# Traffic-Aware Optimization of Task Offloading and Content
## 2 Related Work
[14] concentrated on addressing the security concerns in edge computing and introduced the priority-aware PASTO algorithm. This algorithm aims to minimize the overall task completion time while adhering to energy budget constraints for secure task offloading. These efforts have been concentrated on addressing computation offloading and caching challenges in cloud-edge environments, optimizing for objectives such as latency, energy consumption, cost, and load performance, yielding substantial results. Nevertheless, cloud-edge environments constitute a vast research domain, necessitating a renewed focus. Consequently, our upcoming investigation will center on the resolution of computation offloading and caching challenges in vehicular edge environments, aiming to explore solutions within this particular domain. The in-vehicle edge environment merges computing resources from the vehicle and the edge, creating a strong platform for computing services. [PERSON] et al. [15] introduced a location-based offloading scheme that effectively balances task speed with communication and computational resources, leading to significant system cost reduction. To tackle challenges related to task latency and constraints in RSU (roadside unit) server resources, [PERSON] et al. [16] proposed a contract-based computing resource allocation scheme in a cloud environment, aiming to maximize the benefits of MEC service providers while ad heering to latency limits. [PERSON] et al. [17] devised a novel approach by splitting the joint load balancing and offloading problem into two sub-problems, formulated as a mixed-integer nonlinear programming problem, with the primary objective of maximizing system utility under latency constraints. In the realm of 5G networks, [PERSON] et al. [18] introduced an edge computing framework for offloading using multi-objective optimization and evolutionary algorithms, efficiently exploring the synergy between offloading and resource allocation within MEC and cloud computing, resulting in optimized task duration and server costs. For comprehensive optimization, [PERSON] et al. [19] proposed a collaborative approach that minimizes task duration and server costs through joint optimization of offloading and resource allocation in the MEC and cloud computing domains. To solve the problem of communication delay and energy loss caused by the growth in IoV services, [PERSON] [20] proposed that through a comprehensive analysis of the optoelectronic communication and computing model, the vehicle computing task should be encoded and transformed into a knapsack problem, where the genetic algorithm is used to solve the optimal resource allocation strategy. [PERSON] et al. [21] proposed a data offloading strategy called PKMR, which considers a predicted k-hop count limit and utilizes VVR paths for data offloading with neighboring Rsus. [PERSON] et al. [22] introduced the PVTO method, which offloads V2V tasks to MEC and optimizes the strategy using GA, SAW, and MCDM. [PERSON] et al. [23] introduced the belief-based task offloading algorithm (BTOA), where vehicles make computation and communication decisions based on beliefs while observing the resources and channel conditions in the current VEC environment. While prior research has predominantly focused on addressing computation offloading and resource allocation problems in vehicular edge environments with utility-, latency-, and cost-related objectives in mind, these strategies often overlook the potential impact of future traffic patterns within the context of vehicular networks, which can result in a loss of offloading precision. Moving forward, our discussion shifts to traffic flow prediction in vehicular environments and the accompanying investigation into computation offloading and content caching issues based on this groundwork. In the environment of the Internet of Vehicles, the key to effective resource deployment is real time, accuracy, and intelligence. In order to achieve this goal, traffic flow prediction becomes a crucial factor.