# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 2 Related Work  
Datacenters are evolving to meet the growing demands of escalating data volumes. In general, datacenter networks require multi-path load-balancing routing to optimize bandwidth usage. Many existing network forwarding protocols are optimized to select a single path. However, the data forwarding in enterprise and datacenter environments often use equal cost multi-path (ECMP) [23] to statically distribute flows across available paths through random flow hashing. This method resulted in overwhelming switch buffers. The authors in [24] proposed a simple algorithm to manage the dataflow within a datacenter using a centralized controller called 'arbiter.' This algorithm enabled the arbiter to distribute flows over different time points and paths in a preemptive scheduling and traffic load-balancing manner. [PERSON] et al. [25] proposed pushing load balancing to the soft network edge (virtual switches), enabling load balancing without centralized traffic engineering, where the authors claimed that no changes in the transportation layer were needed. [PERSON] et al. [26] employed congestion-aware switching on specialized switch chips to balance the load; however, the technique demanded hardware modification and it lacked awareness of local switch status. [PERSON] et al. [27] developed \"LetFlow,\" an algorithm that randomly picks a path for balancing network load, but the scheme did not react to congestion. In recent years, the expansion of IoT has led to a growing number of intelligent information-aware devices, resulting in an exponential increase in generated data volume [28]. IoT data is of interest to both industry and research, making the collection and analysis of data essential for many purposes [29]. Moreover, the rise of ubiquitous computing has introduced numerous sensors and small devices, which have led to the connection of the devices to the cloud [30]. As IoT devices communicate through cloud datacenters, much of the data stored in the cloud is of little importance, leading to wastage of resources. [PERSON] et al. [31] proposed a framework for an industrial data management system, where the authors focused on retrieving and collecting data generated by factory devices using a standard communication protocol. [PERSON] et al. [32] proposed the development of geographically distributed cloud datacenters to manage the load, highlighting the need for distributed cloud datacenters instead of relying on centralized datacenters. A paradigm shift from large cloud datacenters to small datacenters led to the introduction of edge datacenters [33], designed to handle data locally, thereby decreasing the dependency on the cloud datacenters [34]. On the other hand, decentralized cloud computing helps to distribute data to several nodes as a way of forming small datacenters, which increases the system's ability to cope with node failures [35]. [PERSON] et al. [36] presented a fog computing-based secure IIoT architecture by offloading a few tasks carefully to fog nodes. The authors claimed that these features secured the system while reducing the burden on the cloud and resource-constrained devices. However, the fog/edge datacenters require scaled-down versions of traditional datacenters' infrastructure, making the implementation still expensive. Thus, it is necessary to propose a mini-datacenter that reduces both infrastructure and operational costs. However, the efficiency of data forwarding can be improved if it is incorporated with intelligence mechanisms. The Industrial Internet of Things (IIoT) is central to digitizing manufacturing processes and enhancing automation across smart factories. As automation becomes increasingly integral to industrial operations, reliability emerges as a crucial performance indicator, measuring how consistently and accurately these systems function. However, maintaining high reliability in IIoT systems remains a significant challenge due to the unpredictable and dynamic nature of industrial environments [37].