In-memory cache systems such as Redis provide low-latency and high-performance data access for modern internet services. However, in large-scale Redis systems, the workloads show strong skewness and varied locality, which degrades system performance and incurs low CPU utilization. Though there are many approaches toward load imbalance, the two-layered architecture of Redis makes its workload skewness show special characteristics. Redis first maps data into data groups, which is calledGroup Mapping. Then the data groups are distributed to instances by Instance Mapping.Under Redis's layered architecture, it gives rise to a small number of hot-spot instances with very limited hot data groups, as well as a large number of remaining cold instances. To improve Redis's performance and CPU utilization, it entails the accurate identification of instance and data group hotness, and handling hot data groups and cold instances. We propose HPUCache+ to address the hot-spot problem via hotness identification, hot data copying, and cold instance merging. HPUCache+ accurately and dynamically detects instance and data group hotness based on multiple resources and workload characteristics at low cost. It enables access to multiple data copies by dynamically updating the cached mapping in Redis client, achieving high user access performance with Redis client compatibility, while providing highly self-definable service level agreement. It also proposes an asynchronous instance merging strategy based on disk snapshots and temporal caches, which separates the massive data movement from the critical user access path to achieve high-performance instance merging. We implement HPUCache+ into Redis. Experiments show that, compared to the native Redis design, HPUCache+ achieves up to 2.3$\times$and 3.5$\times$throughput gains, 11.3$\times$and 14.3$\times$CPU utilization gains, respectively. It also achieves up to 50% less CPU and 75% less memory consumption compared to the state-of-the-art approach Anna.

Erasure coding is a common redundancy scheme to provide higher reliability with much lower storage overhead compared to replication. It prevents data loss due to failures but induces high repair costs. As data volumes grow exponentially, wide stripes are proposed for extreme storage savings. Wide-stripe erasure codes face the challenges of higher repair costs for single and multiple failures. Our extensive analysis shows that existing repair-efficient erasure codes, such as locally repairable codes (LRCs) and minimum storage regenerating (MSR) codes, are insufficient to meet all the requirements of wide stripes: low storage overhead, low repair cost for both single and multiple failures, and high reliability. In this article, we explore an alternative code scheme, locally repairable with zigzag code (LRZC), which combines the advantages of LRCs and zigzag codes. LRZC divides data blocks and global parity blocks into evenly sized local groups, and generates two local parity blocks by a zigzag code in each group. Under the limit of storage overhead of wide stripes, LRZC reduces the repair cost for single and multiple failures and provides higher reliability compared with existing wide-stripe codes. Experiments show that LRZC reduces the repair cost of single and multiple failures by up to 41.9% and 41.7% compared with the state-of-the-art LRCs.

Maximum Distance Separable(MDS) codes can provide the optimal storage efficiency with the same fault tolerance. From the practical considerations, the systematic and optimal update properties of codes are crucial, where the former affects the workflow of read/write operations while the latter impacts the write amplification costs in update intensive scenarios. Moreover, the repair bandwidth, subpacketization level, and finite field size are three important performance metrics to evaluate the effectiveness of codes, which impact the network traffic, I/O performance and computational complexity, respectively. However, various code constructions with the optimal update property were devised to minimize repair bandwidth with high subpacketization levels or huge finite field sizes. While other constructions that reach a good trade-off among these three performance metrics always lack the optimal update property. In this paper, to address the above challenges of constructing practical MDS codes, we presentPermutation Transformation(PT) codesthat excel in the following respects: The systematic and optimal update properties can be both guaranteed; the code reaches nearly optimal repair bandwidth when repairing any single systematic node; the subpacketization level achieves a linear scale of the fault-tolerance capacity; the required size of the finite field to ensure the MDS property is small.

