has been introduced with reversible gates. Thereby, this work aims to explore the possibility of developing reversible Vedic multipliers with reduced delay and higher power efficiency with improved design and implementation aspects. In this work, three models with Vedic Multiplier designs using different reversible logic circuits with low power, speed, and area-efficient reversible logic are proposed to address the lower hardware complexity resulting in a better speed of operations and reduction of power dissipation. The low number of adders in the Vedic multiplier diminishes area and power dissipation and increases the operational speed. The proposed reversible logic methods achieved low power consumption with zero information loss and increased delay. [ABSTRACT FROM AUTHOR]

The explosive growth of massive data makes shingled magnetic recording (SMR) disks a promising candidate for balancing capacity and cost. SMR disks are typically configured with a persistent buffer to reduce the read–modify–write (RMW) overhead introduced by non-sequential writes. Traditional SMR zones-based persistent buffers are subject to sequential-write constraints, and frequent cleanups cause disk performance degradation. Conventional magnetic recording (CMR) zones with in-place update capabilities enable less frequent cleanups and are gradually being used to construct persistent buffers in certain SMR disks. However, existing CMR zones-based persistent buffer designs fail to accurately capture hot blocks with long update periods and are limited by an inflexible data layout, resulting in inefficient cleanups. To address the above issues, we propose a strategy called Amphisbaena. First, a two-phase data block classification method is proposed to capture frequently updated blocks. Then, a locality-aware buffer space management scheme is developed to dynamically manage blocks with different update frequencies. Finally, a latency-sensitive garbage collection policy based on the above is designed to mitigate the impact of cleanup on user requests. Experimental results show that Amphisbaena reduces latency by an average of 29.9% and the number of RMWs by an average of 37% compared to current state-of-the-art strategies.

An algorithm for optimizing the routes of a set of vehicles used for the collection and removal of municipal solid waste in a metropolis is proposed. The algorithm eliminates the problem of applying heuristic methods for multi-agent optimization, which is NP non-deterministic polynomial-time-hard. The application of the algorithm leads to a guaranteed exact solution. Through the application of restrictions on the carrying capacity of vehicles, the size of the input matrix representing the transport network can be reduced to an adequate size. This process uses statistical information about the filling levels of container waste bins. The algorithm is applied to an example of two megacities. The shortest routes are built for different numbers of points (from 12 to 72) on the route. The dependence of the total mileage on the number of involved vehicles is studied. [ABSTRACT FROM AUTHOR]

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.

One of the most important features of solid-state drives (SSDs) is the ability to update data sectors out of place, performing garbage collection operations within its physical blocks, to mitigate limited flash memory lifespan. In SSDs, a firmware called the Flash Translation Layer (FTL) is used to hide such features from the operating system. One way to extend the lifespan of flash memories is to use data compression at the FTL level. In compression-based FTLs, the address mapping type has a significant effect on the efficiency and read/write speed. The type of address mapping in previous similar schemes is based on page-level address translation and requires high memory space for the mapping table. In this paper, we propose a compression-based hybrid FTL called In Block Update Address Mapping, in which the pages of each logical block are compressed, and fewer physical pages are written in physical blocks. Therefore, a small number of free pages remain. In the proposed scheme, the required memory of the mapping table has been reduced by 78% when compared to similar schemes. Moreover, extensive simulation results reported in this paper show that the proposed FTL scheme outperforms other FTL schemes in the read and write operations under realistic benchmark workloads.

Flash-based solid state drives (SSDs) are widely adopted in both industry and the academia since SSDs offer higher performance, lower latency, and lower power consumption compared with traditional hard disk drives (HDDs). Unfortunately, the performance of SSDs can be affected by I/O access patterns. For example, random I/O operation degrades the SSD performance compared with sequential I/O operation since the random I/O operation reduces the spatial locality and increases garbage collection (GC) overhead. To handle this issue, in this article, we propose an efficient block address transformation scheme in the block layer to improve both performance and portability. To do this, we first transform random access patterns to sequential access patterns by sequentializing the block addresses in the block layer. Second, we devise a mapping table for managing transformed block addresses. Third, we support correct read operations and transaction processing for updating the mapping table to avoid sacrificing the consistency. Finally, we provide a cleaning scheme to reclaim invalid data generated by the transformed sequential access. This scheme increases spatial locality, reduces GC overhead, and operates well on any file systems or devices. Experimental results show the proposed scheme improves performance by up to 2x, 1.86x, 2.15x, and 42.8% compared with existing scheme in the case of diverse file systems such as EXT4, XFS, BTRFS, and F2FS, respectively.

