Delay-Aware SFC Provisioning in Hybrid Fog-Cloud Computing Architectures

Abstract

Network function virtualization (NFV) technology enables service providers to implement software-based network processing functionalities on standard computing servers (nodes). As such, this approach mandates the need for mapping virtual network functions (VNFs) in service function chains (SFCs) on these nodes for incoming service requests. Now traditional VNF mapping schemes use cloud nodes for its abundant available resources, at the detriment of prolonged network delays. Alternative schemes use fog nodes that return reduced delays, at the detriment of limited resources. Hence in this work, a novel SFC provisioning scheme is proposed for a hybrid fog-cloud architecture of various resources. The architecture is composed of a single fog and single cloud layer, in order to accommodate both delay-sensitive and delay-tolerant requirements for large number of incoming requests, respectively. This scheme yields a tradeoff between standalone cloud and fog solutions when implemented on the proposed hybrid architecture, in terms of the number of satisfied requests, network delay, resources consumption, energy consumption, and realization cost at large traffic volumes. The proposed scheme achieves 15-40% higher traffic capacity than fog solutions, 21-43% reduced delay, 45-52% less energy consumption levels and 28-30% less cost as compared to cloud solutions.INDEX TERMS Cloud computing, fog computing, hybrid fog-cloud architecture, network function virtualization, service function chain

Delay Optimal Schemes for Internet of Things Applications in Heterogeneous Edge Cloud Computing Networks

“…All coarse-grained workloads are scheduled under their maximum threshold level to avoid any violence of users. The authors in [15][16][17][18][19] suggested that frameworks and architectures solve deadline-aware offloading and scheduling problems for IoT applications. They formulated problems as integer linear programming where all objective functions and constraints are linear and integer variables.…”

“…To obtain the complexity of the algorithms, the lightweight and linear search-based iterative model was devised by studies. The studies [13][14][15][16][17][18][19] devised different optimization heuristics based on Bundle branch-bound, Subgradient linear search, Interior-Points, and Cutting-plane Hungarian with different types of searching methods. These methods search for the best resource among edge/fog cloud nodes before allocating tasks to them.…”

“…The aim is to reduce device energy consumption and accelerate application performance on resource-constrained devices. Furthermore, the authors [15][16][17][18] addressed the issue of task scheduling in the MEC. The goal is to schedule edge cloud resources to improve application performance and decrease the energy consumption of cloud nodes.…”

“…Existing task scheduling problems generally involve soft deadlines in the homogeneous edge only or core cloud-only resources [15][16][17][18][19][20][21][22][23]. Nonetheless, the frameworks proposed in those research works consider mutually heterogeneous fog cloud resources for IoT applications.…”

Trajectory Design for UAV-Based Data Collection Using Clustering Model in Smart Farming

“…Data Execution —The execution delay of the system calculated as [ 43 ]: where , , , and are the processing, queuing, transmission, and propagation delays of workloads in list , respectively. These delays are calculated as: where is traffic load and is the processing rate of a fog node.…”