Edge computing (EC) has emerged as a paradigm aimed at reducing data transmission latency by bringing computing resources closer to users. However, the limited scale and constrained processing power of EC pose challenges in matching the resource availability of larger cloud networks. Load balancing (LB) algorithms play a crucial role in distributing workload among edge servers and minimizing user latency. This paper presents a novel set of distributed LB algorithms that leverage machine learning techniques to overcome the three limitations of our previous LB algorithm, EVBLB : (i) its reliance on static time intervals for execution, (ii) the need for comprehensive information about all server resources and queued requests for neighbor selection, and (iii) the use of a central coordinator to dispatch incoming user requests over edge servers. To offer increased control, custom configuration, and scalability for LB on edge servers, we propose three efficient algorithms: Q-learning (QL), multi-armed bandit (MAB), and gradient bandit (GB) algorithms. The QL algorithm predicts the subsequent execution time of the EVBLB algorithm by incorporating rewards obtained from previous executions, thereby improving performance across various metrics. The MAB and GB algorithms prioritize near-optimal neighbor node servers while considering dynamic changes in request rate, request size, and edge server resources. Through simulations, we evaluate and compare the algorithms in terms of network throughput, average user response time , and a novel LB metric for workload distribution across edge servers.

Edge computing (EC)is a promising solution to enable the next-generation delay-critical network services which are not conceivable in the traditional cloud-based architecture. EC takes the computing and storage resources closer to the end-users at the edge of the networks to eliminate the propagation delays caused by geographical distances. However, due to the lack of facilities such as cooling systems, the capacity of available resources in the edge is far less than that in the remote clouds. So, efficient utilization of the edge resources has a profound impact on the effectiveness of the edge computing paradigm. Load balancing is a key factor in achieving resource efficiency and high utilization. In this paper, we present the design of EVBLB, an efficient load balancing algorithm based on Voronoi tessellation (VT) that assigns the users' service requests to the edge servers while considering the density of edge resources in the area and the distance of the users from the assigned servers. Building on the notion of VT not only allows us to achieve these goals, but is also computable in linear time, which significantly improves the scalability and responsiveness of our proposed method as compared to existing studies. Our simulation results show that EVBLB outperforms two conventional baselines in terms of throughput, response time, task completion time, and request blocking rate.