Year-end Sale % OFF 
Abstract
Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.
Highlights
Edge computing is an evolving computing paradigm offering a more efficient alternative: data is processed and analyzed closer to the point where it is created
An edge computing system usually consists of an edge computing agent (ECA) and multiple edge servers. e edge computing agent manages all resources and each edge server owns several virtual machines (VMs), each of which can usually handle a workflow task that a user offloads at a time
For a task executed on the edge server p, its reliability can be estimated as its success rate of execution, i.e., the probability that its time-to-failure (TTFp) exceeds its completion time: m mp rj xpk · ProbTTFp > FTtpk FT (Tpk), (2)
Summary
Edge computing is an evolving computing paradigm offering a more efficient alternative: data is processed and analyzed closer to the point where it is created. It enables computation as a service model and prepares a proximitybased and mobility-aware resource provisioning model of virtualized resources applicable on demand [1, 2] e edge service providers are equipped with computational facilities, which allow them to provide necessary spaces required by commercial and noncommercial users. As novel bioinspired and genetic algorithms are becoming increasingly versatile and powerful, a great deal of research efforts are paid to applying them in dealing with edge-environment-oriented workflow scheduling problem [9,10,11]. We show through simulative studies as well that our proposed method clearly outperforms traditional ones in terms of multiple metrics
Sign-in/Register to view highlights & summary 
Published Version (
Free)
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.
