# Traffic-Aware Optimization of Task Offloading and Content
## 2 Related Work  
In the high-speed mobile environment of the IoV, efficient task execution plays a crucial role in minimizing time delays. However, the limited computing and storage capacity of automotive systems often necessitate offloading resource-intensive tasks. These tasks are strategically shifted to edge or cloud servers, enabling efficient execution and ensuring prompt fulfillment of user requests. Automotive applications frequently involve repeated content requests, making caching popular content on edge servers highly effective in reducing latency and energy consumption for subsequent access in the IoV. Nevertheless, improper task offloading and inaccurate content caching can lead to increased energy con sumption and latency, mainly due to network congestion and server queuing. To address these challenges, the joint optimization of task offloading and content caching is essential. By harmonizing these processes, we can effectively minimize time latency and energy consumption, enhancing the overall performance and user experience in the IoV. In recent years, the problem of task offloading in cloud-edge environments has received much attention. In their pursuit of optimizing data placement performance, [PERSON] et al. [4] recognized the significance of spatiotemporal data characteristics. They introduced TEMPLiH, where a temperature matrix captures the influence of data features on placement. The Replica Selection Algorithm (RSA-TM) is utilized to ensure compliance with latency requirements. Furthermore, an improved Hungarian algorithm (IHA-RM) based on replica selection is employed to achieve multi-objective balance. [PERSON] et al. [5] proposed an energy-efficient offloading scheme to minimize the overall energy usage. Although their algorithm exhibits low energy consumption on a single task, additional research is needed to evaluate its effectiveness in a multi-task setting. In order to optimize data caching and task scheduling jointly, [PERSON] et al. [6] proposed a multi-index cooperative cache replacement algorithm based on information entropy theory (MCCR) to improve the cache hit rate. Subsequently, they further proposed the NHSA-MCCR algorithm, which aims to optimize the scheduling to achieve the optimization of delays and energy consumption. [PERSON] et al. [7] presented a UT-UDN system model that demonstrated a 20% reduction in time delay and a 30% decrease in energy costs, as indicated by simulation results. [PERSON] et al. [8] proposed a Mobile Edge Computing solution for Unmanned Aerial Vehicles (UAVs), which uses a multi-layer resource allocation scheme, a load balancing algorithm, and integer programming to achieve cost reduction. In the context of multi-cloud spatial crowdsourcing data placement, [PERSON] et al. [9] introduced a data placement strategy with a focus on cost-effectiveness and minimal latency. Concurrently, they incorporated the interval pricing strategy and utilized a clustering algorithm to analyze the geographic distribution patterns of data centers. Furthermore, certain studies have investigated the application of heuristics to address the task offloading problem. For instance, [PERSON] et al. [10] employed enumeration and branch-and-bound algorithms to tackle these challenges. Meanwhile, [PERSON] et al. [11] introduced a task scheduling and resource management strategy based on an enhanced genetic algorithm. This approach took into account both delays and energy consumption, with the objective of minimizing their combined sum. [PERSON] et al. [12] introduced mMAPO as a solution for optimizing conflicting objectives in multi-objective optimization, including completion time, energy consumption, and economic cost. [PERSON] et al. [13] addressed the challenge of edge server state changes and the unavailability of global information by introducing Dec-POMDP, which handles observed states, and a task offloading strategy based on the Value Decomposition Network (VDN). [PERSON] et al.