Management and Optimization of Fog Architectures

Dear Colleagues,

Fog computing has emerged as an extension of the cloud, to allow low-latency demand for IoT applications and to reduce the use of the network bandwidth, by approaching the computation tasks and data management to the location of the users. In this cloud–fog continuum, in-network devices include processing and storage capabilities to allow the execution of multi-component applications. The management and optimization of these applications and resources require additional research efforts, since the already stablished solutions for the cloud are not applicable because of the important differences between fog and cloud features. Fog infrastructures stand out for the heterogeneity, reduced capacity, and geographical distribution of their devices.

The management of the distributed computing system in general, and fog computing in particular, requires optimization processes in the domains of scheduling, orchestration, resource allocation, load balancing, etc. These optimization problems, usually modelled as multi-criteria combinatorial optimization, requires the application of heuristics or metaheuristics (such as evolutionary algorithms, nature-inspired optimization, trajectory-based algorithms, local search metaheuristics, metaphor-based metaheuristics,…) or machine learning algorithms (decision trees, association rules, artificial neural networks,…).  Examples of these algorithms have resulted in suitable solutions and they provide important improvements in terms of non-functional requirements, such as, latency, resource usage, energy consumption, cost, etc.

The main aim of this Special Issue is to seek high-quality submissions that highlight novel solutions to address recent challenges in the optimization and management of fog computing. The topics of interest include, but are not limited to:

• Optimization of workflow scheduling, resource allocation, service migration, etc.
• Hybrid solutions for the optimization of fog computing
• Parallel versions of optimization algorithms for fog computing optimization
• Service orchestration techniques for auto-scaling and/or load balancing
• Service selection and provisioning for fog computing optimization
• Performance optimization of fog architectures
• Optimization of data management techniques in fog architectures
• Service design for orchestration proposes: meta information, or architectures
• Monitoring solutions for orchestrating techniques
• Simulation tools for optimization of fog computing solutions
• Billing models based on placement and other third criteria
• Optimization of DevOps tools on the fog
• Container-based optimization
• Optimization of non-functional aspects of fog computing
• Heuristics and meta-heuristics for fog optimization

Dr. Carlos Guerrero
Dr. Isaac Lera
Prof. Dr. Carlos Juiz
Guest Editors

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