Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks

Abstract

Recently, with the advent of the Internet of Things (IoT), Smart Cities have emerged as a potential business opportunity for most cloud service providers. However, centralized cloud architectures cannot sustain the requirements imposed by many IoT services. High mobility coverage and low latency constraints are among the strictest requirements, making centralized solutions impractical. In response, theoretical foundations of Fog Computing have been introduced to set up a distributed cloud infrastructure by placing computational resources close to end-users. However, the acceptance of its foundational concepts is still in its early stages. A key challenge still to answer is Service Function Chaining (SFC) in Fog Computing, in which services are connected in a specific order forming a service chain to fully leverage on network softwarization. Also, Low Power Wide Area Networks (LPWANs) have been getting significant attention. Opposed to traditional wireless technologies, LP-WANs are focused on low bandwidth communications over long ranges. Despite their tremendous potential, many challenges still arise concerning the deployment and management of these technologies, making their wide adoption difficult for most service providers. In this article, a Mixed Integer Linear Programming (MILP) formulation for the IoT service allocation problem is proposed, which takes SFC concepts, different LPWAN technologies and multiple optimization objectives into account. To the best of our knowledge, our work goes beyond the current state-of-the-art by providing a complete end-to-end (E2E) resource provisioning in Fog-cloud environments while considering cloud and wireless network requirements. Evaluations have been performed to evaluate in detail the proposed MILP formulation for Smart City use cases. Results show clear trade-offs between the different provisioning strategies. Our work can serve as a benchmark for resource provisioning research in Fog-cloud environments since the model approach is generic and can be applied to a wide range of IoT use cases.

Service Placement for Latency Reduction in the Fog Using Application Profiles

“…It is relevant to notice the use of mathematical programming techniques, like Linear Programming (LP), Integer Linear Programming (ILP), and Mixed Integer Linear Programming (MILP) [15], [18], [19], in the design of models to find the optimal location for services, as well as the use of mathematical models [20]- [22]. Genetic Algorithm (GA) is another technique commonly used for placement heuristics [14], [18], [23], [24].…”

“…Mostly, the metrics used are based on network aspects such as resource usage [13], [16], [18], [23], power consumption [21], [24], reliability [25], availability [26], QoS [17], [20], and timerelated metrics such as response time [24], [27], delay [21] and latency [14], [15], [22], [23], [28], [29]. In the case of metrics that concern the applications and their services, QoE, which measures the quality as experienced by the user, is explored as metric [30]; however, no element to describe the applications and the behavior of their services is used during the placement process, except one work [15] that explores the placement of applications according to their requests, but do not categorize the applications accordingly. For the validation, most works use simulation, varying the tool used, largely iFogSim [13], [18], [25], [28], [30], but also YAFS [26], FogTorch [20], Matlab [14], and Python [17], [23].…”

A Scalable Hierarchically Distributed Architecture for Next-Generation Applications

“…For instance, in the case of surveillance management, cameras capture videos from which faces have to be extracted and detected. Further, the extracted faces have to be matched/recognized and finally provided to the monitoring cell [19]. In this process, services such as face detection/extraction, matching/recognition, and storage can be deployed as a chain of VNFs.…”

“…Similarly, it should be routed through intermediate switch 11 for hosts connected to switch 12. In the case of the Internet2 OS3E topology, 14 clients have been connected to edge switches belonging to core switches (1,4,6,9,16,17,19,23,24,27,30,32). Furthermore, when hosts connected to the edge of switch 32 request content, a condition has been placed that the flow must be routed through intermediate node 20.…”

Efficient Orchestration of Service Chains in Fog Computing for Immersive Media

“…In [8], SRFog has been presented, an architecture adopting Fog Computing (FC) and Segment Routing (SR) for VR content delivery. In [9], an IoT service placement approach for FC environments has been proposed. This paper builds further on both works by presenting a Mixed-Integer Linear Programming (MILP) model for VR service orchestration in SRFog.…”