While databases are widely-used in commercial user-facing services that have stringent quality-of-service (QoS) requirement, it is crucial to ensure their good performance and minimize the hardware usage at the same time. Our investigation shows that the optimal DBMS (database management system) software configuration varies for different user request patterns (i.e., workloads) and hardware configurations. It is challenging to identify the optimal software and hardware configurations for a database workload, because DBMSs have hundreds of tunable knobs, the effect of tuning a knob depends on other knobs, and the dependency relationship changes under different hardware configurations. In this paper, we propose SHA, a software and hardware auto-tuning system for DBMSs. SHA is comprised of a scaling-based performance predictor, a reinforcement learning (RL) based software tuner, and a QoS-aware resource reallocator. The performance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance requirement. The software tuner fine-tunes the DBMS software knobs to optimize the performance of the workload. The resource reallocator assigns the saved resources to other applications to improve resource utilization without incurring QoS violation of the database workload. Experimental results show that SHA improves the performance of database workloads by 9.9% on average compared with a state-of-the-art solution when the hardware configuration is fixed, and improves 43.2% of resource utilization while ensuring the QoS.

Decentralized cloud platforms have emerged as a promising paradigm to exploit the idle computing resources across the Internet to catch up with the ever-increasing cloud computing demands. As any user or enterprise can be the cloud provider in the decentralized cloud, the performance assessment of the heterogeneous computing resources is of vital significance. However, with the consideration of the untrustworthiness of the participants and the lack of unified performance assessment metric, the performance monitoring reliability and the incentive for cloud providers to offer real and stable performance together constitute the computational performance assessment problem in the decentralized cloud. In this paper, we present a robust performance assessment solution RODE to solve this problem. RODE mainly consists of a performance monitoring mechanism and an assessment of the claimed performance (AoCP) mechanism. The performance monitoring mechanism first generates reliable and verifiable performance monitoring results for the workloads executed by untrusted cloud providers. Based on the performance monitoring results, the AoCP mechanism forms a unified performance assessment metric to incentivize cloud providers to offer performance as claimed. Via extensive experiments, we show RODE can accurately monitor the performance of cloud providers on the premise of reliability, and incentivize cloud providers to honestly present the performance information and maintain the performance stability.

The combination of visual and textual information in image retrieval remarkably alleviates the semantic gap of traditional image retrieval methods, and thus it has attracted much attention recently. Image retrieval based on such a combination is usually called the content-and-text based image retrieval (CTBIR). Nevertheless, existing studies in CTBIR mainly make efforts on improving the retrieval quality. To the best of our knowledge, little attention has been focused on how to enhance the retrieval efficiency. Nowadays, image data is widespread and expanding rapidly in our daily life. Obviously, it is important and interesting to investigate the retrieval efficiency. To this end, this paper presents an efficient image retrieval method named CATIRI (content-and-text based image retrieval using indexing). CATIRI follows a three-phase solution framework that develops a new indexing structure called MHIM-tree. The MHIM-tree seamlessly integrates several elements including Manhattan Hashing, Inverted index, and M-tree. To use our MHIM-tree wisely in the query, we present a set of important metrics and reveal their inherent properties. Based on them, we develop a top-k query algorithm for CTBIR. Experimental results based on benchmark image datasets demonstrate that CATIRI outperforms the competitors by an order of magnitude.