The advancement of multi-level cell technology that enables storing multiple bits in a single NAND flash memory cell has increased the density and affordability of solid-state drives (SSDs). However, increased latency asymmetry between read and write (R/W) intensifies the severity of R/W interference, so reads cannot be processed for a long time owing to the extended flash memory resource occupancy of writing. Existing flash translation layer (FTL)-level mitigation techniques can allocate flash memory resources in a balanced manner taking R/W interference into account; however, due to the inefficient utilization of parallel flash memory resources, the effect on performance enhancement is restrictive. From the perspectives of the predicted access pattern and available concurrency of flash memory resources, we propose a parallelism-aware channel partition (PACP) scheme that prevents SSD performance degradation caused by R/W interference. Moreover, an additional performance improvement is achieved by reallocating interference-vulnerable page using leveraged garbage collection (GC) migration. The evaluation results showed that compared with the existing solution, PACP reduced the average read latency by 11.6% and average write latency by 6.0%, with a negligible storage overhead. [ABSTRACT FROM AUTHOR]

Due to the fast-growing amount of data and cost consideration, shingled-magnetic-recording (SMR) drives are developed to provide low-cost and high-capacity data storage by enhancing the areal-density of hard disk drives, and (data) deduplication techniques are getting popular in data-centric applications to reduce the amount of data that need to be stored in storage devices by eliminating the duplicate data chunks. However, directly applying deduplication techniques on SMR drives could significantly decrease the runtime performance of the deduplication system because of the time-consuming SMR space reclamation caused by the sequential write constraint of SMR drives. In this article, an SMR-aware deduplication scheme is proposed to improve the runtime performance of SMR-based deduplication systems with the consideration of the sequential write constraint of SMR drives. Moreover, to bridge the information gap between the deduplication system and the SMR drive, the lifetime information of data chunks is extracted to separate data chunks of different lifetimes in different places of SMR drives, so as to further reduce the SMR space reclamation overhead. A series of experiments was conducted with a set of realistic deduplication workloads. The results show that the proposed scheme can significantly improve the runtime performance of the SMR-based deduplication system with limited system overheads. [ABSTRACT FROM AUTHOR]

Bit-alterable flash memory is a cutting-edge technology that enables a novel operation, called page-level erase operation, to erase a flash page within a block arbitrarily. Though the page-level erase operation can ease the overhead of the live-page copying during a garbage collection process, it also introduces a new wear-leveling problem. In such the problem, flash pages within the same block will receive different program/erase (P/E) cycles during runtime. Some specific pages storing hot data will be worn out soon; therefore, the lifespan of the bit-alterable flash memory will be short due to the uneven worn out of flash pages. Consequently, it becomes a critical issue with the bit-alterable flash memory, and the state-of-art wear-leveling designs cannot resolve this problem. For tackling the problem, this study presents a duo-phase data management scheme considering the page-level wear-leveling issue. The duo-phase mechanism simultaneously cares about page- and block-level wear-leveling issues. On the page-level wear-leveling problem, the duo-phase scheme identifies hot flash pages via the few bits and softly restricts the hot flash page to store hot data without sacrificing storage capacity. On the other hand, our proposed mechanism also figures out the solution to minimize the number of copied live pages for the block-level wear-leveling issue. According to the experimental results, our duo-phase scheme can prolong the lifespan of the bit-alterable flash memory by 1.5 to 1.9 times, compared with the state-of-art garbage collection mechanisms. [ABSTRACT FROM AUTHOR]

In the computer science community, garbage collection is a dynamic storage management technology to ensure the reliability of computer systems. In this paper, we consider two garbage collection policies to meet the goal of time consumption for a generational garbage collector when increase in objects might be unclear at discrete times for the high frequency of computer processes. That is, (a) tenuring collection is triggered at the n th minor collection preventively or at a threshold amount K of surviving objects correctively, and (b) major collection is made at discrete times J T for a given T or at the N th collection including minor and tenuring collections. Using the damage process and renewal theory, the expected cost rates are obtained, and their optimal policies for tenuring and major collection are discussed analytically and computed numerically. [ABSTRACT FROM AUTHOR]

