Blockchain, the cornerstone for building the next generation of trustworthy web, suffers from poor scalability. Accelerating block propagation among blockchain nodes through block compression is an effective approach to improving scalability. However, existing block compression techniques, due to either low compression ratios or high recommunication ratios, induce either high transmission latency (proportional to block size, for a given Internet bandwidth) or multi-round Internet delay (dominated by geographical separation), causing significant network latency. To address both issues simultaneously, we propose a novel block propagation protocol, Erasure-Coded Compact Block (ECCB). It is motivated by and designed to exploit the significant transactional redundancy empirically observed in propagating blocks from six Ethereum nodes deployed across three continents. This redundancy refers to two key observations we made. First, most transactions in a propagating block (called hitting transactions) already exist in nodes' local transaction pools. Second, almost all of the remaining non-hitting transactions in the block (called missing transactions) are overlapping between neighboring nodes. Thus, rather than propagating all transactions within a propagating block, ECCB only propagates the much smaller-sized parity data that is erasure-code generated from these transactions. When receiving an ECCB block, a node combines the parity data with the dominant hitting transactions already existing in the local transaction pool to reconstruct the missing transactions, thus significantly compressing the propagating block. Meanwhile, recommunication is avoided by transmitting slightly more parity data than the predicted total size of all missing transactions, leveraging the high degree of overlap in missing transactions between neighboring nodes. Results from both simulation and prototyping experiments demonstrate the high efficacy of ECCB.

The presence of a large number of low-availability nodes in a decentralized environment results in lower overall data availability. Existing systems improve data availability by replicating a data item into multiple replicas across different nodes. Due to disparity in node availability in decentralized environments, replica placement strategies significantly affect overall data availability. Compared to random strategies, greedy strategies effectively improve overall data availability under low system storage utilization. However, greedy strategies fail to achieve the global optimum in the stable storage utilization period. Our modeling analysis reveals that, under stable storage utilization, (1)the overall data availability has a theoretical upper bound; and (2)this bound is achieved if and only if the availability of each data item equals the standard value, which is determined by the geometric mean of node availability and the replication factor. Based on the above analysis, we propose AvailabilityBalanced Store (ABS) to achieve near-optimal overall data availability during the stable period. ABS estimates the standard value based on the preset stable period storage utilization, replication factor and node availability statistics. When a new data item is added, it looks for appropriate nodes to place replicas so that the data item's availability approaches the standard value. In particular, we develop a fast node selection algorithm with$O(N)$complexity. In addition, we employ the combination of static and dynamic replica techniques to reduce the data migration volume during system utilization adjustments. We conduct extensive simulations to validate the effectiveness of ABS.

Blockchain is a promising infrastructure for the internet and digital economy, but it has serious scalability problems, that is, long block synchronization time and high storage cost. Conventional coarse-grained data deduplication schemes (block or file level) are proved to be ineffective on improving the scalability of blockchains. Based on comprehensive analysis on typical blockchain workloads, we propose two new locality concepts (economic and argument locality) and a novel fine-grained data deduplication scheme (transaction level) named Alias-Chain. Specifically, Alias-Chain replaces frequently used data, for example, smart contract arguments, with much shorter aliases to reduce the block sizes, which results in both shorter synchronization time and lower storage cost. Furthermore, to solve the potential consistency issue in Alias-Chain, we propose two complementary techniques: one is generating aliases from history blocks with high consistency, and the other is speeding up the generation of aliases via a specific algorithm. Our simulation results show: (1) the average transfer and SC-call transaction (a transaction used to call the smart contracts in the blockchain) sizes can be significantly reduced by up to 11.03% and 79.44% in native Ethereum, and up to 39.29% and 81.84% in Ethereum optimized by state-of-the-art techniques; and (2) the two complementary techniques well address the inconsistency risk with very limited impact on the benefit of Alias-Chain. Prototyping-based experiments are further conducted on a testbed consisting of up to 3200 miners. The results demonstrate the effectiveness and efficiency of Alias-Chain on reducing block synchronization time and storage cost under typical real-world workloads.

