The flash-memory based Solid State Drive (SSD) presents a promising storage solution for increasi... more The flash-memory based Solid State Drive (SSD) presents a promising storage solution for increasingly critical data-intensive applications due to its low latency (high through-put), high bandwidth, and low power consumption. Within an SSD, its Flash Translation Layer (FTL) is responsible for exposing the SSD's flash memory storage to the computer system as a simple block device. The FTL design is one of the dominant factors determining an SSD's lifespan and performance. To reduce the garbage collection overhead and deliver better performance, we propose a new, low-cost, adaptive separation-aware flash translation layer (ASA-FTL) that combines sampling, data clustering and selective caching of recency information to accurately identify and separate hot/cold data while incurring minimal overhead. We use sampling for lightweight identification of separation criteria, and our dedicated selective caching mechanism is designed to save the limited RAM resource in contemporary SSDs. Using simulations of ASA-FTL with both real-world and synthetic workloads, we have shown that our proposed approach reduces the garbage collection overhead by up to 28% and the overall response time by 15% compared to one of the most advanced existing FTLs. We find that the data clustering using a small sample size provides significant performance benefit while only incurring a very small computation and memory cost. In addition, our evaluation shows that ASA-FTL is able to adapt to the changes in the access pattern of workloads, which is a major advantage comparing to existing fixed data separation methods.
Elastic distributed storage systems have been increasingly studied in recent years because power ... more Elastic distributed storage systems have been increasingly studied in recent years because power consumption has become a major problem in data centers. Much progress has been made in improving the agility of resizing small-and large-scale distributed storage systems. However, most of these studies focus on metadata based distributed storage systems. On the other hand, emerging consistent hashing based distributed storage systems are considered to allow better scalability and are highly attractive. We identify challenges in achieving elasticity in consistent hashing based distributed storage. These challenges cannot be easily solved by techniques used in current studies. In this paper, we propose an elastic consistent hashing based distributed storage to solve two problems. First, in order to allow a distributed storage to resize quickly, we modify the data placement algorithm using a primary server design and achieve an equal-work data layout. Second, we propose a selective data reintegration technique to reduce the performance impact when resizing a cluster. Our experimental and trace analysis results confirm that our proposed elastic consistent hashing works effectively and allows significantly better elasticity.
Solid State Drives (SSDs) using flash memory storage technology present a promising storage solut... more Solid State Drives (SSDs) using flash memory storage technology present a promising storage solution for data-intensive applications due to their low latency, high bandwidth, and low power consumption compared to traditional hard disk drives. SSDs achieve these desirable characteristics using internal parallelism—parallel access to multiple internal flash memory chips—and a Flash Translation Layer (FTL) that determines where data is stored on those chips so that they do not wear out prematurely. Unfortunately, current state-of-the-art cache-based FTLs like the Demand-based Flash Translation Layer (DFTL) do not allow IO schedulers to take full advantage of internal parallelism because they impose a tight coupling between the logical-to-physical address translation and the data access. In this work, we propose an innovative IO scheduling policy called Parallel-DFTL that works with the DFTL to break the coupled address translation operations from data accesses. Parallel-DFTL schedules address translation and data access operations separately , allowing the SSD to use its flash access channel resources concurrently and fully for both types of operations. We present a performance model of FTL schemes that predicts the benefit of Parallel-DFTL against DFTL. We implemented our approach in an SSD simulator using real SSD device parameters, and used trace-driven simulation to evaluate its efficacy. Parallel-DFTL improved overall performance by up to 32% for the real IO workloads we tested, and up to two orders of magnitude for our synthetic test workloads. It is also found that Parallel-DFTL is able to achieve reasonable performance with a very small cache size.
The flash-memory based Solid State Drive (SSD) presents a promising storage solution for increasi... more The flash-memory based Solid State Drive (SSD) presents a promising storage solution for increasingly critical data-intensive applications due to its low latency (high through-put), high bandwidth, and low power consumption. Within an SSD, its Flash Translation Layer (FTL) is responsible for exposing the SSD's flash memory storage to the computer system as a simple block device. The FTL design is one of the dominant factors determining an SSD's lifespan and performance. To reduce the garbage collection overhead and deliver better performance, we propose a new, low-cost, adaptive separation-aware flash translation layer (ASA-FTL) that combines sampling, data clustering and selective caching of recency information to accurately identify and separate hot/cold data while incurring minimal overhead. We use sampling for lightweight identification of separation criteria, and our dedicated selective caching mechanism is designed to save the limited RAM resource in contemporary SSDs. Using simulations of ASA-FTL with both real-world and synthetic workloads, we have shown that our proposed approach reduces the garbage collection overhead by up to 28% and the overall response time by 15% compared to one of the most advanced existing FTLs. We find that the data clustering using a small sample size provides significant performance benefit while only incurring a very small computation and memory cost. In addition, our evaluation shows that ASA-FTL is able to adapt to the changes in the access pattern of workloads, which is a major advantage comparing to existing fixed data separation methods.
Elastic distributed storage systems have been increasingly studied in recent years because power ... more Elastic distributed storage systems have been increasingly studied in recent years because power consumption has become a major problem in data centers. Much progress has been made in improving the agility of resizing small-and large-scale distributed storage systems. However, most of these studies focus on metadata based distributed storage systems. On the other hand, emerging consistent hashing based distributed storage systems are considered to allow better scalability and are highly attractive. We identify challenges in achieving elasticity in consistent hashing based distributed storage. These challenges cannot be easily solved by techniques used in current studies. In this paper, we propose an elastic consistent hashing based distributed storage to solve two problems. First, in order to allow a distributed storage to resize quickly, we modify the data placement algorithm using a primary server design and achieve an equal-work data layout. Second, we propose a selective data reintegration technique to reduce the performance impact when resizing a cluster. Our experimental and trace analysis results confirm that our proposed elastic consistent hashing works effectively and allows significantly better elasticity.
Solid State Drives (SSDs) using flash memory storage technology present a promising storage solut... more Solid State Drives (SSDs) using flash memory storage technology present a promising storage solution for data-intensive applications due to their low latency, high bandwidth, and low power consumption compared to traditional hard disk drives. SSDs achieve these desirable characteristics using internal parallelism—parallel access to multiple internal flash memory chips—and a Flash Translation Layer (FTL) that determines where data is stored on those chips so that they do not wear out prematurely. Unfortunately, current state-of-the-art cache-based FTLs like the Demand-based Flash Translation Layer (DFTL) do not allow IO schedulers to take full advantage of internal parallelism because they impose a tight coupling between the logical-to-physical address translation and the data access. In this work, we propose an innovative IO scheduling policy called Parallel-DFTL that works with the DFTL to break the coupled address translation operations from data accesses. Parallel-DFTL schedules address translation and data access operations separately , allowing the SSD to use its flash access channel resources concurrently and fully for both types of operations. We present a performance model of FTL schemes that predicts the benefit of Parallel-DFTL against DFTL. We implemented our approach in an SSD simulator using real SSD device parameters, and used trace-driven simulation to evaluate its efficacy. Parallel-DFTL improved overall performance by up to 32% for the real IO workloads we tested, and up to two orders of magnitude for our synthetic test workloads. It is also found that Parallel-DFTL is able to achieve reasonable performance with a very small cache size.
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Papers by Wei Xie