A self-learning pattern adaptive prefetching method for big data applications

Li, Xian Shu; Yoon, Su Kyung; Kim, Jung Geun; Kim, Shin Dug

doi:10.1016/j.suscom.2017.12.003

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…In 2017, Li et al proposed a layered caching architecture [29] to improve the performance of real time big data management. This work belongs to the boundary between hardware-level and block-level.…”

Section: Related Workmentioning

confidence: 99%

SEER-MCache: A Prefetchable Memory Object Caching System for IoT Real-Time Data Processing

Dong

Yuan

et al. 2018

IEEE Internet Things J.

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Memory object caching systems, such as Memcached and Redis, have been proved to be a simple and highefficient middleware for improving the performance of IoT (Internet of Things) devices querying the database in cloud. However, its performance guarantee is built on the fact that the target data, queried by the IoT device, will be accessed many times and hit in the caching system. Therefore, when database system is handling the unrepeated IoT queries, it usually presents the sub-optimal performance, which greatly impairs the efficiency of real-time data processing on IoT devices. To improve this issue, we propose Seer-MCache, the memory object caching system with a smart prefetching (read-ahead) function, to fill up the caching system with the desired data before the intensive IoT queries arriving. Seer-MCache includes a set of rules to launch the specific behaviors of read-head. These rules are able to be customized according to the workload characteristics and system load. We implement a prototype system in Redis (caching layer) and MySQL server (database system). Extensive experiments are conducted to verify the effectiveness of Seer-MCache, the results show that Seer-MCache can improve the performance of readintensive workload up to 61% (39.5% in average). Meanwhile, the cost of the read-ahead behavior is moderate and controllable.

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Section: Related Workmentioning

confidence: 99%

SEER-MCache: A Prefetchable Memory Object Caching System for IoT Real-Time Data Processing

Dong

Yuan

et al. 2018

IEEE Internet Things J.

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“…Caching strategy is a relatively broad field of research because this strategy is widely used in several areas such as cloud networks [19], [26], browser applications [27], [28], operating systems [29], embedded systems [30], mobile network [31], [32], and telecommunications [20]. The primary mechanism of caching strategy is cache replacement, changing the content of cached data in cache memory because of its limited capacity [33], [34].…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of cache replacement algorithms onvarious internet traffic

Zulfa

Fadli²,

Permanasari

et al. 2023

J.INFOTEL

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Internet users tend to skip and look for alternative websites if they have slow response times. For cloud network managers, implementing a caching strategy on the edge network can help lighten the workload of databases and application servers. The caching strategy is carried out by storing frequently accessed data objects in cache memory. Through this strategy, the speed of access to the same data becomes faster. Cache replacement is the main mechanism of the caching strategy. There are seven cache replacement algorithms with good performance that can be used, namely LRU, LFU, LFUDA, GDS, GDSF, SIZE, and FIFO. The algorithm is developed uniquely according to the internet traffic patterns encountered. Therefore, a particular cache replacement algorithm cannot be superior to other algorithms. This paper presents a performance comparison simulation of the seven cache replacement algorithms on various internet traffic extracted from the public IRcache dataset. The results of this study indicate that the hit ratio performance is strongly influenced by cache size, cacheable and unique requests. The smaller the unique request that occurs, the greater the hit ratio performance obtained. The LRU algorithm shows an excellent hit ratio performance to perform cache replacement work under normal internet conditions. However, when the access impulse phenomenon occurs, the GDSF algorithm is superior in obtaining hit ratios with limited cache memory capacity. The simulation results show that GDSF reaches a 50.75% hit ratio while LRU is only 49.17% when access anomalies occur.

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A Study on the Business Data Evaluation Method of the Power Grid Value-Added Service

Jun

Zhang

2021

2021 5th International Conference on Power and Energy Engineering (ICPEE)

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A self-learning pattern adaptive prefetching method for big data applications

Cited by 3 publications

References 21 publications

SEER-MCache: A Prefetchable Memory Object Caching System for IoT Real-Time Data Processing

SEER-MCache: A Prefetchable Memory Object Caching System for IoT Real-Time Data Processing

Performance comparison of cache replacement algorithms onvarious internet traffic

A Study on the Business Data Evaluation Method of the Power Grid Value-Added Service

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