Erasure codes are widely deployed in modern storage systems, leading to frequent usage of their encoding/decoding operations. The encoding/decoding process for erasure codes is generally carried out using the parity-check matrix approach. However, this approach is serial and computationally expensive, mainly due to dealing with matrix operations, which results in low encoding/decoding performance. These drawbacks are particularly evident for newer erasure codes, including SD and LRC codes. To address these limitations, this article introduces the Partitioned and Parallel Matrix ( PPM ) algorithm. This algorithm partitions the parity-check matrix, parallelizes encoding/decoding operations, and optimizes calculation sequence to facilitate fast encoding/decoding of these codes. Furthermore, we present a generalized PPM ( gPPM ) algorithm that surpasses PPM in performance by employing fine-grained dynamic matrix calculation sequence selection. Unlike PPM, gPPM is also applicable to erasure codes such as RS code. Experimental results demonstrate that PPM improves the encoding/decoding speed of SD and LRC codes by up to 210.81%. Besides, gPPM achieves up to 102.41% improvement over PPM and 32.25% improvement over RS regarding encoding/decoding speed.

A Blockchain is a promising infrastructure but it has serious scalability problems, i.e., long block synchronization time and high storage cost. Conventional coarse-grained data deduplication schemes (block or file level) are proved to be ineffective on this problem. Based on comprehensive analysis on typical blockchain workloads, we are the first to propose two new locality concepts: economic and argument locality. To further explore these new localities, we propose a novel fine-grained data deduplication scheme (transaction level) named Alias-Chain to improve the scalability of blockchains. Specifically, Alias-Chain replaces frequently used data, e.g., smart contract arguments, with much shorter aliases to reduce the block size. During prop-agation and preservation of blocks, smaller blocks result in both shorter synchronization time and lower storage cost. Simulation results show the average transfer and SC-call transaction sizes can be reduced by up to 11.23% and 43.23% in native Ethereum, and up to 61.95 % and 77.54 % in Ethereum optimized by state-of-the-art techniques, respectively. Prototyping-based experiments are further conducted on a testbed consisting of up to 3200 miners. The results demonstrate the effectiveness and efficiency of Alias-Chain on reducing block synchronization time and storage cost under typical real-world workloads.

Summary Solid state drives (SSDs) built upon MLC NAND flash memories suffer from a low lifetime endurance induced by continuously scaling‐down feature sizes and increasing bit density per cell. To cost‐effectively address this problem, we propose to integrate both hierarchical data redundancy and heterogeneous flash memory techniques into the SSD, named H2‐SSD. Through deploying across‐chips data redundancy in addition to conventional in‐page error correction codes, error correction capacity, so does the lifetime endurance of H2‐SSD can be dramatically improved. Furthermore, an extra small‐size sisngle‐level cell (SLC) chip is integrated into H2‐SSD to store across‐chips parities. Due to the high program/erase performance and lifetime endurance of that SLC chip, I/O performance degradation induced by the hierarchical data redundancy can be significantly mitigated, even under strict synchronous parity update strategies. Quantitative analysis and trace‐driven simulations are conducted to evaluate the effectiveness and efficiency of H2‐SSD, in both the scenes with and without degraded reads. Experimental results demonstrate that H2‐SSD outperform the conventional SSD in maximum Program/Erase cycles by 23% to 178%, and suffer from negligible degradation of I/O performance in terms of both throughput and average response time in most cases.

The RAID-10 architecture has been widely deployed in commercial and industrial storage environments over the past two decades due to its high reliability, availability, and performance. However, during the recovery process of a single disk failure, which accounts for more than 99.75% of the disk failure scenarios, it is still at a high risk of data loss and suffers from a degradation of user I/O performance, which results from the severe interference between the user and recovery I/Os. Based on our observations and analyses, we find highly asymmetric disk bandwidth utilization and disk I/O interference during the recovery process of the RAID-10 systems under the single faulty-disk condition. Motivated by the fact that this asymmetry can be leveraged to significantly and simultaneously speed up the recovery and user I/O performances. As a result, we propose a novel asymmetric buffer cache management scheme, called A-Cache, to mitigate this asymmetry by allocating cache space asymmetrically between the disks that do not participate in the recovery process and those that do. To verify the effectiveness of the A-cache, we have integrated A-Cache into the popular cache algorithms LRU, named A-LRU. The evaluation of our prototype system demonstrates that A-LRU is able to significantly speed up the recovery speed and average user I/O latency under various typical configurations, compared to the original LRU, without any additional hardware cost.

