Cost-Driven Data Caching in the Cloud: An Algorithmic Approach

Wang, Yan; Zhang, Yong; Han, Xinxin; Wang, Pengfei; Xu, Cheng‐Zhong; Horton, J. D.; Culberson, Joseph

doi:10.1109/infocom42981.2021.9488820

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…Supposed the caching cost of the data k is

μ_{k}

at a server, and the storage cost is equal to the maximum storage cost when multiple data items are stored on a server. The AC algorithm in Reference 24 is valid for our semiheterogeneous model. What is more, for the submodular model in its general form, it is also effective.…”

Section: Semiheterogeneous Model and Better Algorithmmentioning

confidence: 99%

“…Subsequently, they extended their analysis to semihomogeneous model, the transmission cost of the data is identical, but the storage cost of servers depends on the storage capacity of the server itself. Reference 24 gave an offline algorithm to gain the optimal caching strategy with time complexity

O (m n \log (m n))

and presented a 2‐competitive algorithm by tradeoff the transmission cost and the caching cost for the online model of this problem.…”

Section: Introductionmentioning

confidence: 99%

“…Under the homogeneous model, the storage cost per unit time of servers is the same and the transmission cost between any two servers is identical, we design a 3‐competitive online algorithm. Suppose that the caching cost function per unit time for all servers is submodular, we present an online modified competitive algorithm based on the homogeneous online algorithm. Different from the above heterogeneous model, our heterogeneous model depends on data items, that is, the storage cost per unit time of the server is different for data items but the transmission cost between any two servers is identical. We propose the AC algorithm in Reference 24 is valid for this model. What is more, the AC algorithm can achieve better competition for our above three models. …”

Section: Introductionmentioning

confidence: 99%

“…In Sections 3 and 4, our homogeneous model, submodular model, and the corresponding online algorithm are shown, respectively. Besides, we introduce our heterogeneous model and the AC algorithm in Reference 24 and prove it is a better algorithm for the above model in Section 5. Finally, we conclude our results and show our views for future research work about data caching problem in Section 6.…”

Section: Introductionmentioning

confidence: 99%

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Online data caching in edge computing

Han

Gao

Wang

et al. 2021

Concurrency and Computation

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Summary Data caching is an effective method to reduce traffic and improve the quality of service in network. Traditionally, users' requests are offloaded to the cloud for centralized computing. However, due to security and privacy, these tasks are executed in the nearest server, so that the data and service needed by the task are also essential. After the task is completed, in case the next arriving request needs the same data, resulting in transmission cost, the data need to be stored for a period of time, because we know nothing about the coming request information under an online request stream. In this article, we study data caching problem by extending single data item to multiple data items among servers. About the homogeneous model and the submodular model with constraint, we propose a data caching strategy minimizing the total transfer and caching costs of the system. Moreover, we also solve the semiheterogeneous model by the anticipatory caching (AC) algorithm in Reference 21. Meanwhile we find it is more efficient for our three models in this article to improve the performance.

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“…Supposed the caching cost of the data k is

μ_{k}

Section: Semiheterogeneous Model and Better Algorithmmentioning

confidence: 99%

O (m n \log (m n))

and presented a 2‐competitive algorithm by tradeoff the transmission cost and the caching cost for the online model of this problem.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Online data caching in edge computing

Han

Gao

Wang

et al. 2021

Concurrency and Computation

Self Cite

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show abstract

“…The caching problem in the cloud has been addressed by [6], wherein the authors proposed optimal algorithms to minimize the total transfer and caching costs in the online and off-line cases. In [7], a federated learning framework was proposed to predict the user demands of a specific edge content in order to provide the exact location for its placement.…”

Section: Introductionmentioning

confidence: 99%

Caching and Multicasting for Fog Radio Access Networks

et al. 2022

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Cache-enabled Fog radio access network (F-RAN) is a promising technology to alleviate the traffic congestion and boost the contents delivery success rate. The efficiency of disseminating the cached contents in F-RAN can be boosted by enabling multicast service at the fog access points (F-APs). This paper proposes a joint random caching and multicasting optimization scheme for wireless backhauled F-RAN. Using tools from stochastic geometry, the expression of the successful transmission probability (STP) is derived by carefully analyzing the different types of serving F-APs and interferers. Then, a closedform expression of the asymptotic STP in the high SNR region is derived to reduce the complexity. The joint caching and multicasting optimization problem is formulated to maximize the STP. The optimization problem is complex and non-convex in general. A novel projected cuckoo search algorithm (PCSA) is proposed to obtain the optimal content placement that maximizes the STP. The numerical simulation results show that PCSA outperforms the original cuckoo search algorithm (CSA) and the proposed asymptotically joint caching and multicasting scheme outperforms the benchmark caching schemes by up to 15% higher STPs.

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