Workload Consolidation for Cloud Data Centers with Guaranteed QoS Using Request Reneging
| Autor: | S. Liu, Shaolei Ren, Soamar Homsi, Gustavo A. Chaparro-Baquero, Ou Bai, Gang Quan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Service (business)
020203 distributed computing Distributed database Computer science business.industry Quality of service Distributed computing Cloud computing 02 engineering and technology Mobile QoS Service provider Virtualization computer.software_genre Resource (project management) Computational Theory and Mathematics Hardware and Architecture Virtual machine Server Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Resource management business computer Computer network |
| Zdroj: | IEEE Transactions on Parallel and Distributed Systems. 28:2103-2116 |
| ISSN: | 1045-9219 |
| DOI: | 10.1109/tpds.2016.2642941 |
| Popis: | Cloud data centers are widely employed to offer reliable cloud services. However, low resource utilization and high power consumption have been great challenges for cloud providers. Moreover, the rapid increase in demand for affordable cloud services magnifies the obstacles for proficient resource management policies. In this paper, we investigate how to improve resource utilization and power consumption in cloud data centers when delivering services with statistically guaranteed Quality of Service (QoS). We assume that the service provider hosts different types of services, each of which has request classes with different QoS requirements. Different from the traditional approaches that distribute workloads with different QoS levels on different Virtual Machines (VMs), we introduce an approach to pack requests of the same service type, even with different QoS requirements, into the same VM, and to remove potential failure requests in time to improve resource usage and energy cost. We formally prove that our algorithm can statistically guarantee QoS conditions in terms of deadline miss ratios. We develop a cloud prototype to empirically validate our proposed methods and algorithm. Our experimental results demonstrate that our approach can significantly outperform other traditional approaches in terms of QoS guarantees, power consumption, resource demand and electricity cost. |
| Databáze: | OpenAIRE |
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