Multilevel cell (MLC) NAND flash memory uses the voltages of the memory cells to represent bits, but high voltages cause much more damage on the cells than low voltages. Free space in MLC can be leveraged to reduce the usage of the high voltages and thus extend the lifetime of MLC. However, limited by the conventional data representation rule that represents bits by the voltage of one single cell, the high voltages are used in a high probability. To fully explore the potential of the free space on reducing the usage of high voltages without changing the total density of MLC, we propose a novel data representation aware of damage, named DREAM. DREAM uses the low voltage combinations of multiple cells instead of the voltage of one single cell to represent bits. It enables to represent the same bits through flexibly replacing the high voltages in some cells with the low voltages in other cells when free space is available. Hence, high voltages which cause more damage are less used and the lifetime of the MLC memory is extended. In addition, complementary techniques are proposed to mitigate performance loss induced by DREAM. Theoretical analysis and simulation results demonstrate the effectiveness and efficiency of DREAM.

Summary An effective way to mitigate the lifetime and performance problems of NAND flash SSDs induced by garbage collection is separating the cold data and hot data. However, all existing solutions do this separation from the viewpoint of the hot data and thus suffer from expensive cost on monitoring the update behaviors of workloads. Different from the conventional wisdom, we propose to do the separation from the viewpoint of the cold data, which is rooted on the following two dedicated observations. First, the data migrated by garbage collection demonstrate a cold property compared with the other data. Second, it is inexpensive to separate the former data from the latter data. In the proposed method, named Isolation, we inexpensively separate the cold data via garbage collection. More specifically, through writing the data migrated by garbage collection (cold data) to dedicated blocks and writing the other data (hot data) to other blocks, the separation is done naturally. To evaluate Isolation, we conduct extensive trace‐driven simulations under seven typical workloads. The simulation results demonstrate the effectiveness and efficiency of Isolation. In most cases, Isolation can reduce the number of erasures and average I/O response time up to 47.3% and 80.1%, respectively. Existing approaches integrated with Isolation can further reduce the number of erasures and average I/O response time up to 7.8%‐18.5% and 10.7%‐41.4%, respectively.

Data reliability and availability, and serviceability (RAS) of erasure-coded data centers are highly affected by data repair induced by node failures. In a traditional failure identification scheme, all chunks share the same identification time threshold, thus losing opportunities to further improve the RAS. To solve this problem, we propose RAFI, a novel risk-aware failure identification scheme. In RAFI, chunk failures in stripes experiencing different numbers of failed chunks are identified using different time thresholds. For those chunks in a high-risk stripe, a shorter identification time is adopted, thus improving the overall data reliability and availability. For those chunks in a low-risk stripe, a longer identification time is adopted, thus reducing the repair network traffic. Therefore, RAS can be improved simultaneously. We also propose three optimization techniques to reduce the additional overhead that RAFI imposes on management nodes and to ensure that RAFI can work properly under large-scale clusters. We use simulation, emulation, and prototyping implementation to evaluate RAFI from multiple aspects. Simulation and prototype results prove the effectiveness and correctness of RAFI, and the performance improvement of the optimization techniques on RAFI is demonstrated by running the emulator.

In large-scale replicated distributed storage systems consisting of hundreds to thousands of nodes, node failures are not rare and can cause data blocks to lose their replicas and become faulty. A simple but effective approach to prevent data loss from the node failures, i.e., ensuring reliability, is to shorten the identification time of the node failures and faulty blocks, which is determined by both timeouts and check intervals for node states. However, to maintain low repair network traffic, the identification time is actually relatively long and even dominates repair processes of critical blocks. In this paper, we propose a novel scheme, named RICK, to explore the potential in the identification time, and thus improve data reliability of replicated distributed storage systems while maintaining a low repair cost. First, by introducing an additional replica state, critical blocks (with two or more lost replicas) have individual short timeouts while sick blocks (with only one lost replica) preserve the long timeouts. Second, by replacing the static check intervals for node states with adaptive ones, the check intervals and the identification time of critical blocks are further shortened, which improves data reliability. Meanwhile, due to the low ratio of critical blocks in all faulty blocks, the repair network traffic remains low. The results from our simulation and prototype implementation show that RICK improves data reliability of replicated distributed storage systems by a factor of up to 14 in terms of mean time to data loss. Meanwhile, the extra repair network traffic caused by RICK is less than 1.5 percent of the total network traffic for data repairs.

