This paper introduces e-spill, an eager spill mechanism, which dynamically finds the optimal spill-threshold by monitoring the GC time at runtime and thereby prevent expensive GC overhead. Our e-spill adopts a slow-start model to gradually increase the spill-threshold until it reaches the optimal point without substantial GCs. We prototype e-spill as an extension to Spark and evaluate it using six workloads on three different parallel platforms. Our evaluations show that e-spill improves performance by up to 3.80× and saves the cost of cluster operation on Amazon EC2 cloud by up to 51% over the baseline system following Spark Tuning Guidelines.

Many mobile systems need to achieve both high performance and low memory usage, and the total performance of such the systems can be largely affected by the effectiveness of GC. Hence, the recent popularization of mobile devices makes the GC performance play one of the important roles on the wide range of platforms. The response performance degradation caused by suspending all processes for GC has been a well-known potential problem. Therefore, GC algorithms have been actively studied and improved, but they still have not reached any fundamental solution. In this paper, we focus on the point that the same objects are redundantly marked during the GC procedure implemented on DalvikVM, which is one of the famous runtime environments for the mobile devices. Then we propose a hardware support technique for improving marking routine of GC. We installed a set of tables to a processor for managing marked objects, and redundant marking for marked objects can be omitted by referring these tables. The result of the simulation experiment shows that the percentage of redundant marking is reduced by more than 50%.

In this letter, we propose a garbage collection technique for non-volatile memory systems, called Migration Cost Sensitive Garbage Collection (MCSGC). Considering the migration overhead from selecting victim blocks, MCSGC increases the lifetime of memory systems and improves response time in garbage collection. Additionally, the proposed algorithm also improves the efficiency of garbage collection by separating cold data from hot data in valid pages. In the experimental evaluation, we show that MCSGC yields up to a 82% improvement in lifetime prolongation, compared with existing garbage collection, and it also reduces erase and migration operations by up to 30% and 29%, respectively.

In this letter, we propose a novel garbage collection technique for index structures based on flash memory systems, called Proxy Block-based Garbage Collection (PBGC). Many index structures have been proposed for flash memory systems. They exploit buffers and logs to resolve the update propagation problem, one of the a main cause of performance degradation of the index structures. However, these studies overlooked the fact that not only the record operation but also garbage collection induces the update propagation problem. The proposal, PBGC, exploits a proxy block and a block mapping table to solve the update propagation problem, which is caused by the changes in the page and block caused by garbage collection. Experiments show that PBGC decreased the execution time of garbage collection by up to 39%, compared with previous garbage collection techniques.

NAND-based block devices such as memory cards and solid-state drives embed a flash translation layer (FTL) to emulate the standard block device interface and its features. The overall performance of these devices is determined mainly by the efficiency of the FTL scheme, so intensive research has been performed to improve the average performance of the FTL scheme. However, its worst-case performance has rarely been considered. The present study aims to improve the worst-case performance without affecting the average performance. The central concept is to distribute the garbage collection cost, which is the main source of performance fluctuations, over multiple requests. The proposed scheme comprises three modules: i) anticipated partial log block merging to distribute the garbage collection time; ii) reclaiming clean pages by moving valid pages to bound the worst-case garbage collection time, instead of performing repeated block merges; and iii) victim selection based on the valid page count in a victim log and the required clean page count to avoid subsequent garbage collections. A trace-driven simulation showed that the worst-case performance was improved up to 1,300% using the proposed garbage collection scheme. The average performance was also similar to that of the original scheme. This improvement was achieved without additional memory overheads.

The Log-structured File System (LFS) transforms random writes to a huge sequential one to provide superior write performance on storage devices. However, LFS inherently suffers from overhead incurred by cleaning segments. Specifically, when file system utilization is high and the system is busy, write performance of LFS degenerates significantly due to high cleaning cost. Also, in the newer flash memory based SSD storage devices, cleaning leads to reduced SSD lifetime as it incurs more writes. In this paper, we propose an enhancement to the original LFS to alleviate the performance degeneration due to cleaning when the system is busy. The new scheme, which we call Slack Space Recycling (SSR), allows LFS to delay on-demand cleaning during busy hours such that cleaning may be done when the load is much lighter. Specifically, it writes modified data directly to invalid areas (slack space) of used segments instead of cleaning on-demand, pushing back cleaning for later. SSR also has the added benefit of increasing the lifetime of the now popular SSD storage devices. We implement the new SSR-LFS file system in Linux and perform a large set of experiments. The results of these experiments show that the SSR scheme significantly improves performance of LFS for a wide range of storage utilization settings and that the lifetime of SSDs is extended considerably.

In this paper, we propose three techniques to improve the performance of YAFFS (Yet Another Flash File System), while enhancing the reliability of the system. Specifically, we first propose to manage metadata and user data separately on segregated blocks. This modification not only leads to the reduction of the mount time but also reduces the garbage collection time. Second, we tailor the wear-leveling to the segregated metadata and user data blocks. That is, worn out blocks between the segregated blocks are swapped, which leads to more evenly worn out blocks increasing the lifetime of the system. Finally, we devise an analytic model to predict the expected garbage collection time. By accurately predicting the garbage collection time, the system can perform garbage collection at more opportune times when the user's perceived performance may not be negatively affected. Performance evaluation results based on real implementations show that our modifications enhance performance and reliability without incurring additional overheads. Specifically, the YAFFS with our proposed techniques outperforms the original YAFFS by six times in terms of mount speed and five times in terms of benchmark performance, while reducing the average erase count of blocks by 14%.

It is an important problem to determine major collection times to meet the pause time goal for a generational garbage collector. From such a viewpoint, this paper proposes two stochastic models based on working schemes of a generational garbage collector: Garbage collections occur in a nonhomogeneous Poisson process, tenuring collection is made at a threshold level K, and major collection is made at time T or at Nth collection including minor and tenuring collections for the first model and at time T or at Nth collection including tenuring collections for the second model. Using the techniques of cumulative processes and reliability theory, expected cost rates are obtained, and optimal policies of major collection times which minimize them are discussed analytically and computed numerically.

Applications usually have their own phases in heap memory usage. The traditional garbage collector fails to match various application phases because the same heuristic on the object behavior is used throughout the entire execution. This paper introduces a phase-adaptive garbage collector which reorganizes the heap layout and adjusts the invocation time of the garbage collection according to the phases. The proposed collector identifies phases by detecting the application methods strongly related to the phase boundaries. The experimental results show that the proposed phase-adaptive collector successfully recognizes application phases and improves the garbage collection time by as much as 41%.

We have launched "Highly-Reliable Embedded Software Development" Project, held as a part of e-Society Project, supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. The aim of this project is to enable the industry to produce highly reliable and advanced software by introducing latest software technologies into embedded software development. In this paper, we introduce the overview of the projects and our activities and results so far.