Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

Ali, Waleed

doi:10.14569/ijacsa.2018.090810

Cited by 5 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers develop GDSF algorithms into WGDSF [48] or WSCRP [34] with quite convincing HR performance results. LRU algorithm has also been developed using metaheuristic optimization methods [49] or machine learning [50]. Both show better HR performance compared to the standard LRU version.…”

Section: Discussionmentioning

confidence: 99%

Performance comparison of cache replacement algorithms onvarious internet traffic

Zulfa

Fadli²,

Permanasari

et al. 2023

J.INFOTEL

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Performance comparison of cache replacement algorithms onvarious internet traffic

Zulfa

Fadli²,

Permanasari

et al. 2023

J.INFOTEL

View full text Add to dashboard Cite

show abstract

“…LRU is a conventional caching algorithm commonly used by proxy servers in the cache replacement mechanism. LRU has advantages in its ease of implementation and fast performance [35]. LRU is also able to protect the cache memory utility from cache pollution.…”

Section: Discussion Of Cache Pollutionmentioning

confidence: 99%

“…The ML method in the caching strategy generally requires input in the form of a proxy log to obtain information about the cached data pattern based on recency, access time, access count, data size, or corresponding time. These variables were then analyzed using several ML algorithms to perform grouping based on specific classes or clusters, namely, fuzzy c-means (FCM) with Euclidean distance [26]; FCM with Waterman distance [30]; SVM [29]; decision tree [31]; k-means on cloudlet caching [32]; k-means with fuzzy bi-clustering [33]; naïve Bayes (NB) cooperating with GDS, LRU, and Dynamic Aging (DA) [34]; NB-LRU [35]; J48 [36]; and KNN [37]. Some of these ML algorithms have also been combined with conventional caching algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…In our prior work, noncyclic ACO-GA (GENACO) [15] outperformed cyclic ACO-GA (CGACA) [11] in terms of an optimal objective function value. In this paper, the proposed LRU-GENACO method is tested more comprehensively using four real proxy-log datasets from IRCache used in [25,35,44]. Subsequently, we tested it by calculating the hit ratio.…”

Section: Simulation and Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

LRU-GENACO: A Hybrid Cached Data Optimization Based on the Least Used Method Improved Using Ant Colony and Genetic Algorithms

et al. 2022

View full text Add to dashboard Cite

An optimization strategy for cached data offloading plays a crucial role in the edge network environment. This strategy can improve the performance of edge nodes with limited cache memory to serve data service requests from user terminals. The main challenge that must be solved in optimizing cached data offloading is assessing and selecting the cached data with the highest profit to be stored in the cache memory. Selecting the appropriate cached data can improve the utility of memory space to increase HR and reduce LSR. In this paper, we model the cached data offloading optimization strategy as the classic optimization KP01. The cached data offloading optimization strategy is then improved using a hybrid approach of three algorithms: LRU, ACO, and GA, called LRU-GENACO. The proposed LRU-GENACO was tested using four real proxy log datasets from IRCache. The simulation results show that the proposed LRU-GENACO hit ratio is superior to the LRU GDS SIZE algorithms by 13.1%, 26.96%, 53.78%, and 81.69%, respectively. The proposed LRU-GENACO method also reduces the average latency by 25.27%.

show abstract

“…Simultaneously, attempts to apply Machine Learning techniques to augment these strategies have gained momentum. For instance, [3] enhanced the LRU policy using supervised ML Algorithms like SVM, naive Bayes Classifier, and decision tree. Their work involved training ML models to predict data reusability, effectively boosting the LRU policy's performance.…”

Section: Related Workmentioning

confidence: 99%

STRCacheML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

Mahara,

Fuentes,

Poellabauer

et al. 2024

Artificial Intelligence and Big Data

View full text Add to dashboard Cite

Content caching is vital for enhancing web server efficiency and reducing network congestion, particularly in platforms predicting user actions. Despite many studies conducted toimprove cache replacement strategies, there remains space for improvement. This paper introduces STRCacheML, a Machine Learning (ML) assisted Content Caching Policy. STRCacheML leverages available attributes within a platform to make intelligent cache replacement decisions offline. We have tested various Machine Learning and Deep Learning algorithms to adapt the one with the highest accuracy; we have integrated that algorithm into our cache replacement policy. This selected ML algorithm was employed to estimate the likelihood of cache objects being requested again, an essential factor in cache eviction scenarios. The IMDb dataset, constituting numerous videos with corresponding attributes, was utilized to conduct our experiment. The experimental section highlights our model’s efficacy, presenting comparative results compared to the established approaches based on raw cache hits and cache hit rates.

show abstract

Performance Improvement of Web Proxy Cache Replacement using Intelligent Greedy-Dual Approaches

Cited by 5 publications

References 23 publications

Performance comparison of cache replacement algorithms onvarious internet traffic

Performance comparison of cache replacement algorithms onvarious internet traffic

LRU-GENACO: A Hybrid Cached Data Optimization Based on the Least Used Method Improved Using Ant Colony and Genetic Algorithms

STRCacheML: A Machine Learning-Assisted Content Caching Policy for Streaming Services

Contact Info

Product

Resources

About