SSD suffer from lifetime problem, particularly when they are deployed in all flash RAID5 systems and work under workload dominated by random updates. To cost- effectively and reliably extend the lifetime of those SSD, we propose a novel reliable hierarchical buffer cache, named RH-cache. RHcache consists of an upper- level write cache built upon a smallsize NVRAM and a lower-level write cache built upon a sub-RAID divided from the underlying SSD RAID. The upper-level write cache merges random updates into sequential writes thus reducing updates on the parity chunks. And the lower- level write cache can reduce the updates on data chunks by exploring content locality in the write workload. A prototype of RH-cache has been built to evaluate its effectiveness and efficiency. Both I/O benchmarks and real-world workloads driven experiments are conducted. The experimental results demonstrate RH-cache can reduce the updates in SSD by up to 80% compared with that without RH-cache.

High reliability is critical to large data centers consisting of hundreds to thousands of storage nodes where node failures are not rare. Data replication is a typical technique deployed to achieve high reliability. When a node failure is detected, blocks with lost replicas are identified and recovered. Long timeouts are usually used for node failure detection. For blocks with one lost replica, the long timeouts can significantly reduce network traffic induced by data recovery. However, for blocks with two or more lost replicas, which can be caused by concurrent node failures that are not rare in large data centers, the long timeouts will result in a high risk of loss of these blocks. In this paper, we propose MFR to separate the identification of the blocks with two or more lost replicas from that of the blocks with one lost replica in a way that the identification of the blocks with two or more replicas can be accelerated while that of the blocks with one lost replica stays the same. Consequently, MFR can significantly improve data reliability while keeping the network traffic induced by data recovery stable. The results from our simulation and prototype implementation show that MFR improves the reliability of storage systems by a factor of up to 4.0 in terms of mean time to data loss. As blocks with two or more lost replicas are far fewer than blocks with one lost replica, the extra network traffic caused by MFR is less than 0.54% of total network traffic for data recovery.

Erasure codes are widely deployed in storage systems and the encoding/decoding process is a common operation in erasure-coded systems. Parity-check matrix method is a general method employed in erasure codes to conduct encoding/decoding process. However, the process is serial and generates high computational cost in dealing with matrix operations, and hence, causes low encoding/decoding performance. Especially for some recently proposed erasure codes, including SD code, PMDS code, and LRC code, the disadvantages are more obvious. To address this issue, in this paper, we present an optimization algorithm, called Partitioned and Parallel Matrix (PPM) algorithm, to accelerate the encoding/decoding processes of these codes by partitioning the parity-check matrix, parallelizing the encoding/decoding operations, and optimizing the calculation sequence, so as to achieve the goal of fast encoding/decoding. Experimental results show that PPM can speed up the encoding/decoding process of these codes by up to 210.81%.

As an alternative to conventional spinning HDDs, MLC NAND flash memory based SSDs suffer from a low life endurance. To cost-effectively address this problem, we propose integrating both Hierarchical data redundancy and Heterogeneous flash memory techniques into those SSDs, named as H2-SSD. By deploying across-chips data redundancy in addition to the conventional in-page ECCs, the error correction capacity of H2-SSD can be dramatically enhanced. As a result, the life endurance can be significantly improved. Furthermore, an extra small sized SLC chip is deployed in H2-SSD to store the across-chips parity. Due to the high Program/Erase performance and life endurance of that SLC chip, the degradation of I/O performance induced by the hierarchical data redundancy will be slight in most cases, even under a strict synchronous parity update strategy. Both quantitatively analysis and trace-driven simulation are conducted to evaluate the effectiveness of H2-SSD. The results demonstrate that H2-SSD outperforms the conventional SSDs in the maximum Program/Erase cycles by 23% to 178% and suffer from a less than 10% degradation of I/O performance under most cases.

