Managing Fog Networks using Reinforcement Learning Based Load Balancing Algorithm

Abstract

The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service closer to end devices. To support this distributed intelligent platform, Software-Defined Network (SDN) has emerged as a viable network technology in the Fog computing environment. However, uncertainties related to task demands and the different computing capacities of Fog nodes, inquire an effective load balancing algorithm. In this paper, the load balancing problem has been addressed under the constraint of achieving the minimum latency in Fog networks. To handle this problem, a reinforcement learning based decision-making process has been proposed to find the optimal offloading decision with unknown reward and transition functions. The proposed process allows Fog nodes to offload an optimal number of tasks among incoming tasks by selecting an available neighboring Fog node under their respective resource capabilities with the aim to minimize the processing time and the overall overloading probability. Compared with the traditional approaches, the proposed scheme not only simplifies the algorithmic framework without imposing any specific assumption on the network model but also guarantees convergence in polynomial time. The results show that, during average delays, the proposed reinforcement learning-based offloading method achieves significant performance improvements over the variation of service rate and traffic arrival rate. The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively.

Analysis of an SDN-Based Cooperative Caching Network with Heterogeneous Contents

“…In addition, ε is the key indicator that decides the value of the successful transmission rate P(SINR NCH ≥ Γ| f b i ) by Neighbor-CH-provide in formula (23) and represents which file blocks can be received successfully. As shown in Figure 6, the larger the proportion of active CHs µ, the less signal superposition and the more interference accumulates such that ε decreases and fewer blocks can be obtained successfully.…”

“…To make full use of the storage capacity of the edge network, the fog ratio access network (F-RAN) is proposed as the evolution of a heterogeneous cloud wireless access network for local content distribution [21][22][23][24], providing the possibility to integrate virtualized servers into networks and brings cloud service closer to end device. The edge caching optimization problem of F-RAN in [21] is formulated to find the optimal policy by maximizing the overall cache hit rate.…”

“…Ref. [23] proposes that uncertainties related to task demands and the different computing capacities of fog nodes inquire an effective load balancing algorithm, and the load balancing problem has been addressed by reinforcement learning under the constraint of achieving the minimum latency. In the paper [24], authors investigate a proactive probabilistic caching optimization in F-RAN and derive the analytical results of successful transmission probability to assess the performance.…”

Heterogeneous Task Offloading and Resource Allocations via Deep Recurrent Reinforcement Learning in Partial Observable Multifog Networks

“…to ensure that the fog node cannot offload the task when it doesn't arrive by (12), cannot allocate more than the number of tasks waiting for allocation by (13), and the sum of newly allocated resources cannot exceed the available resources by (14) and (15). Given that each node is in state O i (t) and action X i (t) is chosen, a transition probability is given by (16), where X(t) = (X i (t) : i ∈ I) are the set of actions occurring at time t. From (16), B e i (t + 1) and R i (t + 1) only depend on the action X i (t) of fog node i, while A i (t + 1) is determined regardless of the action.…”

Multivariate Analysis of COVID-19 on Stock, Commodity & Purchase Manager Indices: A Global Perspective

“…Negative impact on socio-economic activities results due to direct and indirect damages wherein indirect damages are attributed to fear-driven behavioral changes in the public. Authors in [24] use a technique to understand the evolution of COVID-19 by exploring seven scenarios. The scenario presented here claims that even a controlled outbreak hugely impacts the global economy in minimum time.…”