Mobile devices are becoming ubiquitous in our daily lives, but they have limited computational capacity. Thanks to the advancement in the network infrastructure, task offloading from resource-constrained devices to the near edge and the cloud becomes possible and advantageous. Complete task offloading is now possible to almost limitless computing resources of public cloud platforms. Generally, the edge computing resources support latency-sensitive applications with limited computing resources, while the cloud supports latency-tolerant applications. This paper proposes one lightweight task-scheduling framework from cloud service provider perspective, for applications using both cloud and edge platforms. Here, the challenge is using edge and cloud resources efficiently when necessary. Such decisions have to be made quickly, with a small management overhead. Our framework aims at solving two research questions. They are: i) How to distribute tasks to the edge resource pools and multi-clouds? ii) How to manage these resource pools effectively with low overheads? To answer these two questions, we examine the performance of our proposed framework based on Reliable Server Pooling (RSerPool). We have shown via simulations that RSerPool, with the correct usage and configuration of pool member selection policies, can accomplish the cloud/edge setup resource selection task with a small overhead.

Cloud computing makes complex computing an off-premise activity by offering software- and hardware-based services using standard security protocols over the Internet. It has been seen that the cloud is not ideal for latency-sensitive applications. Thanks to the current growth of network communication and infrastructure, fog adds a computing resource delegation layer between the user and the cloud. Fog aims to improve latency-sensitive applications support. Here, we propose one unified, proactive resource orchestration framework from a cloud/fog service provider perspective. The framework consists of a predictor and a resource allocator module. Users subscribe to these resources to execute their applications. The framework does not require application-specific information and is modular, meaning a service provider can customise each module. We have presented the framework prototype by showing each module's simulated performance results using the parameters of our cloud/fog research test bed.