Value‐aware cache replacement in edge networks for Internet of Things
Authors: 
Ting Peng, Haohao Wang, Cangming Liang, Pingping Dong, Yehua Wei, Jianping Yu, Lianming ZhangAuthors Info & Claims
Ting Peng, Haohao Wang, Cangming Liang, Pingping Dong, Yehua Wei, Jianping Yu, Lianming ZhangAuthors Info & ClaimsAbstract
With the development of Internet of Things (IoT), massive amounts of data will be brought. By offloading caching from the cloud to the edge, edge caching technology represents a promising solution in the era of IoT to meet the delay requirements of IoT applications. An efficient cache decision and replacement strategy on edge caching devices is a key factor in ensuring the cache hit ratio. The existing cache replacement policies do not comprehensively consider the characteristics of cache files and are likely to result in cache pollution problems. In order to cache data reasonably, to improve the cache hit ratio, and to reduce the user request delay, we propose a concept of file cache value and a file cache value‐aware cache replacement (FCVACR) algorithm of the edge cache system in this article. File cache value is associated with three aspects of cache file: the file size, the file popularity, and the time of requests. The proposed FCVACR algorithm adopts the file cache value method, improves the utilization of edge caching space, and reduces the content transmission delay. Experimental results show that the proposed FCVACR algorithm has a higher cache hit ratio and lower user request delay than the classical cache replacement algorithms.
Graphical Abstract
We develop a new model of the network edge caching system that explores the impact of the user request behavior, file popularity, and file size on the edge caching system and user request latency. We propose a network edge cache decision strategy based on the characteristics of file size, study the cache replacement strategy to minimize the average latency and maximize the cache hit ratio by introducing file cache value, and propose a cache replacement algorithm based on file cache value‐aware.
References
[1]
Farhan L, Shukur ST, Alissa AE, Alrweg M, Raza U, Kharel R A survey on the challenges and opportunities of the Internet of Things (IoT). Paper presented at: Proceedings of the 2017 11th International Conference on Sensing Technology. Sydney, NSW; 2017:1‐5.
[2]
Mutlag AA, Ghani MKA, Mohammed MA, et al. MAFC: multi‐agent fog computing model for healthcare critical tasks management. Sensors. 2020;20(7):1853‐1872.
[3]
Mostafa SA, Gunasekaran SS, Mustapha A, Mohammed MA, Abduallah WM. Modelling an adjustable autonomous multi‐agent Internet of Things system for elderly smart home. Adv Neuroergonomics Cognit Eng. 2020;301‐311.
[4]
Yin D, Zhang L, Yang K. A DDoS attack detection and mitigation with software‐defined Internet of Things framework. IEEE Access. 2018;6:24694‐24705.
[5]
Chen Z, Luo Z, Duan X, Zhang L. Terminal handover in software‐defined WLANs. EURASIP J Wirel Commun Netw. 2020;2020:68.
[6]
Cisco global cloud index: forecast and methodology, 2016‐2021 White Paper. https://www.cisco.com/c/en/us/solutions/collateral/service‐provider/global‐cloud‐index‐gci/white‐paper‐c11‐738085.html.
[7]
Botta A, Donato WD, Persico V. Integration of cloud computing and Internet of Things. Futur Gener Comput Syst. 2016;56(C):684‐700.
[8]
Abdulkareem KH, Mohammed MA, Gunasekaran SS, et al. A review of fog computing and machine learning: concepts, applications, challenges, and open issues. IEEE Access. 2019;7:153123‐153140.
[9]
Mutlag AA, Khanapi M, Arunkumar N, Mohammed MA, Mohd O. Enabling technologies for fog computing in healthcare IoT systems. Futur Gener Comput Syst. 2019;90:62‐78.
[10]
Zhang L, Zhang H, Tang Q, et al. LNTP: an end‐to‐end online prediction model for network traffic. IEEE Netw. 2021;35(1):226–233.
