Abstract
Fog computing has recently emerged as an extension of cloud computing in providing high-performance computing services for delay-sensitive Internet of Things (IoT) applications. By offloading tasks to a geographically proximal fog computing server instead of a remote cloud, the delay performance can be greatly improved. However, some IoT applications may still experience considerable delays, including queuing and computation delays, when huge amounts of tasks instantaneously feed into a resource-limited fog node. Accordingly, the cooperation among geographically close fog nodes and the cloud center is desired in fog computing with the ever-increasing computational demands from IoT applications. This paper investigates a workload allocation scheme in an IoT–fog–cloud cooperation system for reducing task service delay, aiming at satisfying as many as possible delay-sensitive IoT applications’ quality of service (QoS) requirements. To this end, we first formulate the workload allocation problem in an IoT-edge-cloud cooperation system, which suggests optimal workload allocation among local fog node, neighboring fog node, and the cloud center to minimize task service delay. Then, the stability of the IoT-fog-cloud queueing system is theoretically analyzed with Lyapunov drift plus penalty theory. Based on the analytical results, we propose a delay-aware online workload allocation and scheduling (DAOWA) algorithm to achieve the goal of reducing long-term average task serve delay. Theoretical analysis and simulations have been conducted to demonstrate the efficiency of the proposal in task serve delay reduction and IoT-fog-cloud queueing system stability.
Highlights
The increasing number of Internet of Things (IoT) applications, such as audio recognition, vehicle-to-roadside communications, and virtual reality, often demand a low end-to-end latency between a sensor and a control center [1,2]
Our goal is to find an optimal workload allocation scheme to allocate workload among the local fog node, neighboring fog nodes, or the cloud center according to the system states, which is formulated as a delay-aware workload allocation problem with the goal of minimizing the task service delay (TSD)
We evaluated the efficiency of the proposed delay-aware online workload allocation and scheduling (DAOWA) algorithm by comparing with other algorithms, including fog-processing algorithm (Fog), cloud processing algorithm (Cloud), fog-to-cloud cooperation
Summary
The increasing number of Internet of Things (IoT) applications, such as audio recognition, vehicle-to-roadside communications, and virtual reality, often demand a low end-to-end latency between a sensor and a control center [1,2]. Workload allocation among fog nodes and the cloud center is a key technique that affects the TSD in QoS provisioning [11] It determines where a task is serviced in the fog system, and affects both the queueing delay and network delay. Owing to the heterogeneous resources of fog nodes and the cloud center, there exists a tradeoff between the queuing delay and the network delay, which complicates workload allocation for tasks. A delay-based workload allocation problem is formulated, which suggests the optimal workload allocations among local fog node, neighboring fog nodes, and the cloud center to minimize TSD for tasks. A delay-aware online workload allocation and scheduling algorithm, which enables the local fog node to cooperate with neighboring fog nodes and the cloud center, is proposed.
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