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We replace the original passive eviction mechanism with an automatic dump/load mechanism, in order to smooth the transition between access peaks and valleys.
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At the same time, we also explore the diurnal behavior of a KV store and exploit long-term reuse. We propose pRedis, Penalty and Locality Aware Memory Allocation in Redis, which synthesizes data locality and miss penalty, in a quantitative manner, to guide memory allocation and replacement in Redis. KV accesses also demonstrate locality, which needs to be coordinated with miss penalty to guide cache management. Inadequate consideration of penalty can substantially compromise space utilization and request service time. However, the diversity of miss penalty distinguishes a KV cache from a hardware cache. Inheriting from the traditional hardware cache design, many existing KV cache systems still use recency-based cache replacement algorithms, e.g., LRU or its approximations. The in-memory cache holds hot data, reduces request latency, and alleviates the load on background databases. The experimental results showed that the proposed framework can produce good quality solutions by utilising the main memory space and reducing the latency and read operations from the cloud that lead to reducing the monetary costs.ĭue to large data volume and low latency requirements of modern web services, the use of in-memory key-value (KV) cache often becomes an inevitable choice (e.g. The use of cache memory image reduces the number of reading operations from the cloud and saves the space of the main memory. In addition, a cache memory image in the application server is provided for the hot partition of the cloud database. Therefore, to overcome the mentioned problems, the cloud database is categorised into three partitions (hot, warm, cold). This mechanism retains the keys and all indexed fields of evicted records in the main memory which prevents potential memory space savings for the application that have many keys and secondary indexes. The main memory database and cache use internal tracking in the main memory to track records that are not accessed by transferring the data to the disk. To solve this problem, an optimal data access framework is presented to cache the statistical data of the patients in the application server.
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In addition, users encounter long latency when the required data need to be read from the cloud via the internet and the hard disk drive (HDD) of the cloud servers. Application providers faced a problem in reducing the monetary cost of the whole cloud service and reducing the footprint of the main memory space. In the telerehabilitation system, the statistical data of the patients’ movement are stored in the temporary storage and synchronised to the storage service of online cloud data. Our experimental study on real data shows that ATRAPOS accelerates exploratory data analysis and mining on HINs, outperforming off-the-shelf caching approaches and state-of-the-art research prototypes in all examined scenarios. ATRAPOS selects intermediate results to cache and reuse by detecting frequent sub-metapaths among workload queries in real time, using a tailor-made data structure, the Overlap Tree, and an associated caching policy. In this paper, we present ATRAPOS, a new approach for the real-time evaluation of metapath query workloads that leverages a combination of efficient sparse matrix multiplication and intermediate result caching. While the real-time evaluation of metapath query workloads on large, web-scale HINs is highly demanding in computational cost, current approaches do not exploit interrelationships among the queries. Exploring, analysing, and extracting knowledge from such networks relies on metapath queries that identify pairs of entities connected by relationships of diverse semantics. Heterogeneous information networks (HINs) represent different types of entities and relationships between them.