In cloud-edge collaborative working mode, application services are affected by complex components, configuration and deployment conditions and other multi-dimensional factors. Therefore, it’s important to use simulation tools to conduct experiments reasonably and effectively. EdgeCloudSim is a simulation test platform widely used in edge computing. However, EdgeCloudSim's built-in task scheduling algorithm FF(First Fit) has problem of queuing and blocking, which makes some tasks fail to obtain computing resources in time. Besides, the processing time and load are not ideal. Another algorithm LL(Least load) optimizes above problems, but VM list needs to be traversed every time while scheduling. So the decision-making efficiency is low. Furthermore, EdgeCloudSim ignores migration cost of tasks. This paper improves the task scheduling framework of EdgeCloudSim for above problems and proposes a two-stage cloud-edge task scheduling strategy based on MH-LL(Max Heap-Least Load) algorithm and PP-LL(Position Probability-Least Load) algorithm. When resources of scheduled edge cloud are insufficient, task will be migrated to other edge clouds according to the migration cost calculated by distance factor α and distance coefficient d. Comparing with the original platform by experiments, the improved platform has an average optimization of 1.38%, 2.978s, 0.129s, and 19.26% in task failure rate, service time, delay, and load. It can improve work efficiency and system performance.
With the continuous expansion of cloud data center scale, the flow of network traffic generated by virtual machine scheduling also increased, resulting in a sharp increase of energy consumption in cloud data center. Under certain network topology, different virtual machine scheduling strategies would produce different network flow. Therefore, how to optimize the scheduling strategy and reduce network flow became the key factors to reduce the energy consumption of cloud datacenter. In this paper, based on the cloud computing simulation platform CloudSim, the network module was extended, adding the network communication module of virtual machine migrating. In order to ensure the stability of the network data center, a virtual machine scheduling algorithm NPABFD for network traffic and energy consumption aware was proposed. The experimental results showed that the NPABFD algorithm could effectively reduce the transfer traffic of migrating VMs in high-level switches, reduce the global NDC's global energy consumption and the migrating number of VMs.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.