[11]
Tang Y, Hu Y, Zhang L. A classification‐based virtual machine placement algorithm in mobile cloud computing. KSII Trans Internet Inf Syst. 2016;10(5):1998‐2014.
[12]
Kuang Z, Liu G, Li G, Deng X. Energy efficient resource allocation algorithm in energy harvesting‐based D2D heterogeneous networks. IEEE IoT J. 2019;6(2):557‐567.
[13]
Yu W, Liang F, He X, et al. A survey on the edge computing for the Internet of Things. IEEE Access. 2018;6:6900‐6919.
[14]
Wang S, Zhang X, Zhang Y, Wang L, Yang J, Wang W. A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access. 2017;5:6757‐6779.
[15]
Xu F, Yang F, Zhao C, Fang C. Edge computing and caching based blockchain IoT network. Paper presented at: Proceedings of the 2018 1st IEEE International Conference on Hot Information‐Centric Networking. Shenzhen, China; 2018:238‐239.
[16]
Hajiakhondi‐Meybodi Z, Abouei J, Raouf AHF. Cache replacement schemes based on adaptive time window for video on demand services in femtocell networks. IEEE Trans Mob Comput. 2019;18(7):1476‐1487.
[17]
Tang Y, Guo K, Ma J, Shen Y, Chi T. A smart caching mechanism for mobile multimedia in information centric networking with edge computing. Futur Gener Comput Syst. 2019;91:590‐600.
[18]
Zhang YM, Feng BH, Quan W, et al. Cooperative edge caching: a multi‐agent deep learning based approach. IEEE Access. 2020;8:133212‐133224.
[19]
Hao Y, Li M, Wu D, Chen M, Hassan MM, Fortino G. Human‐like hybrid caching in software‐defined edge cloud. IEEE IoT J. 2020;7(7):5806‐5815.
[20]
Tang J, Zhou Z, Xue X, Wang G. Using collaborative edge‐cloud cache for search in Internet of Things. IEEE IoT J. 2020;7(2):922‐936.
[21]
Gao K, Han F, Dong P, Xiong N, Du R. Connected vehicle as a mobile sensor for real time queue length at signalized intersections. Sensors. 2019;19(9):2059.
[22]
Jiang F, Wang K, Dong L, Pan C, Xu W, Yang K. Deep learning based joint resource scheduling algorithms for hybrid MEC networks. IEEE IoT J. 2020;7(7):6252‐6265.
[23]
Tran AT, Nguyen TV, Tuong VD, Dao NN, Cho S. On stalling minimization of adaptive bitrate video services in edge caching systems. Paper presented at: Proceedings of the 2020 International Conference on Information Networking. Barcelona, Spain; 2020:115‐116.
[24]
Mach P, Becvar Z. Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor. 2017;19(3):1628‐1656.
[25]
Wang X, Chen M, Taleb T, Ksentini A. Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag. 2014;52(2):131‐139.
[26]
Aloulou N, Ayari M, Zhani MF. A popularity‐driven controller‐based routing and cooperative caching for named data networks. Paper presented at: Proceedings of the 2015 6th International Conference on the Network of the Future. Montreal, QC, Canada; 2015:1‐5.
[27]
Yan H, Gao D, Su W. Caching strategy based on hierarchical cluster for named data networking. IEEE Access. 2017;5:8433‐8443.
[28]
Gao J, Zhao L, Sun L. Probabilistic caching as mixed strategies in spatially‐coupled edge caching. Paper presented at: Proceedings of the 2018 29th Biennial Symposium on Communications. Toronto, ON, Canada; 2018:1‐5.
[29]
Shurman MM, Al‐Rashdan RM, Al‐Bataineh MK. Comparison study of FIFO and MDRR queuing mechanisms on 5G cellular network. Paper presented at: Proceedings of the 2018 9th International Conference on Information and Communication Systems. Irbid, Jordan; 2018:61‐65.
