# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 4 Research Methodology  
As mentioned in the introduction, this paper aims to answer the following research questions: What performance-related metrics drive the task scheduling decisions in fog computing? What techniques are used to solve the task scheduling problem in fog computing? and What are the open challenges of task scheduling in fog computing? In undertaking our research, we employed a Systematic Mapping Study (SMS) methodology with the primary objective of systematically organizing and categorizing literature spanning the last five years in fog computing [59; 60]. Our literature search commenced by identifying seminal works in the field, serving as the foundational starting point for our study. From this initial stage, we systematically traced forward citations using the snowballing technique, a key component of the SMS methodology [61]. At each stage of the mapping process, we meticulously considered various dimensions, encompassing decision-making criteria and techniques utilized in addressing the task scheduling problem within fog computing. The SMS methodology facilitated the systematic categorization and visualization of the literature, offering a comprehensive overview of the research landscape [59]. The resulting taxonomy of task scheduling algorithms and the identification of research gaps and challenges stand as direct outcomes of our SMS methodology. This approach ensured a rigorous and structured analysis of the literature, providing valuable insights into the evolving domain of fog computing task scheduling. The search string used to gather papers is (\"task scheduling\" OR \"job scheduling\") AND (\"fog computing\" OR \"edge computing\") AND (\"IoT\" OR \"Internet of Things\") AND (\"challenges\" OR \"issues\" OR \"solutions\"). We used the Boolean operators \"OR\" and \"AND\" to refine the results we obtained. The use of \"AND\" limited the search results to articles containing both keywords, while \"OR\" expanded the search scope to include articles containing either. Our literature search, conducted from 2018 to October 2023, employed a targeted search string, applied to major Digital Libraries (DLs). The initial search returned a substantial pool of papers totaling 200. To ensure a robust dataset, we implemented a systematic deduplication process. Following this meticulous process, we arrived at a final set of 71 papers for detailed analysis. The temporal distribution of these articles is presented in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Application’s Constraints** & **Use in Literature** \\ \hline Response time & [3; 11; 19; 20; 24; 25; 26; 27] \\ Completion time & [3; 4; 28; 29; 30; 31] \\ Execution time & [21; 32; 33; 34; 35; 36; 37; 38] \\ Processing time & [30] \\ Delay & [5; 8; 10; 11; 15; 18;  
Our screening process is structured into two distinctive stages to ensure a meticulous and unbiased selection of studies [62]:  
1. Initial Screening: Deduplication: rigorous deduplication is conducted to eliminate duplicate records from the database, ensuring a clean dataset. Title and abstract examination: The initial screening phase involves a detailed examination of titles and abstracts. This preliminary assessment is essential to minimize biases, including potential publication bias. 2. Secondary Screening: Full study screening and critical appraisal: The second phase comprises a comprehensive evaluation of the full texts of selected studies. We meticulously followed predefined inclusion and exclusion criteria throughout the screening process, as detailed in Table 2. These criteria served as a robust framework for assessing the relevance and quality of each identified study. Figure 5 highlights that a significant portion of our final set of papers has been published in IEEE journals, indicating both the credibility and relevance of our selected studies. We employed a two-fold approach to comprehensively survey the literature on task scheduling in fog computing, integrating the snowballing technique and the Quasi-Gold Standard [63].