## 3 Background
### Resource Management Algorithms
Finally, in order to effectively handle and analyze the data produced by edge devices in order to glean insightful information and facilitate decision-making processes, support for data management and analytics pipelines is essential [18]. In order to guarantee the resource management algorithms' applicability and efficacy in edge cloud systems, a set of criteria was used in their selection. Initially, the algorithms were selected based on their proven [19, 20, 21] capacity to maximize important performance indicators like CPU usage, memory usage, disk usage, and power consumption. Secondly, all algorithms had to demonstrate flexibility in handling the varied and ever-changing workload situations that are characteristic of edge computing. Third, to ensure that the algorithms are applicable to edge deployments in the real world, we gave priority to those that have demonstrated scalability across distributed edge infrastructures. Finally, to improve latency reduction and the user experience overall, the algorithms chosen have to take into account network conditions and geographic closeness. We specifically look into four resource management algorithms: user-location, power-saving, worst-fit, and best-fit algorithms. These scheduling algorithms, as shown in Table 2, were chosen to offer a thorough grasp of resource allocation and optimization in edge cloud settings. \begin{table}
\begin{tabular}{c c} \hline \hline
**Requirement** & **Description** \\ \hline Scalability & Ability to address a large number of edge devices of different types and capabilities with appropriate deployment and communicating protocols \\ \hline Security & Privacy preservation for security tokens and support for integrity checks within the infrastructure \\ \hline Heterogeneity & Support for a high degree of heterogeneity within hardware/software \\ \hline Volatility & Support for volatile availability and mobile hardware/software components \\ \hline Data Protection & GDPR compliance, ensuring all data are kept locally and on-the-fly encrypted \\ \hline Infrastructure & Very-low-latency, lightweight publish-subscribe network protocol such as MQTT. High-performance containerized resources with fast (zero-touch) provisioning allowing easy system upgrades \\ \hline Application & Unified architecture view via MEC compliance enabling Function as a Service (FaaS) capabilities \\ \hline Data Analytics & Supports for data management and data analytics pipeline engine \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General requirement for edge device management and orchestration [2]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Description** \\ \hline Best-fit algorithm [19] & Allocates tasks to the edge server with the closest-matching \\  & available resources, minimizing resource wastage and \\  & maximizing efficiency \\ \hline Worst-fit Algorithm [19] & Assigns incoming tasks to the edge server with the greatest \\  & available excess capacity, aiming to minimize resource \\  & fragmentation and maximize system stability, even if it results \\  & in suboptimal resource utilization \\ \hline Power-saving algorithm [20] & Designed to reduce energy consumption and promote \\  & sustainability in edge cloud environments by assigning \\  & workload to servers with the lowest power consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Brief description of the algorithms we selected. More specifics on each algorithm:  
* By choosing the server with the closest resource capacity match to incoming tasks or the smallest memory block that is available, the best-fit [19] scheduling method maximizes resource allocation. By reducing resource fragmentation and waste, this tactic improves system performance and efficiency. In an edge cloud environment, the best-fit method enhances overall system throughput and responsiveness by giving resources a higher priority depending on suitability.