With the increased density and the technology scaling, flash memory is more vulnerable to noise effects, overwhelmingly lowering the data retention time. The periodic data refresh technique is commonly used to retain the long-term data integrity. However, the frequent refresh requests introduce the increased access conflict and severe write amplification, leading to suboptimal performance and lifetime improvement of flash memory-based storage systems. In this article, we propose ApproxRefresh, which enables the uncorrectable data reuse with the assistance of approximate-computing applications to reduce the refresh costs. Specifically, we design a lightweight remapping-free refresh technique, called RFR, to periodically correct, compute an enhanced ECC, and only remap new ECC parity bits, which dramatically reduces the refresh costs. Then, with approximate-read and precise-read hotness awareness, ApproxRefresh selectively adopts RFR or the traditional remapping-based refresh technique to reduce the refresh costs and the read disturbance. Besides, ApproxRefresh further improves flash access performance based on data hotness and process variations. In addition, a corresponding ApproxRefresh-aware garbage collection algorithm is proposed to complete the design. Evaluations show that ApproxRefresh reduces the refresh latency by 36.19% over the state of the art. [ABSTRACT FROM AUTHOR]

Given the "out-of-place" nature of write mode, Solid State Drive (SSD) has to execute a garbage collection (GC) to reclaim the space of invalidated data and free flash blocks for new written data. However, the GC can incur the movements of valid data inside SSD, resulting in the problems such as write latency, write amplification, etc. The GC overheads of SSD depend not only on the current write pattern but also on how data have been already placed in SSD. If data can be classified by their lifetime when written to SSD, and the data with similar lifetime are written into the same flash block, we can select the block with the shortest expected lifespan as the victim block. Ideally, the victim block contains few valid data, implying there are few valid pages that need to be migrated during the GC process with greatly reduced overhead. In this paper, we propose and implement a GC method, called Lifespan-based GC , for SSDs based on the data lifetime, together with the I/O distribution and the dynamic flash memory allocation. The results show that for those applications with the data lifetime characteristics, the lifespan-based GC can effectively reduce the amount of valid pages migrated in GC process, thereby minimizing the write latency and write amplification caused by GC. • Introduce a method for reasonably predicting the data lifetime. • Design an I/O distribution mechanism. • Propose a method of dynamically allocating flash blocks on demand. • Present a lifespan-based garbage collection algorithm. [ABSTRACT FROM AUTHOR]

In the past ten years, solid-state drive (SSD) has become one of the mainstream storage devices due to its performance advantages. However, how to reduce the impact of garbage collection operations on host-side IO is still a challenging issue in SSD firmware design. To solve this problem, this paper proposes a uniform scheduling of interruptible garbage collection and host IO (USIGC). The main contributions of USIGC include: First, USIGC sets up an interruptible garbage collection sub-request queue, and then uniformly schedules this sub-request queue with the host IO queue. In this way, the idle time of each channel is fully utilized to complete valid page migration and erase operation of interrupt garbage collection. Second, USIGC predicts the probability that the erase operation can be completed in the current idle time based on the historical idle interval and then makes the erase operation decision to reduce the probability of blocking the host IO. Third, by converting the amount of data that can be written in future to the present and taking the number of invalid pages of the current block as the selection basis of the victim block, the garbage collection and wear-leveling can be unified. Experimental results show that, compared with existing works, USIGC can reduce the average response time and max wait time and achieve the best wear-leveling performance. [ABSTRACT FROM AUTHOR]

NAND Flash-based solid-state drives (SSDs) have been widely used as secondary storage devices due to their faster access speed, lower power consumption, and higher reliability compared with hard disk drives. However, application I/O performance can be significantly affected by garbage collection (GC) inside SSDs. For example, a GC operation usually moves valid pages from a victim block into a clean block in a serialized manner. Thus, it increases the GC time and affects the application I/O performance. To address this issue, this article presents a preliminary study on a parallel GC scheme for flash-based SSDs. In our scheme, we parallelize valid page migrations during a GC operation to reduce the total GC time. To do this, we first propose a new flash chip architecture that enables valid page migrations in parallel. Second, we collect information such as new addresses for the migration of valid pages by considering the restriction of SSD operations. Finally, we employ multiple worker threads to migrate the valid pages using the collected information in a parallel manner. We implement and evaluate our scheme using Disksim with Microsoft SSD extension. The experimental result shows that the proposed scheme reduces the overall GC time by 70% and 57% on average compared with the existing scheme and a state-of-the-art GC scheme IPPBE, respectively.

Aiming at the unique properties of NAND flash, an average update frequency based NAND flash garbage collection algorithm is proposed. Firstly, the victim block selection strategy based on invalid page age sum is adopted. Then, based on FaGC and GCbAH algorithm, the proposed algorithm re-defines the heat degree calculation method, and applies average update frequency of logical page to replace fixed threshold as the criterion of cold or hot data. Thus, more accurate determination and separation of cold and hot data are realized. The experimental simulation results show that compared with GR, CB, CAT, FaGC and GCbAH algorithms, the proposed algorithm has achieved a better performance in terms of garbage collection overhead and wear leveling.

