## 3 Background
### Resource Management Algorithms  
Optimizing the performance and scalability of edge cloud environments requires effective resource management. Because edge environments are dynamic and heterogeneous, resource management activities, including scheduling workloads, allocating resources, and optimizing energy use, are difficult. The development and implementation of resource management algorithms are greatly impacted by the various complications that arise from optimizing energy usage and minimizing latency in edge computing scenarios. Because edge devices range in power from robust servers to low-power Internet of Things devices, scalable and adaptable algorithms that can adjust to different processing capacities and operating environments are required [14]. Additionally, resource management solutions must be flexible enough to quickly adapt in order to maintain low latency and energy efficiency due to dynamic and unpredictable workloads, which are typified by changing data rates and occasional surges. This optimization is made more difficult by the constrained computational capacity and strict energy constraints of many edge devices, which necessitate a careful balance between local processing and task offloading to more powerful nodes. A further degree of complexity is introduced by real-time processing needs, which call for cooperative processing among edge devices and latency-aware scheduling. These difficulties force the creation of complex, context-aware algorithms that take security and privacy issues into account while being able to perform dynamic adaptation, predictive analytics, and intelligent offloading. Thus, in order to efficiently balance energy consumption and latency, resource management in edge computing entails a multimodal strategy that incorporates real-time decision-making, sophisticated optimization techniques, and collaborative processing strategies [15]. In conventional edge cloud environments, resource management plays a vital role in assigning computing resources such as CPU, memory, storage, and network. The energy resource is crucial, particularly for edge computing. For instance, a smartphone user prefers not to frequently recharge their device; therefore, some sensors should not require regular charging [16]. Developers should be aware of how much power is required for a given application and how data can be delivered or withdrawn from the device, especially for edge analytics. This type of issue eventually becomes an optimization issue with scheduling and task placement. There are a few key prerequisites for edge platform management in general [2]. As shown in Table 1, the first and most important requirement is _scalability_, which calls for the capacity to manage a sizable number of edge devices with various functionalities and communication protocols [11]. _Security_ is critical, necessitating support for integrity checks to guarantee data integrity and confidentiality inside the infrastructure, as well as privacy-preserving mechanisms for security tokens. It is imperative to consider heterogeneity by providing support for diverse hardware and software configurations to facilitate smooth integration and interoperability. To preserve system performance and stability, the availability and mobility of hardware and software components must be effectively handled. _Data protection_ is essential in order to preserve privacy and secrecy; it is necessary to comply with laws like the GDPR and to make sure that all data are locally stored and encrypted instantly. For effective system upgrades and maintenance, infrastructure performance is critical. This calls for low-latency, lightweight communication protocols like _MQTT_ and high-performance containerized resources with quick provisioning capabilities. _Flexibility_ and _agility_ require application mobility [17].