As hard disk failure rates are rarely improved and the reconstruction time for TB-level disks typically amounts to days, multiple concurrent disk/storage node failures in datacenter storage systems become common and frequent. As a result, the erasure coding schemes used in datacenters must meet the critical requirements of high fault tolerance, high storage efficiency, and fast fault recovery. In this paper, we introduce a new XOR-based non-MDS erasure code family with an ability of tolerating up to 12-disk/node failures, called PSG-Codes. The basic idea behind PSG-Codes is to partition disks into groups, and exploit short parity chains to generate parity units. Then, the parity chain is further shortened by varying the number of parity elements for each strip. We conduct a simulation-based study to search configuration parameter space of PSG-Codes, and prove that PSG-Codes can tolerate up to 12 disk/node failures. Compared with a well-known XOR-based non-MDS code, WEAVER codes, PSG-Codes have higher storage efficiency and lower reconstruction cost. Moreover, the storage efficiency and performance of PSG-Codes are also competitive with another stat-of-the-art GF-based non-MDS codes, LRC codes.

In today's large data centers, hundreds to thousands of nodes are deployed as storage clusters to provide cloud and big data storage service, where failures are not rare. Therefore, efficient data redundancy technologies are needed to ensure data availability and reliability. Compared to traditional technology based on replication, erasure codes which tolerate multiple failures provide availability and reliability at a much lower cost. However, those erasure-coded, particularly XOR-coded storage clusters, suffer from performance problem caused by degraded reads under concurrent node failures. With the traditional centralized decoding method, a large amount of extra data has to be transmitted over the network to service degraded reads. In particular, the degraded reads in XOR-coded stripes with concurrent failures result in notably high network traffic. To address this problem, we propose a novel decoding approach called Local Decoding First or LDF for short. Via exploiting decoding computational locality of XOR-coded storage clusters, LDF significantly reduces the required network traffic and hence reduces the access latency of degraded reads, thus improving I/O throughput. A prototype of LDF with two typical XOR codes has been implemented in the popular distributed file system HDFS on a storage cluster composed of 40 nodes. The experimental results show that LDF dramatically reduces the network traffic under concurrent node failures and thus improves both the I/O throughput and access latency.

I/O performance has been critical for large-scale distributed systems. Many approaches, including hint-based multilevel cache, have been proposed to smooth the gap between different levels. These solutions demote or promote cache blocks based on the latest history information, which is insufficient for applications where frequent demote and promote operations occur. In this paper, we propose a novel multilevel buffer cache using K-step hints (Hint-K) to improve the I/O performance of distributed systems. The basic idea is to promote a block from the lower level cache to the higher level(s) or demote a block vice versa based on the block's previous K-step promote or demote operations, which are referred to as K-step hints. If we make an analogy between Hint-K and LRU-K, then LRU-K keeps track of the times of last K references for blocks within a single cache level, while our Hint-K keeps track of the information of the last K movements (either demote or promote) of blocks among different cache levels. We develop our Hint-K algorithms and design a mathematical model that can efficiently describe the activeness of any block in any cache level. Simulation results show that Hint-K achieves better performance compared to the existing popular multilevel cache schemes such as PROMOTE, DEMOTE, and MQ under different I/O workloads.

The buffer cache plays an essential role in smoothing the gap between the upper level computational components and the lower level storage devices. A good buffer cache management scheme should be beneficial to not only the computational components, but also the storage components by reducing disk I/Os. Existing cache replacement algorithms are well optimized for disks in normal mode, but inefficient under faulty scenarios, such as a parity-based disk array with faulty disk(s). To address this issue, we propose a novel penalty-aware buffer cache replacement strategy, named Victim Disk(s) First (VDF) cache, to improve the reliability and performance of a storage system consisting of a buffer cache and disk arrays. VDF cache gives higher priority to cache the blocks on the faulty disks when the disk array fails, thus reducing the I/Os addressed directly to the faulty disks. To verify the effectiveness of the VDF cache, we have integrated VDF into the popular cache algorithms least frequently used (LFU) and least recently used (LRU), named VDF-LFU and VDF-LRU, respectively. We have conducted intensive simulations as well as a prototype implementation for disk arrays to tolerate one disk failure (RAID-5) and two disk failures (RAID-6). The simulation results have shown that VDF-LFU can reduce disk I/Os to surviving disks by up to 42.3 percent in RAID-5 and 50.7 percent in RAID-6, and VDF-LRU can reduce those by up to 36.2 percent in RAID-5 and 48.9 percent in RAID-6. Our measurement results also show that VDF-LFU can speed up the online recovery by up to 46.3 percent in RAID-5 and 47.2 percent in RAID-6 under spare-rebuilding mode, or improve the maximum system service rate by up to 47.7 percent in RAID-5 under degraded mode without a reconstruction workload. Similarly, VDF-LRU can speed up the online recovery by up to 34.6 percent in RAID-5 and 38.2 percent in RAID-6, or improve the system service rate by up to 28.4 percent in RAID-5.

