ACM Transactions on Internet Technology · 2019 · 85 citations · 27 references
ComputingCloud ComputingComputer SystemsCloud InfrastructureEdge ComputingMobile SystemsEdge Computing InfrastructureEdge InfrastructureEdge-cloud ArchitectureEdge-cloud IntegrationMobile Edge ComputingTask OffloadingBattery Consumption ModelingCloud Computing ResourcesCloud-edge ContinuumCloud-edge OffloadingEdge-cloud ContinuumEdge-to-cloud ContinuumEdge-to-cloud IntegrationService Offloading
Mobile, edge, and cloud computing can form a continuum that allows applications to dynamically execute parts of their logic on different infrastructures to reduce latency, save battery, and improve availability. The authors propose A3‑E, a unified model for managing the lifecycle of applications across the mobile‑edge‑cloud continuum. A3‑E uses a Functions‑as‑a‑Service approach to deploy microservices across the continuum, selecting execution locations based on context and user requirements, and is implemented in a prototype framework. Evaluation shows that A3‑E can dynamically deploy microservices and route requests, cutting latency by up to 90 % with edge resources and reducing battery consumption by 74 % when computation is offloaded.
Technologies such as mobile, edge, and cloud computing have the potential to form a computing continuum for new, disruptive applications. At runtime, applications can choose to execute parts of their logic on different infrastructures that constitute the continuum, with the goal of minimizing latency and battery consumption and maximizing availability. In this article, we propose A3-E, a unified model for managing the life cycle of continuum applications. In particular, A3-E exploits the Functions-as-a-Service model to bring computation to the continuum in the form of microservices. Furthermore, A3-E selects where to execute a certain function based on the specific context and user requirements. The article also presents a prototype framework that implements the concepts behind A3-E. Results show that A3-E is capable of dynamically deploying microservices and routing the application’s requests, reducing latency by up to 90% when using edge instead of cloud resources, and battery consumption by 74% when computation has been offloaded.
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