Recently, fog computing has been introduced as a modern distributed paradigm and complement to cloud computing to provide services. The fog system extends storing and computing to the edge of the network, which can remarkably solve the problem of service computing in delay-sensitive applications besides enabling location awareness and mobility support. Load balancing is an important aspect of fog networks that avoids a situation with some under-loaded or overloaded fog nodes. Quality of service parameters such as resource utilization, throughput, cost, response time, performance, and energy consumption can be improved by load balancing. In recent years, some research in load balancing algorithms in fog networks has been carried out, but there is no systematic study to consolidate these works. This article investigates the load-balancing algorithms systematically in fog computing in four classifications, including approximate, exact, fundamental, and hybrid algorithms. Also, this article investigates load balancing metrics with all advantages and disadvantages related to chosen load balancing algorithms in fog networks. The evaluation techniques and tools applied for each reviewed study are explored as well. Additionally, the essential open challenges and future trends of these algorithms are discussed. 

Load Balancing Algorithms in Fog Computing

The proliferation of ubiquitous Internet of Things (IoT) sensors and smart devices in several domains embracing healthcare, Industry 4.0, transportation and agriculture are giving rise to a prodigious amount of data requiring ever-increasing computations and services from cloud to the edge of the network. Fog/Edge computing is a promising and distributed computing paradigm that has drawn extensive attention from both industry and academia. The infrastructural efficiency of these computing paradigms necessitates adaptive resource management mechanisms for offloading decisions and efficient scheduling. Resource Management (RM) is a non-trivial issue whose complexity is the result of heterogeneous resources, incoming transactional workload, edge node discovery, and Quality of Service (QoS) parameters at the same time, which makes the efficacy of resources even more challenging. Hence, the researchers have adopted Artificial Intelligence (AI)-based techniques to resolve the above-mentioned issues. This paper offers a comprehensive review of resource management issues and challenges in Fog/Edge paradigm by categorizing them into provisioning of computing resources, task offloading, resource scheduling, service placement, and load balancing. In addition, existing AI and non-AI based state-of-the-art solutions have been discussed, along with their QoS metrics, datasets analysed, limitations and challenges. The survey provides mathematical formulation corresponding to each categorized resource management issue. Our work sheds light on promising research directions on cutting-edge technologies such as Serverless computing, 5G, Industrial IoT (IIoT), blockchain, digital twins, quantum computing, and Software-Defined Networking (SDN), which can be integrated with the existing frameworks of fog/edge-of-things paradigms to improve business intelligence and analytics amongst IoT-based applications.

AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges and Future Perspectives

The Fog computing paradigm utilises distributed, heterogeneous and resource-constrained devices at the edge of the network for efficient deployment of latency-critical and bandwidth-hungry IoT application services. Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up with the rapid development and deployment needs of fast-evolving IoT applications. Due to the fine-grained modularity of the microservices and their independently deployable and scalable nature, MSA exhibits great potential in harnessing Fog and Cloud resources, thus giving rise to novel paradigms like Osmotic computing. The loosely coupled nature of the microservices, aided by the container orchestrators and service mesh technologies, enables the dynamic composition of distributed and scalable microservices to achieve diverse performance requirements of the IoT applications using distributed Fog resources. To this end, efficient placement of microservice plays a vital role, and scalable placement algorithms are required to utilise the said characteristics of the MSA while overcoming novel challenges introduced by the architecture. Thus, we present a comprehensive taxonomy of recent literature on microservices-based IoT applications placement within Fog computing environments. Furthermore, we organise multiple taxonomies to capture the main aspects of the placement problem, analyse and classify related works, identify research gaps within each category, and discuss future research directions.

https://dl.acm.org/doi/pdf/10.1145/3592598

Placement of Microservices-based IoT Applications in Fog Computing: A Taxonomy and Future Directions

Fog computing is the new technology era, which is deployed as a middle layer computing system between Internet of Things (IoT) devices and cloud computing systems, where data are acquired and analyzed at the border of the system. Cloud computing offers many advantages, and drawbacks of network congestions due to the huge amount of information coming from various sources, which causes higher latency for immediate responsive devices. To conquer these problems fog computing provides solutions as they are deployed near the edge of end users. The load examination concern arises in fog computing when a great amount of new IoT user applications are connected to the fog nodes. To efficiently handle load balancing, a particle swarm optimization-based Enhanced Dynamic Resource Allocation Method (EDRAM) has been proposed which in turn reduces task waiting time, latency and network bandwidth consumption and improves the Quality of Experience (QoE). The Enhanced Dynamic Resource Allocation Method (EDRAM), which in turns helps for allocating the required resource by removing the long-time inactive, unreferenced and sleepy services from the Random-Access Memory.

Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

A QoS-Aware IoT Service Placement Mechanism in Fog Computing Based on Open-Source Development Model

The Internet of Things (IoT) devices are characterized by the less storage capacity and limited computational power. This calls for a great amount of cloud services to provide the required support to these smart devices. However such cloud services performance is restrained by the performance offered by the respective servers in Data Centers, which host the cloud services. Such Data Centers are usually located at a nearby proximity to the core networks, which are at large distances from the edge or field locations. This results in end to end higher latency. This problem is further compounded by the big amount of data that comes from IoT devices, which are required to be examined in real-time. However, in real-life cases such as health condition monitoring, urgently responsive and other latency sensitive applications, the latency caused by the transfer of the data to cloud server and getting it analyzed to reach conclusions, which have to be sent back to the edge is very large and therefore not realistic or acceptable. So, a new paradigm known as Fog Computing is emerging which seamlessly integrates edge devices on one hand with cloud resources on the other. In large traffic situations load balancing will become essential. Application Migration across Fog Servers may also become warranted, either for load balancing or due to physical movement of IoT devices. In this paper we provide a survey of techniques of service migration, load optimization and load balancing in the Fog eco system.

Survey on Service Migration, load optimization and Load Balancing in Fog Computing Environment

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Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method

Survey on Service Migration, load optimization and Load Balancing in Fog Computing Environment