Fog computing is an intermediate infrastructure between edge devices (e.g., Internet of Things) and cloud systems that is used to reduce latency in real‐time applications. An application can be composed of a collection of virtual functions, between which dependency constraints can be captured in a service function chain (SFC). Virtual functions within an SFC can be executed at different geo‐distributed locations. However, virtual functions are prone to failure and often do not complete within a deadline. This results in function reallocation to other nodes within the infrastructure; causing delays, potential data loss during function migration, and increased costs. We proposed Greedy Nominator Heuristic (GNH) to address these issues. GNH is based on redundant deployment and failure tracking of virtual functions. GNH places replicas of each function at multiple locations—taking account of expected completion time, failure risk, and cost. We make use of a MapReduce‐based mechanism, where Mappers find suitable locations in parallel, and a Reducer then ranks these locations. Our results show that GNH reduces latency by up to 68%, and is more cost effective than other approaches which rely on state‐of‐the‐art optimization algorithms to allocate replicas.

No abstract

No abstract

Cloud computing provides on-demand access to computational resources while outsourcing infrastructure and service maintenance. Edge computing could extend cloud computing capability to areas with limited computing resources, such as rural areas, by utilizing low-cost hardware, such as singleboard computers. Cloud data centre hosted machine learning algorithms may violate user privacy and data confidentiality requirements. Federated learning (FL) trains models without sending data to a central server and ensures data privacy. Using FL, multiple actors can collaborate on a single machine learning model without sharing data. However, rural network outages can happen at any time, and the quality of a wireless network varies depending on location, which can affect the performance of the Federated Learning application. Therefore there is a need to have a platform that maintains service quality independent of infrastructure status. We propose a self-adaptive system for rural FL, which employs the Greedy Nominator Heuristic (GNH) based optimisation to orchestrate application workflows across multiple resources that make up a rural computing environment. GNH provides distributed optimization for workflow placement. GNH utilises resource status to reduce failure risks and costs while still completing tasks on time. Our approach is validated using a simulated rural environment -composed of multiple decentralized controllers sharing the same infrastructure and running a shared FL application. Results show that GNH outperforms three algorithms for deployment of FL tasks: random placement, round-robin load balancer and simple greedy algorithm.