Erasure coding becomes increasingly popular in distributed storage systems (DSSes) for providing high reliability with low storage overhead. However, traditional random data placement induces massive cross-rack traffic and severely imbalanced load during failure recovery, which degrades the recovery performance significantly. In addition, various erasure codes coexisting in a DSS exacerbates the above problems. In this paper, we propose PDL, a PBD-based Data Layout, to optimize failure recovery performance in DSSes. PDL is constructed based on Pairwise Balanced Design, a combinatorial design scheme with uniform mathematical properties, and thus presents a uniform data layout for mixed erasure codes. Then we propose rPDL, a failure recovery scheme based on PDL. rPDL reduces cross-rack traffic effectively and provides nearly balanced cross-rack traffic distribution by uniformly choosing replacement nodes and retrieving determined available blocks to recover the lost blocks. We implemented PDL and rPDL in Hadoop 3.1.1. Compared with the existing data layout and recovery scheme in HDFS, experimental results show that rPDL achieves much higher recovery throughput, 6.27x for single-node failures, 5.14x for multi-node failures and 1.48x for single-rack failures, respectively. It also reduces degraded read latency by 62.83%, and provides evidently better support to front-end applications in case of component failures.

Erasure coding becomes increasingly popular in distributed storage systems (DSSes) for providing high reliability with low storage overhead. However, traditional random data placement causes massive cross-rack traffic and severely unbalanced load during failure recovery, degrading the recovery performance significantly. In addition, various erasure coding policies coexisting in a DSS exacerbates the above problem. In this paper, we propose PDL, a PBD-based Data Layout, to optimize failure recovery performance in DSSes. PDL is constructed based on Pairwise Balanced Design, a combinatorial design scheme with uniform mathematical properties, and thus presents a uniform data layout. Then we propose rPDL, a failure recovery scheme based on PDL. rPDL reduces cross-rack traffic effectively and provides nearly balanced cross-rack traffic distribution by uniformly choosing replacement nodes and retrieving determined available blocks to recover the lost blocks. We implemented PDL and rPDL in Hadoop 3.1.1. Compared with existing data layout of HDFS, experimental results show that rPDL reduces degraded read latency by an average of 62.83%, delivers 6.27× data recovery throughput, and provides evidently better support for front-end applications.

Due to individual unreliable commodity components, failures are common in large-scale distributed storage systems. Erasure codes are widely deployed in practical storage systems to provide fault tolerance with low storage overhead. However, random data distribution (RDD), commonly used in erasure-coded storage systems, induces heavy cross-rack traffic, load imbalance, and random access, which adversely affects failure recovery. In this article, with orthogonal arrays, we define a Deterministic Data Distribution (D3) to uniformly distribute data/parity blocks among nodes, and propose an efficient failure recovery approach based on D3, which minimizes the cross-rack repair traffic against a single node failure. Thanks to the uniformity of D3, the proposed recovery approach balances the repair traffic not only among nodes within a rack but also among racks. We implement D3over Reed-Solomon codes and Locally Repairable Codes in Hadoop Distributed File System (HDFS) with a cluster of 28 machines. Compared with RDD, our experiments show that D3 significantly speeds up the failure recovery up to 2.49 times for RS codes and 1.38 times for LRCs. Moreover, D3supports front-end applications better than RDD in both of normal and recovery states.

Due to individual unreliable commodity components, failures are common in large-scale distributed storage systems. Erasure codes are widely deployed in practical storage systems to provide fault tolerance with low storage overhead. However, the commonly used random data placement in storage systems based on erasure codes induces to heavy crossrack traffic, load imbalance, and random access, which slow down the recovery process upon failures. In this paper, with orthogonal arrays, we define a Deterministic Data Distribution (D3) of blocks to nodes and racks, and propose an efficient failure recovery approach based on D3. D3not only uniformly distributes data/parity blocks among storage servers, but also balances the repair traffic among racks and storage servers for failure recovery. Furthermore, D3also minimizes the cross-rack repair traffic for data layouts against a single rack failure and provides sequential access for failure recovery. We implement D3in Hadoop Distributed File System (HDFS) with a cluster of 28 machines. Our experiments show that D3significantly speeds up the failure recovery process compared with random data distribution, e.g., 2.21 times for (6, 3)-RS code in a system consisting of eight racks and three nodes in each rack.

