rel="nofollow" href="http://www.cloudbus.org/cloudsim/">http://www.cloudbus.org/cloudsim], the ONE simulator [https://www.netlab.tkk.fi/tutkimus/dtn/theone], and iFogSim [https://github.com/Cloudslab/iFogSim], etc.) to validate their system designs. However, existing simulation tools are insufficient to validate many MFC systems because they are unable to address all the elements of MFC. For example, although the cloud service–based simulation tools are capable of simulating the hardware heterogeneity, they do not include mobility-related factors. For another example, while the mobile service-based simulation tools are capable of simulating the movement of entities, they do not have corresponding mechanisms to simulate the complete MFC network that contains the hierarchical and the vertical interconnection among the entities. Finally, iFogSim is capable of simulating the stationary fog nodes but it does not provide the mechanism to simulate the mobile fog nodes. Consequently, integrating the existing tools to develop a comprehensive MFC testbed becomes a critical challenge.
1.6.5.2 Autonomous Runtime Adjustment and Rapid Redeployment
To achieve optimal operation in MFC, the system demands autonomous adjustment and rapid redeployment based on context-awareness and the real-time system process analysis. In particular, considering an MFC system with the large-scale deployment of mFog and iFog nodes, manned optimization becomes impractical and inefficient. In order to overcome such an issue, the system needs to introduce a certain level of self-aware mechanisms to the fog nodes. Specifically, at an early stage, the system manager can preconfigure the basic knowledge to the fog nodes that help the fog nodes to identify the situation at runtime and to adjust or to redeploy the fog service. While the system continuously operates, the fog nodes should support edge intelligence mechanism in which the fog nodes together with the back-end cloud can study the historical records of the operation in order to identify the weak parts and to perform adjustment and redeployment automatically. For example, by enabling edge intelligence on the UAV-Fog nodes, the UAV-Fog nodes are capable of learning when and where to adjust their location, when and where they should migrate or redeploy their services, or when they should reserve their computational resources in order to provide the best QoE to the tenant-side clients.
1.6.5.3 Scheduling of Fog Applications
A few works have addressed fog application scheduling for hybrid MFC environments that consist of both iFog and mFog nodes. However, existing frameworks for mFog either designed for a specific purpose, such as for data routing in SDN [28], or for process/task distribution [20, 64], have not considered all the contextual factors (see Section 1.5.2) and the heterogeneity factors (see Section 1.5.1). Specifically, besides the movement of the entities, the tenants' application scheduling scheme should consider that the fog servers have the different computational and networking performance at different time slot due to the hardware specifications and the runtime context factors. In other words, developers need to have insight into the processing delay by considering all the factors toward proposing the optimal application scheduling scheme.
1.6.5.4 Scalable Resource Management of Fog Providers
In general, fog nodes have limited resources to serve tenants because they are fundamentally the independent network gateway devices that do not interconnect with one another in a short range like the server pools in the cloud. In other words, introducing computational scalability in MFC faces the network latency challenge. Commonly, providers of fog servers may manage multiple fog servers that are interconnected vertically within the hierarchy or are interconnected horizontally in a peer-to-peer manner. However, since the primary objective of fog servers in MFC is to serve the tenant-side clients, the distances between the fog servers are rarely within the range that is capable of achieving ultra-low latency. Therefore, the classic cloud-based scalability scheme is incompatible in MFC and hence, scalability becomes an unsolved challenge, especially for mFog environments. In order to address the challenge in scalability, the developers may consider developing a hybrid framework that integrates opportunistic computing, SDN, and context-aware software architecture toward enabling an adaptive fog service topology that can be orchestrating the fog servers in a highly dynamic manner.
1.7 Conclusion
Fog computing has appeared as a paradigm that extends Cloud computing and offers interesting and promising possibilities to overcome the limitations of the traditional environment. However, fog computing still faces a challenge with respect to mobility when the tasks come from ubiquitous mobile devices or applications in which the data sources are in constant movement. Therefore, this chapter pointins a spotlight on the development and advancement in the MFC and its related models. In this process, it reflects the areas where MFC is needed and has a very positive impact on enhancing the existing systems and opening new directions for evolving the environment, such as in the infrastructures, equipment and devices, land and marine vehicles, autonomous vehicles, etc. In addition, it investigates the communication technologies that have emerged or upgraded based on MFC with an emphasis on the added value that facilitated the process of solving some of the big challenges existing in the traditional design. Furthermore, the chapter also presents, in a comprehensive manner, the basics of nonfunctional requirement needed to achieve the basic QoS principles. Finally, it addresses the important open challenges that still need to be addressed in the new environment designed under the MFC framework.
Acknowledgment
The work is supported by the Estonian Centre of Excellence in IT (EXCITE), funded by the European Regional Development Fund.
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