# Edge Data Center Organization and Optimization by Using Cage Graphs
## 2 Motivation  
Data center organization and optimization are key aspects when it comes to boosting operation and maintenance (O&M), which is basically driven by three converging forces: powerful operations, technology, and economics [15]. The first point is focused on data availability for real-time decision making. The second point is centered on the flexibility to deal with fast-changing situations. The third point is about delivering the best performance while reducing carbon emissions. Optimizing facilities is about increasing efficiency, which produces a variety of benefits such as decreasing power consumption, which also reduces downtime; about increasing the mechanical capacity of the facility, thus improving HVAC efficiency and redundancy, prolonging equipment life span, and saving money on new equipment or unplanned maintenance; and becoming more climate-friendly, as both power consumption and HVAC needs are reduced [16]. According to the literature, there are many variables involved in achieving efficiency in data centers, such as servers, storage, networking, infrastructure, environment monitoring, cabling, backup power, cooling, and software. For example, some commonly used techniques in data centers are virtualization, containerization, server consolidation, storage unification, and enhanced cooling systems [17]. In this sense, network systems and storage capacities should be maximized by updating protocols to achieve higher bandwidth rates and capacity. As such, monitor power usage is necessary to avoid unexpected fallouts, whereas high-quality cabling guarantees high-speed rates while lowering downtime. Moreover, physical space can be reduced by consolidating servers, whereas virtualization improves the usage ratio for both storage systems and central servers, as it reduces the number of servers needed and facilitates the accommodation of dynamically changing requirements. Additionally, the use of software-defined networks (SDNs) allows moving server bandwidth away from processing units within the infrastructure, although at the expense of an additional load cost. Moreover, software optimizations help improve efficiency, which may be automatically performed by implementing AI tools. Furthermore, hyperscale networks may optimize computer-intensive tasks through software to enhance caching, data encryption, or intelligent tiering. Additionally, service-oriented architecture (SOA) may be applied to increase the number of connections between applications and systems. Nonetheless, power consumption may be seen as the greatest concern related to data center optimization. For this optimization, the applied power efficiency strategies may be classified into IT equipment, power infrastructure, HVAC systems, and airflow management [18]. The first one includes server consolidation, efficient data storage systems, and built-in server power management. The second involves the use of smart power distribution units (PDUs) and uninterruptible power supplies (UPSs). The third is the utilization of water-side or air-side economizers, using rack or in-row cooling, adjusting humidity, and employing sensors and controls to match IT loads with the level of cooling applied. The fourth involves efficient cooling airflow management, using a hot aisle and cold aisle layout and employing enclosure strategies. However, the focus on this study was networking, from the point of view of the networking interconnection among the diverse nodes being part of a data center in order to reduce the number of hops between any pair of nodes so as to optimize the time required from a given source to a particular destination. Therefore, the main contribution of this study is related to the use of cage graph topologies to interconnect nodes in an edge data centre. As such, an algorithm was designed for each of the smallest cage graphs chosen in order to easily move traffic between any given pair of nodes, as described in Sections 5 and 6.