The growing volume of performance-critical parameters in distributed storage systems, coupled with diverse and dynamic workload patterns, has significantly increased the complexity of system configuration. These trends have expanded the parameter space while tightening the time window for tuning convergence, making it challenging to maintain high system performance. Existing tuning strategies often struggle to balance thorough parameter exploration with real-time responsiveness, limiting their effectiveness under fast-evolving workloads and heterogeneous deployment environments. To address these challenges, we propose KGQW, the first framework that formulates automated parameter tuning as a knowledge graph query workflow. KGQW models workload features and system parameters as graph vertices, with performance metrics represented as edges, and constructs an initial knowledge graph through lightweight performance tests. Guided by performance prediction and Bayesian-driven exploration, KGQW progressively expands the graph, prunes insensitive parameters, and refines performance relationships to build an informative and reusable knowledge graph that supports rapid configuration retrieval via graph querying. Moreover, KGQW enables efficient knowledge transfer across clusters, substantially reducing the construction cost for new clusters. Experiments on real-world applications and storage clusters demonstrate that KGQW achieves second-level tuning latency, while maintaining or surpassing the performance of state-of-the-art methods. These results highlight the promise of knowledge-driven tuning in meeting the scalability and adaptability demands of modern distributed storage systems.

GEA-1 and its successor GEA-2 are two stream ciphers, designed to protect against eavesdropping General Packet Radio Service (GPRS) between the phone and the base station. They were widely used for GPRS encryption in the late 1990s and during the 2000s, and are surprisingly still supported in a range of current mobile phones. In this paper, new key recovery attacks on GEA-1 and GEA-2 are proposed by combining with the time-memory trade-off technique. The new attacks significantly reduce the time and memory costs of the previous attacks on GEA-1, and show that GEA-1 only offers 32-bit (out of 64) security. Furthermore, the slide properties of GEA-1 and GEA-2 are first found and used to explore practical related key attacks. The results show that GEA-1 and GEA-2 can be broken on a common PC within about 0.81 and 6.2 seconds in the multiple related key setting, respectively. Finally, an improved variant of GEA-2 called GEA-2a is proposed. The reasons for making the changes from GEA-2 are explained in detail, which indicates that GEA-2a has significantly better resistance than GEA-2 against all known attacks and can offer 64-bit security.