A predictive based proactive garbage collection algorithm was proposed.Firstly,the data was separated according to different heat factors,then the upper and lower predictions were performed on the number of different types of page allocation requests (PAR) that would be reached in the future.While satisfying the page allocation request lower prediction,the PAR upper prediction requirement was maximally satisfied,the WA problem was optimized,and invalid effective data migration was reduced,thereby maximizing the garbage collection utility.A mathematical model was defined for this problem,and an algorithm for obtaining the approximate optimal solution was given.The applicable scenario of the model was analyzed.The practical results show that the algorithm can obtain the maximum benefit and can significantly improve the performance of SSD and reduce the cost.

One of the limitations of flash memory in real-time and high dependability systems is its need for garbage collection, resulting in performance degradation due to non-deterministic response times. Recent work has presented RAID architectures for solid state storage systems. These RAID architectures increase the dependability from a data storage perspective but they do not provide application level dependability when real-time response times are required. In this study we present a garbage collection aware Flash Translation Layer that offers guaranteed access time to a solid state RAID array by managing incoming requests and preventing them from being blocked by ongoing garbage collection. We present a novel serial technique with a dynamic page allocation mechanism that eliminates non-deterministic behaviours of the garbage collectors in the array. The result is real-time access guarantees that maintain the data dependability enhancements using a run time parity migration technique. The mechanisms are evaluated using a trace driven simulator and a number of synthetic and realistic traces. Simulation results indicate that the garbage collection aware techniques offer improved upper bound response times for I/O requests of up to 73% compared to an existing mechanism, without disturbing the data dependability at the storage level. Traces dominated by random writes exhibit similarly significant enhancements.

In order to reduce the overhead of garbage collection, as well as yield an even erasure distribution of blocks in solid-state drives (SSDs), this paper proposes a mapping approach at flash translation layer (FTL) of the SSD devices. To be specific, it first analyzes certain I/O requests, for mining frequent item sets of logical sector numbers, to direct scheduling the write requests. After that, we map the requests belonging to the same frequent itemset, to the SSD blocks having a relative small erasure count. That is to say, these requests are supposed to be flushed onto the same SSD blocks when they have been ejected from the cache. As a result, the data on the same block are mostly like to be rewritten together in the near future, so that the overhead of garbage collection can be significantly cut down. Furthermore, it is able to yield a uniform erasure distribution among all blocks of SSDs even without wear-leveling optimization, to purposely extend the lifetime of the SSDs. Through a series of simulation experiments with a number of write-intensive disk traces of real-world applications, we conclude that this newly proposed mechanism does work, as expected.

NAND flash memory is being widely used as data storage in consumer electronics devices such as tablet computers and smartphones. However, due to the inherent nature of NAND flash memory where in-place update is not supported, NAND flash-based SSDs (Solid-State Drives) suffer from severe performance degradation as they need to move valid data during garbage collection (GC). Recently, multi-streamed SSDs have been proposed to reduce the cost of GC in the SSDs. However, commercial SSDs used in consumer electronics devices support only a small number of streams due to the device’s limitation in hardware resources. This makes it difficult to fully utilize the benefits of the multi-streamed SSDs. In this article, we propose a new concept of virtual streams (vStreams) that are independent of the number of available streams within the multi-streamed SSDs. We present the design and implementation of virtual stream management architecture, called vStream-FTL, for efficient stream management in the SSD. Specifically, we present novel mechanisms to monitor the lifetime of each stream with a negligible memory overhead and map one or more vStreams into a physical stream at run time according to the lifetime of vStream. In addition, we implement the enhanced garbage collection scheme, called vStream-aware GC that increases the benefits of multi-streamed SSDs further. Our vStream-FTL allows embedded system developers to manage a sufficient number of streams regardless of the physical streams supported by the device. The evaluation results with smartphone workload show that the proposed vStream-FTL improves throughput by 48% compared to the Legacy-FTL with no stream support. [ABSTRACT FROM AUTHOR]

More and more consumer electronics, such as personal computers and smart phones, adopt NAND flash memory as their data storage devices. However, garbage collection (GC) operations in flash memory may degrade both the performance and lifetime for consumer electronics due to the extra page reads and page writes caused by GC. Greedy algorithm, which selects the block with the least valid pages for GC, is usually used for victim block selection so as to reduce page movements. In this paper, to further improve GC performance, we propose a novel caching-aware GC scheme, and its basic idea is to take into account the pages cached in memory so as to further reduce page movements during GC. We also develop a consistent caching scheme to ensure data consistency and revise the victim block selection process with a lightweight caching-aware block selection scheme. We validate the performance of our caching-aware GC on FlashSim, and results show that it improves the performance of SSDs and prolongs their lifetime compared with conventional greedy algorithm. [ABSTRACT FROM AUTHOR]

