# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 2 Related Work  
[PERSON] et al. [31] considered a data transmission network architecture based on the Manhattan mobility model and proposed a temporal convolutional network (TCN) model to predict the allocation of the weight of delay. Finally, they solve the optimal routing problem using a genetic algorithm based on a reinforcement learning mechanism (RLGA) to pre-allocate resources for offloading requests. [PERSON]. [PERSON] et al. [32] proposed four schemes to dynamically and adaptively reduce the delay in user services. These schemes (Adaptive, Decision Tree-Based, Hybrid, and Lazy) have low-overhead and are effective. [PERSON] et al. [33] proposed a new communication planning approach that enables the vehicle to optimally preselect the available RATs such as Wi-Fi, LTE, or 5G to offload tasks to clouds when its local resources are insufficient.  
[PERSON] et al. [8] proposed a hierarchical multi-agent deep reinforcement learning (H-MADRL) framework to maximize the overall energy efficiency by jointly optimizing the beamforming of the access point (AP) and the users' offloading decisions.  
Sum of successfully offloaded tasksHe et al. [34] proposed a task flow graph (TFG) to depict the collaborative data offloading procedure in earth observation satellite networks. Based on the proposed TFG, authors formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of successfully offloaded tasks.  
[PERSON] et al. [35] present a novel data offloading scheme built upon the exponential learning-based minority game (MG) theory which leverages the unlicensed Wi-Fi spectrum for the cellular users while at the same time, maintains the quality of service.  
ThroughputJia et al. [36] proposed a collaborative data offloading scheme to maximize the overall system throughput. The data amount of each offloading task is represented as the number of equal packets. As such, they introduced decision variables to indicate whether a larger number of packets are scheduled instead of which one task is offloaded, thereby significantly enlarging the solution space and further increasing the computation complexity. [PERSON] et al. [37] considered a user-centric Wi-Fi data offloading model in heterogeneous networks and optimized the user throughput. The heterogeneous network collects network information, such as the number of users in Wi-Fi networks and their traffic load. Then, the network decides the specific portion of traffic to be transmitted via Wi-Fi networks. [PERSON] et al. [38] devised an iterative algorithm by progressively tightening the realistic constraints of data offloading to maximize the total amount of earth observation data.  
Data lossParmeet [39] proposed a technique for the fault tolerant offloading of data by IoT devices such that the data collected by them is transferred to the CC with minimal loss. The proposed technique employs opportunistic contacts between IoT and mobile fog nodes to provide a fault tolerant enhancement to the IoT architecture.  
We summarize the existing works on computing offloading in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimization Objective** & **Reference** & **Strategy** & \multicolumn{2}{c}{**Processing Node**} & \multirow{2}{*}{**Offloading Type**} \\  & & & **End** & **Edge** & **Cloud** \\ \hline \multirow{4}{*}{Latency} & [PERSON] et al. [32] & Adaptive Decision Tree-Based & \(\surd\) & & Delay tolerance \\  & & Hybrid and Lazy & & & \\ \cline{1-1}  & [PERSON] et al. [31] & Heuristic \& RL & \(\surd\) & \(\surd\) & Allocate resources \\ \cline{1-1}  & [PERSON] et al. [33] & RL & \(\surd\) & \(\surd\) & RATs preselect \\ \hline \multicolumn{1}{c}{Energy} & [PERSON] et al. [8] & DRL & \(\surd\) & \(\surd\) & \multicolumn{1}{c}{AP decision} \\ \hline Success rate & [PERSON] et al. [38] & Heuristic & & \(\surd\) & \multicolumn{1}{c}{Path decision} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of existing data offloading algorithms.