With higher reliability requirements in clusters and data centers, RAID-6 has gained popularity due to its capability to tolerate concurrent failures of any two disks, which has been shown to be of increasing importance in large scale storage systems. Among various implementations of erasure codes in RAID-6, a typical set of codes known as Maximum Distance Separable (MDS) codes aim to offer data protection against disk failures with optimal storage efficiency. However, because of the limitation of horizontal parity or diagonal/anti-diagonal parities used in MDS codes, storage systems based on RAID-6 suffers from unbalanced I/O and thus low performance and reliability. To address this issue, in this paper, we propose a new parity called Horizontal-Diagonal Parity (HDP), which takes advantages of both horizontal and diagonal/anti-diagonal parities. The corresponding MDS code, called HDP code, distributes parity elements uniformly in each disk to balance the I/O workloads. HDP also achieves high reliability via speeding up the recovery under single or double disk failure. Our analysis shows that HDP provides better balanced I/O and higher reliability compared to other popular MDS codes.

RAID-6 is widely used to tolerate concurrent failures of any two disks to provide a higher level of reliability with the support of erasure codes. Among many implementations, one class of codes called Maximum Distance Separable (MDS) codes aims to offer data protection against disk failures with optimal storage efficiency. Typical MDS codes contain horizontal and vertical codes. Due to the horizontal parity, in the case of partial stripe write (refers to I/O operations that write new data or update data to a subset of disks in an array) in a row, horizontal codes may get less I/O operations in most cases, but suffer from unbalanced I/O distribution. They also have limitation on high single write complexity. Vertical codes improve single write complexity compared to horizontal codes, while they still suffer from poor performance in partial stripe writes. In this paper, we propose a new XOR-based MDS array code, named Hybrid Code (H-Code), which optimizes partial stripe writes for RAID-6 by taking advantages of both horizontal and vertical codes. H-Code is a solution for an array of (p+1) disks, where p is a prime number. Unlike other codes taking a dedicated anti-diagonal parity strip, H-Code uses a special anti-diagonal parity layout and distributes the anti-diagonal parity elements among disks in the array, which achieves a more balanced I/O distribution. On the other hand, the horizontal parity of H-Code ensures a partial stripe write to continuous data elements in a row share the same row parity chain, which can achieve optimal partial stripe write performance. Not only within a row but also within a stripe, H-Code offers optimal partial stripe write complexity to two continuous data elements and optimal partial stripe write performance among all MDS codes to the best of our knowledge. Specifically, compared to RDP and EVENODD codes, H-Code reduces I/O cost by up to 15.54% and 22.17%. Overall, H-code has optimal storage efficiency, optimal encoding/decoding computational complexity, optimal complexity of both single write and partial stripe write.

As the technique developing, some important problems in storage systems have been solved appropriately. A good example is the development of RAID-6 code techinque, the appear of it has greatly improved the reliability, availability of modern storage systems. Some best known vertical RAID-6 code like P-code and X-code has acquire optimal or near optimal performance in encoding, decoding and update. But they do not detailedly analysis the status of reconstruction with single-disk failure. In the status, there are many paths to perform reconstructing. But the path you choice will greatly affect the performance of whole storage system. Based the phenomenon found above, we present a fast and effcient scheme, Path Directed Recovery Scheme (PDRS for short), to find a optimal path to reconstruct single-disk failure in P-code and X-code. Using PDRS, we will acquire some benefits: (1) it can decrease the disk I/O complexity caused by reconstruction and therefore accelerating the speed of reconstruction, (2) it can balance the load on each disk, consequently can avoid the hot problem in a degree. We perform theoretical analysis and evaluation of the PDRS when applied in P-code with (p-1)-disk and X-code with p-disk. Our theoretical analysis shows that PDRS applied in P-code with (p-1)-disk can acquire up to 25% performance improvement. To verify the effectiveness of PDRS, we have conducted intenvice simulation. The simulation results shows that PDRS applied in P-code with (p-1)-disk can speedup the recovery duration by up to 23.6% under spare-rebuilding mode. Overall, PDRS is a efficient and useful recovery scheme that can applied to all of the vertical RAID-6 code.

