In Attribute Based Encryption, a set of descriptive attributes is used as an identity to generate a secret key, as well as serving as the access structure that performs access control. It successfully integrates Encryption and Access Control and is ideal for sharing secrets among groups, especially in a Cloud environment. Most developed ABE schemes support key-policy or ciphertext-policy access control in addition to other features such as decentralized authority, efficient revocation and key delegation. This paper surveys mainstream papers, analyzes main features for desired ABE systems, and classifies them into different categories. With this high-level guidance, future researchers can treat these features as individual modules and select related ones to build their ABE systems on demand.

In recent years, NAND flash-based solid state drives (SSD) have been widely used in datacenters due to their better performance compared with the traditional hard disk drives. However, little is known about the reliability characteristics of SSDs in production systems. Existing works study the statistical distributions of SSD failures in the field. However, they do not go deep into SSD drives and investigate the unique error types and health dynamics that distinguish SSDs from hard disk drives. In this paper, we explore the SSD-specific SMART (Self-Monitoring, Analysis, and Reporting Technology) attributes to conduct an in-depth analysis of SSD reliability in a production environment. Data is collected from a scalable production system having several physical locations. Our dataset contains over a million records with more than twenty attributes. We leverage machine learning technologies, specifically data clustering and correlation analysis methods, to discover groups of SSDs which have different health status and relations among SSD-specific SMART attributes. Our results show that 1) Media wear affects the reliability of SSDs more than any other factors, and 2) SSDs transit from one health group to another which infers the reliability degradation of those drives. To the best of our knowledge, this is the first study that investigates SSD-specific SMART data to characterize SSD reliability in a production environment.

Log-structured merge-tree (LSM-tree) based key-value stores are widely deployed in largescale storage systems. The underlying reason is that the traditional relational databases cannot reach the high performance required by big-data applications. As high-throughput alternatives to relational databases, LSM-tree-based key-value stores can support high-throughput write operations and provide high sequential bandwidth in storage systems. However, the compaction process triggers write amplification and is confronted with degraded write performance, especially under update-intensive workloads. To address this issue, we design a holistic key-value store to explorer near-data processing and on-demand scheduling for compaction optimization in an LSM-tree key-value store, named DStore. DStore makes full use of various computing capacity in the host- and device-side subsystems. DStore dynamically divides the whole host-side compaction tasks into the above two-side subsystems according to two-side different computing capabilities. Meanwhile, the device must be featured with a near-data processing model. The divided compaction tasks are performed by the host and the device in parallel. In DStore, the NDP-based devices exhibit low-latency and high-bandwidth performance, thus facilitating key-value stores. DStore does not only accomplish compaction for key-value stores but also improve the system performance. We implement our DStore prototype in a real-world platform and different kinds of testbeds are employed in our experiment. LevelDB and a static compaction optimization using the NDP model (called Co-KV) are used to compare with the DStore in our evaluation. Results show that DStore achieves about 3.7x performance improvement over LevelDB under the db_bench workload. In addition, DStore-enabled keyvalue stores outperform LevelDB by a factor of about 3.3x and 77.0% in terms of throughput and latency under YCSB benchmark, respectively.