Abstract
Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model.
Highlights
The exponential growth in wireless and mobile devices has resulted in producing an unprecedented amount of traffic that needs to be processed by various nodes, sometimes in real time
We developed a two-stage tandem queuing model for Edge Sensor Networks (ESNs) and Data Center Networks (DCNs), as well as feedback mechanisms to investigate key performance indicators, such as latency, delay variation, utilization, and the optimal number of computing units needed in a cluster
We present a two-stage tandem queuing model to characterize the Edge Sensor Network, which consists of k sensors or Internet of Things (IoT) nodes communicating with edge servers or gateways
Summary
The exponential growth in wireless and mobile devices has resulted in producing an unprecedented amount of traffic that needs to be processed by various nodes, sometimes in real time. With more smart houses being built than ever before and more IoT devices being implemented, data centers will need to respond within a constrained time These applications service several millions of queries and instructions on several thousands of machines and are concerned with response latency, latency variation (jitter), and tail latency. We developed a two-stage tandem queuing model for Edge Sensor Networks (ESNs) and Data Center Networks (DCNs), as well as feedback mechanisms to investigate key performance indicators, such as latency, delay variation (jitter), utilization, and the optimal number of computing units needed in a cluster.
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