asting status message from one node to its neighbors. Both centralized and decentralized orchestrators use a decision-making approach to reach the best solution to the multi-objective resource allocation problem. Optimization techniques use algorithms that minimize a mathematical function that represents the given problem. Optimization techniques used for resource allocation include genetic algorithms [19-21], greedy algorithms [18], and the iterative search algorithm [22]. In game-theoretic methods, the problem is modeled as a game where each user device or MEC server is a player, and the goal is to reach a Nash equilib- rium state. Nash equilibrium is a state where each player cannot decrease their over- head by changing their chosen strategy. To reach the Nash equilibrium, each player considers the equilibrium offloading strategy of other players and tries to choose an offloading strategy that decreases the computation and delay overhead. The conflict graphs used in [21] is an example of using graph theory in resource allocation. In this approach, nodes represent possible assignments for each task, and edges represent conflicts between assignments. Each node holds the cost of the assignment, and a minimum weighted vertex search algorithm is used to find the best assignment that has no conflicts. Deep reinforcement learning (DRL) was proposed as a resource allo- cation technique by Luong et al. [23] because it can provide autonomous decision- making and considers long-term reward. Most DRL techniques use deep Q networks [24, 25] or their variant, the double-Q [26]. All methods consider one or more of the following factors to decide the best assignment: 1. Task delay on MEC versus cloud (includes the processing delay plus the propagation delay) Energy consumed by assigning task on MEC versus cloud (includes the pro- cessing energy consumption+ transmission energy consumption) 3. Monetary cost of assigning task on MEC versus cloud. Minimizing these factors is constrained by the maximum delay allowed for each task