When dealing with distributed applications in Edge or Fog computing environments, the service latency that the user experiences at a given node can be considered an indicator of how much the node itself is loaded with respect to the others. Indeed, only considering the average CPU time or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters are application- and hardware-agnostic. They do not give any information about how the application is performing from the user's perspective, and they cannot be used for a QoS-oriented load balancing. In this paper, we propose a load balancing algorithm that is focused on the service latency with the objective of levelling it across all the nodes in a fully decentralised manner. In this way, no user will experience a worse QoS than the other. By providing a differential model of the system and an adaptive heuristic to find the solution to the problem in real settings, we show both in simulation and in a real-world deployment, based on a cluster of Raspberry Pi boards, that our approach is able to level the service latency among a set of heterogeneous nodes organised in different topology.

Fog computing infrastructures must support increasingly complex applications where a large number of sensors send data to intermediate fog nodes for processing. As the load in such applications (as in the case of a smart cities scenario) is subject to significant fluctuations both over time and space, load balancing is a fundamental task. In this paper we study a fully distributed algorithm for load balancing based on random probing of the neighbors' status. A qualifying point of our study is considering the impact of delay during the probe phase and analyzing the impact of stale load information. We propose a theoretical model for the loss of correlation between actual load on a node and stale information arriving to the neighbors. Furthermore, we analyze through simulation the performance of the proposed algorithm considering a wide set of parameters and comparing it with an approach from the literature based on random walks. Our analysis points out under which conditions the proposed algorithm can outperform the alternatives.

The growth of large scale sensing applications (as in the case of smart cities applications) is a main driver of the fog computing paradigm. However, as the load for such fog infrastructures increases, there is a growing need for coordination mechanisms that can provide load balancing. The problem is exacerbated by local overload that may occur due to an uneven distribution of processing tasks (jobs) over the infrastructure, which is typical real application such as smart cities, where the sensor deployment is irregular and the workload intensity can fluctuate due to rush hours and users behavior. In this paper we introduce two load sharing mechanisms that aim to offload jobs towards the neighboring nodes. We evaluate the performance of such algorithms in a realistic environment that is based on a real application for monitoring in a smart city. Our experiments demonstrate that even a simple load balancing scheme is effective in addressing local hot spots that would arise in a non-collaborative fog infrastructure.