The improvement for energy efficiency has been increasingly becoming a major consideration in server and data center design, especially for the power-hungry ones. Numerous studies have provided various new methods or proposals for the building of "green" server and data center, but this paper concentrates on how different configuration schemes in a server effect practical performance and power consumption for specific applications. It is completely necessary to obtain thin provisioning for the particular applications to meet performance requirements with minimal energy consumption. This paper evaluates the different hardware configurations' impact on energy consumption and performance for typical applications, hoping for offering evidences or clues to subsequent researches. The File Bench is used to generate four sever workloads and ZH-101 is employed to collect relevant real-time power consumptions. Our result shows that different workloads need different hardware configurations at the demands of both energy-efficiency and performance. And running multiple workloads on a reduced hardware configuration is a wise choice.

In this paper, we present a novel coding scheme that can tolerate up to two-disk failures, satisfying the RAID-6 property. Our coding scheme, Code-M, is a non-MDS (Maximum Distance Separable, tolerating maximum failures with a given amount of redundancy) code that is optimized by trading rate for fast recovery times. Code-M is lowest density and its parity chain length is fixed at 2C − 1 for a given number of columns in a strip-set C. The rate of Code-M, or percentage of disk space occupied by non-parity data, is (C − 1)/C. We perform theoretical analysis and evaluation of the coding scheme under different configurations. Our theoretical analysis shows that Code-M has favorable reconstruction times compared to RDP, another well-established RAID-6 code. The quantitative comparisons of Code-M against RDP demonstrate recovery performance improvement by a factor of up to 5.18 under single disk failure and 2.8 under double failures using the same number of disks. Overall, Code-M is a RAID-6 type code supporting fast recovery with reduced I/O complexity.

Redundant Arrays of Independent Disks RAID is a popular storage architecture with high performance and reliability. RAID-6 with a higher level of reliability based on MDS (Maximum Distance Separable) code is well studied, for its optimal storage efficiency. RAID-6 could offer continuous services in degraded mode, during the period of online failure recovery. However, the online recovery would bring a considerable I/O workflow to the storage system, that almost all the surviving data in the system need to be accessed. Due to the limitation of disk bandwidth, user response time would be significantly affected by the recovery workflow. In this paper, we examine the online recovery performance of two typical MDS RAID-6 codes RDP code and P-code. To our observation, P-code significiantly outperforms RDP in user response time and recovery duration during a single disk failure recovery. To our analysis, the difference comes from not only the parity layout but also the parity organization. Therefore, we propose a new categorization for existing MDS RAID-6 codes, based on the methodology of parity organization. By our approach, all the MDS RAID-6 codes could be categorized to Sym-codes with only one type of parity, and Asym-codes with at least two different types of parity.

Storage performance is critical in today's distributed stream-processing systems. One approach to improve the performance is to use hotspot attribute in object-based storage systems. This paper discusses hotspot classification and identification, and then presents an object hotspot prediction model (OHPM) to dynamically predict hotspots. Based on this model, we discuss an efficient hotspot caching strategy to improve the performance. To demonstrate the effectiveness of our proposed approach, we have developed a prototype of hotspot attribute-managed storage system (HASS) by extending object-based storage device (OSD) file system and iSCSI protocols. Experimental results show that the HASS improves the throughput by up to 62% and reduces the disk I/O by as much as 25% in our VoD tests by integrating our object hotspot prediction and cache approaches.

Cache plays an essential role in modern computer systems to smooth the performance gap between memory and CPU. Most existing cache replacement algorithms use three stacks: recency stack, frequency stack and history stack. The balance and design of those stacks is a key to achieve high hit ratio, thus improving the buffer cache efficiency. In this paper we propose a new cache replacement algorithm, adaptive dual LRU, or AD-LRU for short, to efficiently utilize the buffer cache pages. Instead of using one LRU stack, we use two LRU stacks: one LRU stack LR to catch the accesses of pages with low recency, and the other LRU stack HR to catch the accesses of pages with high recency. The idea is to adaptively adjust the sizes of the history stack, recency and frequency stacks, an overall buffer cache efficiency in terms of hit ratio will be improved. Simulations results show that AD-LRU demonstrates higher hit ratio compared to existing popular algorithms such as LRU, ARC, and LIRS.