Integrated data placement and task assignment for scientific workflows in clouds
Proceedings of the fourth international workshop on Data-intensive distributed computing
Online optimization for scheduling preemptable tasks on IaaS cloud systems
Journal of Parallel and Distributed Computing
QoS monitoring and dynamic trust establishment in the cloud
GPC'12 Proceedings of the 7th international conference on Advances in Grid and Pervasive Computing
An efficient data dissemination approach for cloud monitoring
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
DARGOS: A highly adaptable and scalable monitoring architecture for multi-tenant Clouds
Future Generation Computer Systems
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Cloud computing paradigm contains many shared resources, such as infrastructures, data storage, various platforms and software. Resource monitoring involves collecting information of system resources to facilitate decision making by other components in Cloud environment. It is the foundation of many major Cloud computing operations. In this paper, we extend the prevailing monitoring methods in Grid computing, namely Pull model and Push model, to the paradigm of Cloud computing. In Grid computing, we find that in certain conditions, Push model has high consistency but low efficiency, while Pull model has low consistency but high efficiency. Based on complementary properties of the two models, we propose a user-oriented resource monitoring model named Push&Pull (P&P) for Cloud computing, which employs both the above two models, and switches the two models intelligently according to users’ requirements and monitored resources’ status. The experimental result shows that the P&P model decreases updating costs and satisfies various users’’ requirements of consistency between monitoring components and monitored resources compared to the original models.