I-BOT: Interference-Based Orchestration of Tasks for Dynamic Unmanaged Edge Computing

by   Shikhar Suryavansh, et al.

In recent years, edge computing has become a popular choice for latency-sensitive applications like facial recognition and augmented reality because it is closer to the end users compared to the cloud. Although infrastructure providers are working toward creating managed edge networks, personal devices such as laptops and tablets, which are widely available and are underutilized, can also be used as potential edge devices. We call such devices Unmanaged Edge Devices (UEDs). Scheduling application tasks on such an unmanaged edge system is not straightforward because of three fundamental reasons-heterogeneity in the computational capacity of the UEDs, uncertainty in the availability of the UEDs (due to devices leaving the system), and interference among multiple tasks sharing a UED. In this paper, we present I-BOT, an interference-based orchestration scheme for latency-sensitive tasks on an Unmanaged Edge Platform (UEP). It minimizes the completion time of applications and is bandwidth efficient. I-BOT brings forth three innovations. First, it profiles and predicts the interference patterns of the tasks to make scheduling decisions. Second, it uses a feedback mechanism to adjust for changes in the computational capacity of the UEDs and a prediction mechanism to handle their sporadic exits. Third, it accounts for input dependence of tasks in its scheduling decision (such as, two tasks requiring the same input data). To evaluate I-BOT, we run end-to-end simulations with applications representing autonomous driving, composed of multiple tasks. We compare to two basic baselines (random and round-robin) and two state-of-the-arts, Lavea [SEC-2017] and Petrel [MSN-2018]. Compared to these baselines, I-BOT significantly reduces the average service time of application tasks. This reduction is more pronounced in dynamic heterogeneous environments, which would be the case in a UEP.


page 1

page 9

page 11


Resource Provisioning in Edge Computing for Latency Sensitive Applications

Low-Latency IoT applications such as autonomous vehicles, augmented/virt...

Predictive Edge Computing with Hard Deadlines

Edge computing is a promising approach for localized data processing for...

On Computing In the Network: Covid-19 Coughs Detection Case Study

Computing in the network (COIN) is a promising technology that allows pr...

A Case for a Programmable Edge Storage Middleware

Edge computing is a fast-growing computing paradigm where data is proces...

An Adaptive Performance-oriented Scheduler for Static and Dynamic Heterogeneity

With the emergence of heterogeneous hardware paving the way for the post...

Towards Privacy-aware Task Allocation in Social Sensing based Edge Computing Systems

With the advance in mobile computing, Internet of Things, and ubiquitous...

Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation

Smart devices have become an indispensable part of our lives and gain in...

Please sign up or login with your details

Forgot password? Click here to reset