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Empowered by computing resources at the network edge, data sensed from Internet of Things (IoT) devices can be processed and stored in their nearby cloudlets to reduce the traffic load in the core network, while various IoT applications can be run in cloudlets to reduce the response time between IoT users (e.g., user equipment in mobile networks) and cloudlets. Considering the spatial and temporal dynamics of each application's workloads among cloudlets, the workload allocation among cloudlets for each IoT application affects the response time of the application's requests. While assigning IoT users' requests to their nearby cloudlets can minimize the network delay, the computing delay of a type of requests may be unbearable if the corresponding virtual machine of the application in a cloudlet is overloaded. To solve this problem, we design an application aware workload allocation scheme for edge computing-based IoT to minimize the response time of IoT application requests by deciding the destination cloudlets for each IoT user's different types of requests and the amount of computing resources allocated for each application in each cloudlet. In this scheme, both the network delay and computing delay are taken into account, i.e., IoT users' requests are more likely assigned to closer and lightly loaded cloudlets. Meanwhile, the scheme will dynamically adjust computing resources of different applications in each cloudlet based on their workloads, thus reducing the computing delay of all requests in the cloudlet. The performance of the proposed scheme has been validated by extensive simulations.
Fan et al. (Thu,) studied this question.