Special issue: Elastic computing from edge to the cloud environments

Abstract

We are pleased to present a special issue that focuses on state-of-the-art research on Elastic Computing from Edge to the Cloud Environments. Today, a huge amount of data is being generated by the Internet of Things (IoT) devices such as smartphones, sensors, cameras, cars, and robots.1 In order to process the generated data, there exist Big Data platforms (such as Hadoop and Spark). Conventionally, they are deployed in centralized Data Centers, which, however, fall short of addressing time-critical requirements of the applications due to high latency between the Edge, where the data are generated and the Data Centers where they are processed.2 The emerging Edge and Fog computing paradigms promise to solve this problem by seamlessly integrating hardware and software resources across multiple computing tiers, from the Edge to the Data Center/Cloud. Since computing resources at the Edge may be power and capacity constrained, it is necessary to invent new lightweight platforms and techniques that seamlessly interact, sense, execute and produce results with very low latency, while at the same time address other high-level requirements of applications, such as security and privacy. Regarding these problems, there are many challenges that must be addressed with the invention of new architectures, methods, algorithms, and solutions. This special issue features six papers covering a range of topics including IoT application deployment frameworks in edge-cloud environments, cost optimization models, and lightweight virtualization model. The first paper in this special issue titled “Edge-adaptable serverless acceleration for machine learning Internet of Things applications”3 presents STOIC (serverless teleoperable hybrid cloud), an IoT application deployment and offloading system that extends the serverless model. The authors have developed a dynamic feedback control mechanism to precisely predict latency and dispatch workloads uniformly across edge and cloud systems using a distributed serverless framework. STOIC leverages hardware acceleration (e.g., GPU resources) for serverless function execution when available from the underlying cloud system. Finally, it configures the system to overcome deployment variability associated with public clouds. The system is evaluated using real-world machine learning applications and multitier IoT deployments (edge and cloud) and shown that it reduces overall execution time and achieves placement accuracy in the range of 92%–97%. The second paper in this special issue titled “Server Configuration Optimisation in Mobile Edge Computing: A Cost-Performance Tradeoff Perspective”4 studies the problem of server configuration optimization in Mobile edge computing environments. The authors use M/M/m queuing models and establish the performance and cost models for the system. The article considers cost-constrained performance optimization, and performance constrained cost optimization based on multiple numerical algorithms. The numerical simulation-based experiments have shown this approach is able to balance the trade-off between investment cost and service quality. The third paper in this special issue titled “EFFORT: Energy-efficient framework for offload. Communication in mobile cloud computing”5 proposes a task offloading mechanism from mobile to the remote cloud. The author's solution aims to solve the energy consumption of communication-intensive applications from mobile devices such as smartphones. The experimental evaluation is done by implementing a demonstration application in Android mobile OS. The results have shown that the proposed solution reduces the energy consumption of smartphones while executing applications and simultaneously reducing the communication cost. The fourth paper in this special issue titled “Human Microservices: A framework for turning humans into service providers”6 presents a framework facilitating the deployment of Application Programming Interface on companion devices (s