Pricing and Repurchasing for Big Data Processing in Multi-Clouds

Abstract

Processing streaming big data becomes critical as new divers Internet of Tings (IoT) applications begin to emerge. Existing cloud pricing strategy is unfriendly for processing streaming big data with varying load. Multiple cloud environment is a potential solution with an efficient pay-on-demand pricing strategy for processing streaming big data. In this paper, we propose a intermediary framework with multiple cloud environment to provide streaming big data computing service with lower cost per load, in which a cloud service intermediary rents the cloud service from multiple cloud providers and provides streaming processing service to the users with multiple service interfaces. In this framework, we also propose a pricing strategy to maximize the revenue of the multiple cloud intermediary. With extensive simulations, our pricing strategy brings higher revenue than other pricing methods.

Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing

“…Taleb et al have analyzed the MEC reference architecture and main deployment scenarios and conducted an overview of the current standardization activities. Li et al [13] proposed an intermediary framework, where there exists an intermediary between multiple cloud providers and users. e intermediary first rents the cloud service from cloud providers and then provides streaming processing service to users with low cost and delay.…”

“…e increase in price and the increase in data transmission delay caused by network congestion will lead to the DSA adjusting the data resource strategy. e utility of the DSA can be expressed by the following formula: (13) where U i ( m j�1 x ij ) is the total revenue earned by the DSA when car is served by the DSA, p j is the price set by the FN, P j (p j x ij ) is the price paid by the DSA to the FN, and D j (x ij ) represents the delay cost of the DSA's service to cars.…”

Multiobjective Optimization in Cloud Brokering Systems for Connected Internet of Things

“…The proposed model can increase the profit of both the service providers and the applicant. In [13], the authors proposed a service framework and a pricing strategy for a multi-cloud environment. The proposed framework can provide streaming big data computing service and maximize the profits of the multi-cloud intermediary.…”

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for L p -Regularization Using the Multiple Sub-Dictionary Representation

“…The proposed algorithms can be applied in many practical applications [36,37,38,39,40,41,42]. In this section, we conduct some typical applications about sparse image recovery and medical imaging to extend the applications of $L_{1/2}$ and $L_{2/3}$ regularizations and illustrate the excellent robustness and adaptation of the proposed SAITA-$L_p$,$\left(p\in \left\{1/2,2/3\right\}\right)$ algorithm.…”