원문정보
초록
영어
Cloud computing is designed as computing as a utility. Customers rent computing resources in the cloud to complete their work. To ensure the quality of service (QoS) requirements defined by customers and guarantee the resource utilization in cloud datacenters, effective resource management systems should be considered. However, with more and more individuals and enterprises migrating their work into the cloud, workloads in the cloud become more and more heterogeneous. Meanwhile, resources are much more heterogeneous as cloud providers constantly scale or update the clusters with new generations of machines. Withal, workloads are dynamic with different resource demands during their execution. Besides, machines are re-engaged frequently after removal due to various reasons such as crushing and updating. The heterogeneity and dynamicity in both workloads and resources are huge barriers to using classic resource management systems. This paper will first introduce the status quo of cloud computing environment, and then give an overview of resource demand prediction and allocation policies. Finally, challenges are proposed to help build adaptive resource management systems.
Keywords
목차
1. Introduction
2. Workloads in the Cloud
2.1. Workload Classification
2.2. Workload Characterization
2.3. Workload Modeling Techniques
3. Resource Demand Prediction Overview
4. Resource Allocation Overview
5. Resource Management System
6. Challenges
6.1. Finding the Most Vital Application Parameters
6.2. Predicting the Multi-resource Demand
6.3. Sharing Resources with Other Jobs
6.4. Dynamic Adjustment during Job Execution
6.5. Other Challenges
7. Conclusions
Acknowledgements
References