Dynamic cooperative caching strategy for delay-sensitive applications in edge computing environment

In the context of the interconnection of everything, the edge data are experiencing explosive growth, and the bandwidth and computing resources of cloud computing cannot be efficiently processed. Edge computing, with its low latency, high throughput and low network pressure, has become a very effective mode to deal with massive data. Due to the increasing number of end users, a large number of data are generated on the edge of the network, and the timeliness of users’ service requirements is constantly improving, so further reducing the delay of cloud service network is still a major challenge. Cache is an effective solution to this problem. In order to make full use of the limited edge device space, a dynamic cache replacement algorithm is proposed based on edge popularity and node heat, which caches popular content in the core node and non-popular content in the secondary node, so as to improve the hit rate of the whole network and reduce the server load. In order to meet the increasing demand of data content access in the network, a cooperative caching algorithm is proposed. The idea of this algorithm is to put the cache object in the proper node, so that the user’s request can get timely response. Thus, the availability of the object is improved and the network delay is reduced. In the edge computing environment of campus network, dynamic cache replacement algorithm and cooperative cache algorithm are evaluated. The experimental results show that the dynamic cache replacement algorithm proposed in this paper is better than the benchmark replacement algorithm in cache hit rate, server load, average delay and average hops, and the cooperative cache algorithm is better than the benchmark cooperative cache algorithm in node hit rate and average hops.

[1]  Laith Mohammad Abualigah,et al.  Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering , 2018, Studies in Computational Intelligence.

[2]  Jianhua Ma,et al.  KID Model-Driven Things-Edge-Cloud Computing Paradigm for Traffic Data as a Service , 2018, IEEE Network.

[3]  Youlong Luo,et al.  Energy-efficient fault-tolerant replica management policy with deadline and budget constraints in edge-cloud environment , 2019, J. Netw. Comput. Appl..

[4]  Tang Jianhang,et al.  Joint optimization of data placement and scheduling for improving user experience in edge computing , 2019, J. Parallel Distributed Comput..

[5]  Laith Mohammad Abualigah,et al.  Hybrid clustering analysis using improved krill herd algorithm , 2018, Applied Intelligence.

[6]  Peichang Zhang,et al.  Maximum likelihood approach to DoA estimation using lens antenna array , 2019, EURASIP J. Wirel. Commun. Netw..

[7]  Haiquan Wang,et al.  A hotspot-based probabilistic cache placement policy for ICN in MANETs , 2019, EURASIP J. Wirel. Commun. Netw..

[8]  Xavier Masip-Bruin,et al.  Managing resources continuity from the edge to the cloud: Architecture and performance , 2018, Future Gener. Comput. Syst..

[9]  Sebastian Thiede,et al.  Identifying the potential of edge computing in factories through mixed reality , 2019 .

[10]  Ching-Hsien Hsu,et al.  Machine learning algorithms towards merging of mobile edge computing and Internet of Things , 2019, Comput. Networks.

[11]  Nirwan Ansari,et al.  An optimal delay aware task assignment scheme for wireless SDN networked edge cloudlets , 2020, Future Gener. Comput. Syst..

[12]  Thar Baker,et al.  Improving fog computing performance via Fog-2-Fog collaboration , 2019, Future Gener. Comput. Syst..

[13]  Laith Mohammad Abualigah,et al.  A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis , 2018, Eng. Appl. Artif. Intell..

[14]  Laith Mohammad Abualigah,et al.  APPLYING GENETIC ALGORITHMS TO INFORMATION RETRIEVAL USING VECTOR SPACE MODEL , 2015 .

[15]  Javier Bajo,et al.  Survey of agent-based cloud computing applications , 2019, Future Gener. Comput. Syst..

[16]  Hongming Cai,et al.  A short-term energy prediction system based on edge computing for smart city , 2019, Future Gener. Comput. Syst..

[17]  Liang He,et al.  ECASS: Edge computing based auxiliary sensing system for self-driving vehicles , 2019, J. Syst. Archit..

[18]  Leïla Azouz Saïdane,et al.  Towards a novel cache replacement strategy for Named Data Networking based on Software Defined Networking , 2017, Comput. Electr. Eng..

[19]  Lucia D'Acunto,et al.  BidCache: Auction-Based In-Network Caching in ICN , 2016, 2016 IEEE Globecom Workshops (GC Wkshps).

[20]  Alex Reznik,et al.  Mobile Edge Cloud System: Architectures, Challenges, and Approaches , 2018, IEEE Systems Journal.

[21]  Debashis De,et al.  Edge computing for Internet of Things: A survey, e-healthcare case study and future direction , 2019, J. Netw. Comput. Appl..

[22]  Laith Mohammad Abualigah,et al.  Modified Krill Herd Algorithm for Global Numerical Optimization Problems , 2018, Advances in Nature-Inspired Computing and Applications.

[23]  Laith Mohammad Abualigah,et al.  A new feature selection method to improve the document clustering using particle swarm optimization algorithm , 2017, J. Comput. Sci..

[24]  B. Shameedha Begum,et al.  Cache lifetime enhancement technique using hybrid cache-replacement-policy , 2019 .

[25]  Rodrigo Roman,et al.  Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges , 2016, Future Gener. Comput. Syst..

[26]  Youlong Luo,et al.  Collaborative cache allocation and task scheduling for data-intensive applications in edge computing environment , 2019, Future Gener. Comput. Syst..

[27]  Laith Mohammad Abualigah,et al.  Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering , 2017, The Journal of Supercomputing.

[28]  Alberto Ceselli,et al.  Mobile Edge Cloud Network Design Optimization , 2017, IEEE/ACM Transactions on Networking.

[29]  Jie Cui,et al.  Secure data sharing scheme for VANETs based on edge computing , 2019, EURASIP Journal on Wireless Communications and Networking.

[30]  Min Chen,et al.  Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis , 2018, IEEE Wireless Communications.

[31]  Donald F. Towsley,et al.  Joint cache resource allocation and request routing for in-network caching services , 2017, Comput. Networks.

[32]  George Pavlou,et al.  Cache "less for more" in information-centric networks (extended version) , 2013, Comput. Commun..

[33]  George Pavlou,et al.  Cache "Less for More" in Information-Centric Networks , 2012, Networking.

[34]  Xing Zhang,et al.  A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications , 2017, IEEE Access.

[35]  Youlong Luo,et al.  Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud , 2019, Future Gener. Comput. Syst..

[36]  R. Nadarajan,et al.  WARM Based Data Pre-fetching and Cache Replacement Strategies for Location Dependent Information System in Wireless Environment , 2016, Wirel. Pers. Commun..

[37]  Udai Shanker,et al.  SPMC-CRP:A Cache Replacement Policy for Location Dependent Data in Mobile Environment , 2018 .