# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 6 Literature Mapping  
### Optimization Criteria  
Table 3 maps the studied literature with the optimization criteria described in Section 5.1 and is used to make scheduling decisions. The metrics authors consider more often are energy consumption, latency, and cost. Less frequently, research work use response time, execution time, completion time, and availability. Task scheduling requires different decision-making metrics depending on workload, resource availability, deadlines, and specific optimization goals. The choice of appropriate decision-making metrics for task scheduling also hinges on the nature of the computing environment, the type of tasks being scheduled, and the overall system architecture. Moreover, considerations like real-time requirements, task dependencies, and the dynamic nature of workloads play a crucial role in determining the most effective decision metrics for optimal task scheduling. Three evaluation metrics (cost, energy, latency) are considered in [15], focusing on minimizing average End-to-End (E2E) service delay, managing computation costs within resource and deadline constraints, and enhancing energy efficiency during periods of heavy task loads[PERSON] The following metrics have been considered in [20; 37; 41; 49; 50] as well[PERSON]  
The authors [3; 20] have extensively examined decision metrics. [PERSON] et al. [3] present a methodology to minimize energy consumption, response time, cost, and completion time within Edge-Cloud environments. [PERSON] et al. [20] contribute significantly to comprehending essential criteria by accentuating overall latency, energy consumption, response time, and operational cost reduction. The authors of [5; 9; 16; 17; 19; 21; 25; 27; 34; 40; 43; 45; 48; 52; 53; 54; 55; 57; 58; 68; 71; 74; 75] have explored the only the effect of one performance on scheduling decisions. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**References** & **Cost** & **Energy** & **Latency** & **Exec. Time** & **Resp. Time** & **Completion Time** & **Availability** \\ \hline [15; 37; 41; 49; 50] & ✓ & ✓ & ✓ & & & & \\ \hline [5; 9; 16; 17; 45; 54; 52; 53; 54; 52; 53; 54] & & ✓ & & & & \\ \hline [32; 33; 71] & ✓ & & & & & \\ \hline [10; 39; 42; 44; 46] & & ✓ & ✓ & & & \\ \hline [40] & ✓ & & & & & \\ \hline [3] & ✓ & ✓ & & & ✓ & ✓ \\ \hline [4; 18; 28] & & ✓ & & & & ✓ \\ \hline [21; 34; 68] & & & & ✓ & & \\ \hline [51] & & ✓ & & ✓ & & \\ \hline [19; 25; 27] & & & & & ✓ & \\ \hline [35; 38; 72] & ✓ & & & ✓ & & \\ \hline [36; 73] & ✓ & ✓ & & ✓ & & \\ \hline [43; 74; 75] & & & & & & ✓ \\ \hline [55; 57; 58] & & & ✓ & & & \\ \hline [20] & ✓ & ✓ & ✓ & & ✓ & \\ \hline [24; 29] & ✓ & ✓ & & & ✓ & \\ \hline [56] & ✓ & ✓ & & & & \\ \hline [47] & ✓ & & & ✓ & & \\ \hline [11] & & & ✓ & & ✓ & \\ \hline [30] & & & & ✓ & & ✓ \\ \hline [31] & & & & ✓ & & ✓ \\ \hline [26] & & ✓ & & & ✓ & \\ \hline [64] & ✓ & & ✓ & & & \\ \hline [6] & & & & & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Decision-making features. The significance of incorporating energy consumption and latency metrics into the decision-making process is demonstrated in [10; 39; 42; 44; 46]. [PERSON] et al. [39] utilized energy consumption and delay metrics within homogeneous fog networks. They considered the energy expended during task execution and the power consumed during task scheduling. Additionally, they established that the average traffic delay for each mobile fog node is directly linked to the average number of unexecuted computation tasks. Other authors offer innovative strategies for fog computing task scheduling, utilizing fuzzy reinforcement learning, centralized DDQN algorithms, and efficient schedulers to reduce energy consumption and latency. The emphasis is on enhancing efficiency in dynamic and resource-constrained environments, with a focus on optimizing performance metrics for IoT devices. Execution time and cost metrics have been investigated together by [32; 33; 35; 36; 38; 72]. [PERSON] et al.