Parity declustering is widely deployed in erasure coded storage systems so as to provide fast recovery and high data availability. However, to perform scaling on such RAIDs, it is necessary to preserve the parity declustered data layout so as to guarantee the RAID performance after scaling. Unfortunately, existing scaling algorithms fail to achieve this goal so they can not be applied for scaling RAIDs which have deployed parity declustering. To address this challenge, we develop an efficient scaling algorithm called PDS (Parity Declustering Scaling). In particular, we first employ an auxiliary Balanced Incomplete Block Design (BIBD) to define the data migrations during scaling so as to preserve parity declustered data layout, and then define the addressing algorithm in the scaled system based on the migrations. We provide theoretical proofs to show that PDS preserves the parity declustered data layout, which is the basis for scaling RAIDs with parity declustering, and also theoretically prove that PDS achieves the even distribution of data/parity blocks after scaling and requires only the minimal data migrations. To show the performance of PDS, we implement it in MD in Linux Kernel, and conduct experiments with real-world traces. Results show PDS can reduce 89.70% of data migration time and 24.44% of user response time during scaling on average, compared with the round-robin scheme.

Redundant array of independent disk (RAID) offers a good option to provide device-level fault tolerance for solid-state drives (SSDs). However, parity update with either read-modify-write or read-reconstruct-write may introduce a lot of extra I/Os and thus significantly degrades SSD RAID performance. To reduce the parity update cost, elastic striping chooses to reconstruct new stripes with only the newly updated data chunks instead of directly updating parity chunks. However, it necessitates an RAID-level garbage collection (GC) process, which may incur a very high cost due to the mixture of hot and cold data chunks. To address this problem, we follow the idea of elastic striping and propose a workload-aware scheme (WAS) to reduce the RAID-level GC cost so as to improve the performance and endurance of SSD RAID. In particular, we first develop a novel lightweight hot data identification scheme which requires only a very small computation time and memory cost, then propose a hotness-aware elastic striping approach to separately write data chunks with different hotness to different regions in SSD RAID. To evaluate the effectiveness and efficiency of our WAS, we implement a prototype system on RAID-5 and RAID-6 arrays composed of commercial SSDs. Experimental results show that compared to original elastic striping, our scheme reduces 30.0%-70.6% (and 23.9%-63.2%) of chunk writes under the RAID-5 (and RAID-6) settings, and also reduces the average response time by 60.9%-79.3% (and 56.8%-80.9%) for RAID-5 (and RAID-6), respectively. Besides, our scheme also improves the endurance and reliability of SSD RAID compared to original elastic striping.

RAID provides a good option to provide device-level fault tolerance. Conventional RAID usually updates parities with read-modify-write or read-reconstruct-write, which may introduce a lot of extra I/Os and thus significantly degrade SSD RAID performance. The recently proposed elastic striping scheme reconstructs new stripes with updated new data chunks without updating old parity chunks. However, it necessitates RAID-level garbage collection which may incur a very high cost. In this paper, we propose a hotness-aware caching scheme to buffer incoming writes and categorize data chunks in buffers into multiple groups according to their hotness values. We then propose a grouping-based elastic striping scheme to separately write data chunks in different groups into SSDs. We deployed the proposed schemes on a RAID-5 array composed of eight commercial SSDs, and experimental results show that compared to elastic striping, our scheme reduces 26% -- 65% of chunk writes to SSDs, and also reduces the average response time by 17.2% -- 63.9%.

Most sampling techniques for online social networks (OSNs) are based on a particular sampling method on a single graph, which is referred to as a statistic. However, various realizing methods on different graphs could possibly be used in the same OSN, and they may lead to different sampling efficiencies, i.e., asymptotic variances. To utilize multiple statistics for accurate measurements, we formulate a mixture sampling problem, through which we construct a mixture unbiased estimator which minimizes the asymptotic variance. Given fixed sampling budgets for different statistics, we derive the optimal weights to combine the individual estimators; given a fixed total budget, we show that a greedy allocation towards the most efficient statistic is optimal. In practice, the sampling efficiencies of statistics can be quite different for various targets and are unknown before sampling. To solve this problem, we design a two-stage framework which adaptively spends a partial budget to test different statistics and allocates the remaining budget to the inferred best statistic. We show that our two-stage framework is a generalization of 1) randomly choosing a statistic and 2) evenly allocating the total budget among all available statistics, and our adaptive algorithm achieves higher efficiency than these benchmark strategies in theory and experiment.