Shingled Magnetic Recording (SMR) Disks are adopted as a high-density, non-volatile media that significantly precedes conventional disks in both the storage capacity and cost. However, inefficient read-modify-writes (RMWs) greatly challenge the management of SMR disks. This article for the first time presents an approach called Tiler to manage SMR disks by dividing the physical space into small autonomous regions (ARs). Each AR can manage its space allocation, address mapping, and cleaning independently. By managing these ARs in a log-structured way, RMWs can be avoided; besides, ARs can also help update data when the adjacent tracks contain no valid data. Tiler is capable of partitioning a large-scale cleaning into self-contained-small-scale cleaning and thus, the data that need to be relocated are limited inside independent ARs, which further minimizes the performance overhead. Our experimental results show that Tiler can shorten the overall system response time by 50.21 percent and reduce the cleaning time by 90.24 percent on average. [ABSTRACT FROM AUTHOR]

Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of nand flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data preclassifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of nand flash devices with improved GC-costs, compared with related works.

NAND flash memory has the advantages of strong shock resistance, low power consumption, non-volatility, and high performance, and it is gradually applied to embedded systems and enterprise servers, such as the IoT-based power grid storage system. Therefore, research on the flash translation layer strategy has become a popular trend for solid-state storage devices. The mapping granularity of different FTL policies is different in the methods of garbage collection. Furthermore, there is a problem of “write amplification”in the write operation of the flash memory device. This paper proposes a data aggregation preprocessing algorithm that can aggregate as many active pages as possible and reduce the proportion of write amplification. The algorithm is implemented and tested on the flash simulation platform FlashSim. The results indicate that our algorithm can effectively improve the performance of the IoT-based power grid storage system, reducing redundant write operations, the number of physical block erasures, the number of physical page read and write times, and the system response time, and extending the life of solid-state devices.

Recent advances in non-volatile memory (NVM) technology offer more capacity, scalability, and durability than regular volatile memory. However, adopting emerging memory introduces new challenges such as endurance issue and performance degradation. This paper proposes a software wear leveling technique that is designed specifically for a scenario, where the NVM is managed by generational garbage collectors that divide heap into separate young and old space. Prior work found that the majority of write traffic occurs to young generation heap, thus proposed a hybrid generational heap, where the NVM is utilized as old space and DRAM works as young space. We further investigate the access pattern in NVM-contained old generation. This paper first highlights a common observation across various workloads that the write distribution of old space is highly unbalanced. Specifically, among all writes to the NVM, 56%-96% occurs to 2% memory pages, and in worst cases, 83%-96% traffic occurs to only 0.5% pages. This leveling can be achieved by only swapping a very small fraction of NVM pages during the garbage collection cycle, which makes a low performance overhead runtime approach possible. We maintain a write intensity counter for each thread and insert a new garbage collection phase, where a few hottest pages are selected and remapped. Results show our approach significantly mitigates the skewness indicated by Gini coefficient dropping from 0.95 to 0.60, leading to extending lifetime 41 times on average. Meanwhile, it incurs a performance cost of 7.2% longer mutation time measured on emulated memory without performance penalty, and 3.31 % extra stop time for garbage collection cycles.

Spark is one of the most widely used systems for the distributed processing of big data. Its performance bottlenecks are mainly due to the network I/O, disk I/O, and garbage collection. Previous studies quantitatively analyzed the performance impact of these bottlenecks but did not focus on iterative algorithms. In an iterative algorithm, garbage collection has more performance impact than other workloads because the algorithm repeatedly loads and deletes data in the main memory through multiple iterations. Spark provides three caching mechanisms which are "disk cache," "memory cache," and "no cache" to keep the unchanged data across iterations. In this paper, we provide an in-depth experimental analysis of the effect of garbage collection on the overall performance depending on the caching mechanisms of Spark with various combinations of algorithms and datasets. The experimental results show that garbage collection accounts for 16–47% of the total elapsed time of running iterative algorithms on Spark and that the memory cache is no less advantageous in terms of garbage collection than the disk cache. We expect the results of this paper to serve as a guide for the tuning of garbage collection in the running of iterative algorithms on Spark. [ABSTRACT FROM AUTHOR]