[30]
Vakil‐Ghahani A, Mahdizadeh‐Shahri S, Lotfi‐Namin MR, Bakhshalipour M, Lotfi‐Kamran P, Sarbazi‐Azad H. Cache replacement policy based on expected hit count. IEEE Comput Archit Lett. 2018;17(1):64‐67.
[31]
Hasslinger G, Heikkinen J, Ntougias K, Hasslinger F, Hohlfeld O. Optimum caching versus LRU and LFU: comparison and combined limited look‐ahead strategies. Paper presented at: Proceedings of the 2018 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks. Shanghai, China; 2018:1‐6.
[32]
Gallo M, Kauffmann B, Muscariello L, Simonian A, Tanguy C. Performance evaluation of the random replacement policy for networks of caches. Paper presented at: Proceedings of the 12th ACM Sigmetrics/Performance Joint International Conference on Measurement and Modeling of Computer Systems. London England, UK; 2012:395‐396.
[33]
Kurniawan FS, Yovita LV, Wibowo TA Modified‐LRU algorithm for caching on named data network. Paper presented at: Proceedings of the 2019 International Conference on Electrical Engineering and Informatics. Bandung, Indonesia; 2019:438‐443.
[34]
Zhang L, Wang K, Xuan D, Yang K. Optimal task allocation in near‐far computing enhanced C‐RAN for wireless big data processing. IEEE Wirel Commun. 2018;25(1):50‐55.
[35]
Tran TX, Hajisami A, Pandey P, Pompili D. Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag. 2017;55(4):54‐61.
[36]
Du J, Jiang C, Gelenbe E. Double auction mechanism design for video caching in heterogeneous ultra‐dense networks. IEEE Trans Wirel Commun. 2019;18(3):1669‐1683.
[37]
Luo Z, LiWang M, Lin Z, Huang L, Du X, Guizani M. Energy‐efficient caching for mobile edge computing in 5G networks. Appl Sci. 2017;7(6):557.
[38]
Gelenbe E. The distribution of a program in primary and fast buffer storage. Commun ACM. 1973;16(7):431‐434.
[39]
Gelenbe E. A unified approach to the evaluation of a class of replacement algorithms. IEEE Trans Comput. 1973;100(6):611‐618.
[40]
Gelenbe E, Zhu Q. Adaptive control of pre‐fetching. Perform Eval. 2001;46(2‐3):177‐192.
[41]
Wang K, Yang K, Chen HH, Zhang L. Computation diversity in emerging networking paradigms. IEEE Wirel Commun. 2017;24(1):88‐94.
[42]
Matsumoto T, Onoyama T, Komoda N. File size distribution model in enterprise file server toward efficient operational management. Paper presented at: Proceedings of the World Congress on Engineering and Computer Science. San Francisco; vol. 2, 2012:1400‐1404.
Index Terms
- Value‐aware cache replacement in edge networks for Internet of Things
Index terms have been assigned to the content through auto-classification.
Recommendations
A Case for MLP-Aware Cache Replacement
ISCA '06: Proceedings of the 33rd annual international symposium on Computer ArchitecturePerformance loss due to long-latency memory accesses can be reduced by servicing multiple memory accesses concurrently. The notion of generating and servicing long-latency cache misses in parallel is called Memory Level Parallelism (MLP). MLP is not ...
A Case for MLP-Aware Cache Replacement
Performance loss due to long-latency memory accesses can be reduced by servicing multiple memory accesses concurrently. The notion of generating and servicing long-latency cache misses in parallel is called Memory Level Parallelism (MLP). MLP is not ...
Counter-Based Cache Replacement and Bypassing Algorithms
Recent studies have shown that in highly associative caches, the performance gap between the Least Recently Used (LRU) and the theoretical optimal replacement algorithms is large, motivating the design of alternative replacement algorithms to improve ...
Comments
Information & Contributors
Information
Published In
© 2021 John Wiley & Sons, Ltd.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 08 September 2021
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025