Adaptive priority-based cache replacement and prediction-based cache prefetching in edge computing environment

Li, Chunlin; Song, Mingyang; Du, Shaofeng; Wang, Xiaohai; Zhang, Min; Luo, Yingwei

doi:10.1016/j.jnca.2020.102715

Cited by 17 publications

(11 citation statements)

References 15 publications

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“…Cached data offloading is an extraordinary capability required to be owned by an application, workstation, server, and network to manage data storage considered to be larger than its capacity (Zulfa, Hartanto & Permanasari, 2020). It is a very common method used in cloud computing (Wang et al, 2019;Li et al, 2020a;Li et al, 2020b), mobile computing (Dutta & Vandermeer, 2017;Zhu & Reddi, 2017), operating system (Silberschatz, Galvin & Gagne, 2008;Tian & Liebelt, 2014), and telecommunication Prerna, Tekchandani & Kumar (2020). Luo et al (2017) examined the energy consumption optimization in Mobile Edge Computing (MEC) by formulating objective functions based on the variables of energy consumption, backhaul capacities, and content popularity.…”

Section: Related Workmentioning

confidence: 99%

“…Meanwhile, the knapsack problem belongs to a discrete domain. Therefore, additional performance indicators are the best and worst profit, the average profit achieved, and the total number of items in the knapsack (Rizk-Allah & Hassanien, 2018;Li et al, 2020a;Li et al, 2020b;Liu, 2020). However, the best solution in the cached data offloading optimization case study was selected based on the highest number of knapsack items with the lowest objective function value.…”

Section: Performance Measurementmentioning

confidence: 99%

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GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

Zulfa

Hartanto

Permanasari

et al. 2021

PeerJ Computer Science

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Background Data exchange and management have been observed to be improving with the rapid growth of 5G technology, edge computing, and the Internet of Things (IoT). Moreover, edge computing is expected to quickly serve extensive and massive data requests despite its limited storage capacity. Such a situation needs data caching and offloading capabilities for proper distribution to users. These capabilities also need to be optimized due to the experience constraints, such as data priority determination, limited storage, and execution time. Methods We proposed a novel framework called Genetic and Ant Colony Optimization (GenACO) to improve the performance of the cached data optimization implemented in previous research by providing a more optimum objective function value. GenACO improves the solution selection probability mechanism to ensure a more reliable balancing of the exploration and exploitation process involved in finding solutions. Moreover, the GenACO has two modes: cyclic and non-cyclic, confirmed to have the ability to increase the optimal cached data solution, improve average solution quality, and reduce the total time consumption from the previous research results. Result The experimental results demonstrated that the proposed GenACO outperformed the previous work by minimizing the objective function of cached data optimization from 0.4374 to 0.4350 and reducing the time consumption by up to 47%.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Measurementmentioning

confidence: 99%

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

Zulfa

Hartanto

Permanasari

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

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“…Many researchers have contributed to the placement of the data and efficient task scheduling in edge servers. Prediction-based cache prefetching and adaptive priority cache replacement approach named Context-Aware Data and Task processing was proposed by Li et al 20 with the benefits of Least Recently Used and priority for minimizing the delay with improved cache hit rate. The replacement in this adaptive priority cache replacement approach was attained based on the smallest re-access file with reduced delay and prefetching time.…”

Section: Related Workmentioning

confidence: 99%

“…Calculate A T−C (U Task(a) , C j G ) //Association between the tasks and the generated containers// 19. for each edge server, 'j' do 20.…”

Section: Integrated Data Placement and Task Scheduling Algorithmmentioning

confidence: 99%

Hybrid Harris Hawk‐Salp swarm optimization algorithm‐based integrated optimal data placement and task scheduling for improving the user experience in edge computing

Vivekanandan¹,

Gnansekaran²

2021

Concurrency and Computation

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Edge computing permits the computation process at the edge of the network for enhancing the efficiency of the data access. The problem of optimal data block placement entirely depends on the maximization of interdependent factors, which corresponds to an NP-hard problem. In this paper, the Hybrid Harris Hawk-Salp Swarm-based Optimization, Data Placement and Task Scheduling (HHHSS-ODPTS) scheme is proposed for improving the user experience in edge computing. This data placement is devised considering the popularity and user preference of data blocks, storage capacity of edge server, and the ratio of replacement associated with the edge servers. This proposed scheme utilized a 2/3-approximation method for essential mapping of tasks with generated containers of the edge server. The simulation results confirmed that the proposed HHHSS-ODPTS scheme is better at data response time, response time of tasks, number of the replaced data blocks, and hit rate of tasks with different capacity of data storage, number of required data blocks, and data popularity.

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“…It attempts to identify the patterns of individual web requests, stores them, and acts as a proxy by responding to the demands of the clients in the place of servers. Over the years, researchers develop several implementations [1]- [8] of web proxy caches. A set of presenter devices [6] can be used as a proxy between the collaboration server and the clients.…”

Section: Introductionmentioning

confidence: 99%

An Improved Web Caching System With Locally Normalized User Intervals

Vaithinathan¹,

Benadit

Parthiban

et al. 2021

IEEE Access

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Caching is one of the most promising areas in the field of future internet architecture like Information-centric Networking, Software Defined Networking, and IoT. In Web caching, most of the web content is readily available across the network, even if the webserver is not reachable. Several existing traditional caching methods and cache replacement strategies are evaluated based on the metrics like hit ratio and byte hit Ratio. However, these metrics have not been improved over the period because of the traditional caching policies. So, in this paper, we have used an intelligent function like locally normalized intervals of page visit, website duration, users' interest between user groups is proposed. These intervals are combined with multiple distance metrics like Manhattan, squared Euclidean, and 3-,4-,5-norm Minkowski. In order to obtain significant common user navigation patterns, the clustering relation between the users using different intervals and distances is thoroughly analyzed. These patterns are successfully coupled with greedy web cache replacement strategies to improve the efficiency of the proposed web cache system. Particularly for improving the caching metrics more, we used an AI-based intelligent approach like Random Forest classifier to boost the prefetch buffer performance and achieves the maximum hit rate of 0.89, 0.90, and byte hit rate of 0.87, 0.89 for Greedy Dual Size Frequency and Weighted Greedy Dual Size Frequency algorithms, respectively. Our experiments show good hit/byte hit rates than the frequently used algorithms like least recently used and least frequently used.

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Adaptive priority-based cache replacement and prediction-based cache prefetching in edge computing environment

Cited by 17 publications

References 15 publications

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

GenACO a multi-objective cached data offloading optimization based on genetic algorithm and ant colony optimization

Hybrid Harris Hawk‐Salp swarm optimization algorithm‐based integrated optimal data placement and task scheduling for improving the user experience in edge computing

An Improved Web Caching System With Locally Normalized User Intervals

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