Abstract
The computing resources of today’s smartphones are underutilized most of the time. Using these resources could be highly beneficial in edge computing and fog computing contexts, for example, to support urban services for citizens. However, new challenges, especially regarding job scheduling, arise. Smartphones may form ad hoc networks, but individual devices highly differ in computational capabilities and (tolerable) energy usage. We take into account these particularities to validate a task execution scheme that relies on the computing power that clusters of mobile devices could provide. In this paper, we expand the study of several practical heuristics for job scheduling including execution scenarios with state-of-the-art smartphones. With the results of new simulated scenarios, we confirm previous findings and better comprehend the baseline approaches already proposed for the problem. This study also sheds some light on the capabilities of small-sized clusters comprising mid-range and low-end smartphones when the objective is to achieve real-time stream processing using Tensorflow object recognition models as edge jobs. Ultimately, we strive for industry applications to improve task scheduling for dew computing contexts. Heuristics such as ours plus supporting dew middleware could improve citizen participation by allowing a much wider use of dew computing resources, especially in urban contexts in order to help build smart cities.
Highlights
Smartphones have increasing capabilities of processing information, which typically are underutilized [1,2]
In a previous work [1], we proposed baseline heuristics to perform such resource management for a variant of self-supported sensing and a computing scheme where data collected in a local context is processed by a group of smartphones within the same context
We see a direct relationship between completed jobs and makespan, meaning that the heuristic which beats the other, either AhESEAS or remaining transfer capacity (RTC), in jobs completion achieved the relatively least makespan
Summary
Smartphones have increasing capabilities of processing information, which typically are underutilized [1,2]. Any person carrying a smartphone could contribute with valuable resources to help cities grow and to manage them in a more sustainable way. Processing locally sensed data can be done in different but not necessarily mutually exclusive ways, for instance, using distant cloud resources, offloaded to proximate fog servers, or with the help of devices with computing capabilities within the data collection context, e.g., with smartphones. This latter architectural option has been considered as an attractive self-supported sensing and computing scheme [8]. Depending on the adopted approach—hybrid or self-supported—effectively managing smartphones’
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
