The proliferation of mobile devices that support the acceleration of data services (especially smartphones) has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary contents. Thus, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching.

Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

Principal-agent relationships between the supervisory board and the management of bidding firms in auctions are widespread in high-stakes auctions. Often only the agent has information about the value of the objects being sold. The board wants to maximize the profit, but the management wants to win the package with the highest value. In environments where it is efficient for firms to coordinate, we show that the principals would coordinate, while the agents would not. We analyze environments with decreasing levels of information that the principal has about the valuations. Sometimes it can also be impossible to set budget constraints which align the agents’ strategies in equilibrium. The analysis helps explain price wars in high-stakes auctions.

A Principal-Agent Model of Bidding Firms in Multi-Unit Auctions.

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 closed-form 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. 

/pdf/caching-and-multicasting-for-fog-radio-access-networks-3q1selja.pdf

Caching and Multicasting for Fog Radio Access Networks

Online data caching in edge computing

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 closed-form 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.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09656846.pdf

Data caching in the cloud is an efficient way to improve the QoS of diverse data applications. However, this benefit is not freely available, given monetary cost to manage the caches in the cloud. In this paper, we study the data caching problem in the cloud that is driven by the monetary cost reduction, instead of the hit rate under limited capacity as in traditional cases. In particular, given a stream of requests $\mathcal{R}$ to a shared data item, we present a shortest-path based optimal algorithm that can minimize the total transfer and caching costs within O(mn) time for off-line case, here m represents the number of nodes in the network, while n is the length of the request stream. The cost model in this computation is semi-homo, which indicates that all pairs of nodes have the same transfer cost, but each cache server node has its own caching cost rate. Our off-line algorithm improves the previous results not only in reducing the time complexity from O(m2n) to O(mn), but also in relaxing the cost model to be semi-homogeneous, rendering the algorithm more practical in reality. Furthermore, we also study this problem in its online form, and by extending the anticipatory caching idea, we propose a 2-competitive online algorithm based on the same cost model and show its tightness by giving a lower bound of the competitive ratio as 2 − o(1) for any deterministic online algorithm. We provably achieve these results with our deep insights into the problem and careful analysis of the solution algorithms, together with a trace-based study to evaluate their performance in reality.

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

We consider the contract design problem where a principal designs a contract to incentivize an agent to undertake n independent tasks. In the contract, the principal decides the payment $\textit{\textbf{w}}=\{w_0, w_1, ..., w_n\}$. The principal is associated with a non-decreasing revenue function f(k), where k is the number of successful tasks. The objective of the contract design problem is to maximize the principal’s expected profit, i.e., $\max _{\textit{\textbf{s}},\textit{\textbf{w}}}\{F(\textit{\textbf{s}})-W(\textit{\textbf{s}},\textit{\textbf{w}})\}$, where $\textit{\textbf{s}}$ is the agent’s strategy set, $F(\textit{\textbf{s}})$ is the expected revenue of the principal and $W(\textit{\textbf{s}},\textit{\textbf{w}})$ is the expected payment to the agent. Given the payment $\textit{\textbf{w}}$, the agent will choose her strategy from $\textit{\textbf{s}}$ to maximize her expected utility. For each task, the agent may work hard or shirk and her strategy is to decide the number of tasks to work hard on. If the principal knows the strategy set $\textit{\textbf{s}}$, the optimum contract can be found by linear programming. In [7], a more complicated model where the strategy set $\textit{\textbf{s}}$ is unknown to the principal is considered, they showed that the linear contract is robust and presented an 1/N-approximation algorithm given $N\le n$ dominating strategies. In this paper, we further analyze this model and prove the approximation factor can be upper bounded by $(1-\alpha _{N})/(1-\alpha _{N}^{N})$, where $\alpha _{N}\in [0,1)$ is a given constant.

Robustness and Approximation for the Linear Contract Design

In the edge-cloud environment, offloading technique decides the task to be executed either at the cloud or at the edge. Offloading can improve the quality of service and the efficiency of the system. In most previous works on the offloading problem, the communication costs between tasks on both cloud side or the edge side are often ignored. We consider a general offloading model where the communication costs between any two tasks is non-zero and asymmetric. Moreover, due to the resource limitation on the edge side, we assume that the number of tasks executed on the edge side is bounded by a fixed constant k. This generalized offloading problem is NP-hard in minimizing the total cost with cardinality constraint. Based on semidefinite program, we give an approximation algorithm with the performance guarantee of $2/\pi $.

Approximation Algorithm for the Offloading Problem in Edge Computing

We consider a classical principal–agent model in the contract theory A principal designs the payment $$\mathbf {w}=\left\{ w_{0},w_{1},\ldots ,w_{n}\right\} $$
 to incentivize the agent to enter into the contract Given the payment $$\mathbf {w}$$
 , the agent will take hidden actions from her strategy set $$\mathbf {S_{t}^{n}}$$
 to finish it and from the perspective of the agent, she will select the best strategy to maximize her expected utility Due to the hidden strategy set, the principal obtains the expected revenue $$R(S_{t}^{n})$$
 from the agent Furthermore, the principal has a non-decreasing revenue function r(k), which is common information, where k is the number of successful tasks in the total n independent tasks The objective of the problem is to maximize the principal’s expected profit, ie, $$\max _{S,\mathbf {w}}\left\{ R(S_{t}^{n})-P(S_{t}^{n},\mathbf {w})\right\} $$
 , where $$P(S_{t}^{n},\mathbf {w})$$
 is the agent’s expected payment The difficulty of this problem is due to the asymmetric information If the principal knows all the information about the agent, then the optimal contract can be solved by linear programming Based on Dutting et al (in: Proceedings of the EC, pp 369–387, 2019), we consider the more general model When information is asymmetric, we further analyze that the approximation ratio of the linear contract can reach $$(1-\alpha _{N})/(1-\alpha _{N}^{N})$$
 , which improves the results of Dutting et al (in: Proceedings of the EC, pp 369–387, 2019), where $$\alpha _{N}\in [0,1)$$
 is a given constant and the coefficient of the linear contract

Xinxin Han

Papers

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

Online data caching in edge computing

Robustness and Approximation for the Linear Contract Design

Approximation Algorithm for the Offloading Problem in Edge Computing

Principal–agent problem under the linear contract