Elastic Block Storage (EBS) has a pivotal role in modern data center infrastructure, providing reliable, high-performance and flexible block storage service to users. In Alibaba Cloud, EBS is the most widely used service and has been supporting the operation of millions of virtual disks. However, even with layers of load balancing and caching, we still observe significant traffic skewness across the EBS stack. This motivates us to comprehensively investigate symptoms and root causes behind the traffic patterns and, more importantly, explore the fixes for the identified issues.

In this paper, we qualitatively and quantitatively discuss the design choices, production experience, and lessons in building the Elastic Block Storage ( EBS ) at Alibaba Cloud over the past decade. To cope with hardware advancement and users’ demands, we shift our focus from design simplicity in EBS1 to high performance and space efficiency in EBS2 , and finally reducing network traffic amplification in EBS3 . In addition to the architectural evolutions, we also summarize development lessons and experiences as four topics, including: (i) achieving high elasticity in latency, throughput, IOPS, and capacity; (ii) improving availability by minimizing the blast radius of individual, regional, and global failure events; (iii) identifying the motivations and key tradeoffs in various hardware offloading solutions; and (iv) identifying the pros/cons of alternative solutions and explaining why seemingly promising ideas would not work in practice.

Cloud block storage systems support diverse types of applications in modern cloud services. Characterizing their input/output (I/O) activities is critical for guiding better system designs and optimizations. In this article, we present an in-depth comparative analysis of production cloud block storage workloads through the block-level I/O traces of billions of I/O requests collected from two production systems, Alibaba Cloud and Tencent Cloud Block Storage. We study their characteristics of load intensities, spatial patterns, and temporal patterns. We also compare the cloud block storage workloads with the notable public block-level I/O workloads from the enterprise data centers at Microsoft Research Cambridge, and we identify the commonalities and differences of the three sources of traces. To this end, we provide 6 findings through the high-level analysis and 16 findings through the detailed analysis on load intensity, spatial patterns, and temporal patterns. We discuss the implications of our findings on load balancing, cache efficiency, and storage cluster management in cloud block storage systems.

Many network data encountered are two-mode networks. These networks are characterized by having two sets of nodes and links are only made between nodes belonging to different sets. While their two-mode feature triggers interesting interactions, it also increases the risk of privacy exposure, and it is essential to protect sensitive information from being disclosed when releasing these data. In this paper, we introduce a weak notion of edge differential privacy and propose to release the degree sequence of a two-mode network by adding non-negative Laplacian noises that satisfies this privacy definition. Under mild conditions for an exponential-family model for bipartite graphs in which each node is individually parameterized, we establish the consistency and Asymptotic normality of two differential privacy estimators, the first based on moment equations and the second after denoising the noisy sequence. For the latter, we develop an efficient algorithm which produces a readily useful synthetic bipartite graph. Numerical simulations and a real data application are carried out to verify our theoretical results and demonstrate the usefulness of our proposal.

Stragglers (i.e., nodes with slow performance) are prevalent and incur performance instability in large-scale storage systems, yet it is challenging to detect stragglers in practice. We make a case by showing how erasure-coded caching provides robust straggler tolerance without relying on timely and accurate straggler detection, while incurring limited redundancy overhead in caching. We first analytically motivate that caching only parity blocks can achieve effective straggler tolerance. To this end, we present POCache, a parity-only caching design that provides robust straggler tolerance. To limit the erasure coding overhead, POCache slices blocks into smaller subblocks and parallelizes the coding operations at the subblock level. Also, it leverages a straggler-aware cache algorithm that takes into account both file access popularity and straggler estimation to decide which parity blocks should be cached. We implement a POCache prototype atop Hadoop 3.1 HDFS, while preserving the performance and functionalities of normal HDFS operations. Our extensive experiments on both local and Amazon EC2 clusters show that in the presence of stragglers, POCache can reduce the read latency by up to 87.9% compared to vanilla HDFS.

Frequency diverse array- (FDA-) based directional modulation (DM) is a promising technique for physical layer security, due to its angle-range dependent transmit beampattern. However, the existing schemes are not suitable for the broadcasting scenario, where there are multiple legitimate users (LUs) to receive the confidential message. In this paper, we propose a novel random frequency diverse array- (RFDA-) based DM scheme to realize the point to multi-point broadcasting secure transmission in both angle and range dimension. In the first stage, the beamforming vector is designed to maximize the artificial noise (AN) power, while satisfying the power requirement of LUs for transmitting the confidential message simultaneously. In the second stage, the AN projection matrix is obtained by maximizing signal-to-interference-plus-noise ratio (SINR) at the LUs. The proposed scheme only broadcasts the confidential message to the locations of LUs while the other regions are covered by AN, which promotes the security of the wireless broadcasting system. Moreover, it is energy efficient since the power of each LU is under accurate control. Numerical simulations are presented to validate the performance of the proposed scheme.

Recently, multi-source remote sensing data and their derived features such as vegetation indices, texture metrics have been frequently applied to quantitatively estimate forest above-ground biomass (AGB). However, it is still challenging to efficiently select the optimal features for modeling the forest AGB. In this study, a fast, efficient and automatic method has been proposed, called as k-nearest neighbor with fast iterative features selection (KNN-FIFS). This method iteratively pre-select the optimal features which determined by the minimum root mean square error (RMSE) between the forest field data and the k-nearest neighbor (k-NN) estimates based on the leave-one-out (LOO) cross-validation. By use of KNN-FIFS and multisource data, including Landsat-8 OLI (operational land imager) and its vegetation indices, texture metrics, HV polarization of P-band Synthetic Aperture Radar (SAR) data (PHV), and forest inventory data, were applied to estimate forest AGB over Genhe forest reserve located in Inner Mongolia, China. Afterwards, the model behaviors between KNN-FIFS and stepwise multiple linear regression (SMLR) methods were compared, which showed that the KNN-FIFS method (R2= 0.77 and RMSE = 22.74 t·ha-1) was superior to the SMLR method (R2= 0.53 and RMSE = 32.37 t·ha-1).

Interest in multimodal function optimization is expanding rapidly since real-world optimization problems often require location of multiple optima in searching space. The concept of restricted evolution is introduced to provide multiple optima. Combined with a simple evolution strategy, restricted evolution method can provide multiple solutions with low time consumption. Consequent local search based on the found solutions improves accuracy of optimization. The proposed method is applied to practical design of varied-line-spacing holographic gratings. Optimization results show the efficiency and usefulness of this method.