Name
Abstract | ||
|---|---|---|
Growing tenant workload needs and an increasingly competitive market will force cloud providers to operate their data centers at significantly higher utilization levels than seen today. We argue that a key enabler of such cloud ecosystems would be facilities for tenants to engage in fine-grained resource scaling in addition to those offered by current providers. The basic unit of resource scaling exposed by current cloud providers is the canonical interface of virtual machines (VMs) with relatively static resource capacities. This paper describes opportunities and challenges in augmenting this interface to also include fine-grained scaling of CPU and memory within an already procured VM. Qualitative arguments for why this would offer cost benefits for both the provider and its tenants are presented. We focus on the cost-effective operation of a tenant in such an environment via the design of a feedback controller. The efficacy of our ideas is illustrated by implementing a case study in a Memcached tenant workload. Our results are promising and point to an interesting and broad area for further research - e.g., with the real-world workload in our evaluation, up to 50% utility improvement can be achieved by just applying memory scaling, a further 66% improvement can be achieved by coordinating fine-grained CPU and memory scaling. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/CLOUD.2016.0026 | 2016 IEEE 9th International Conference on Cloud Computing (CLOUD) |
Keywords | Field | DocType |
fine-grained resource scaling,public cloud,data centers,cloud ecosystems,canonical interface,virtual machines,static resource capacity,CPU,feedback controller design,Memcached tenant workload | Resource management,Enabling,Virtual machine,Workload,Computer science,Real-time computing,Memory management,Dynamic priority scheduling,Procurement,Distributed computing,Cloud computing | Conference |
ISSN | ISBN | Citations |
2159-6182 | 978-1-5090-2620-3 | 2 |
PageRank | References | Authors |
0.38 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Wang | 1 | 88 | 6.95 |
| Aayush Gupta | 2 | 213 | 11.81 |
| Bhuvan Urgaonkar | 3 | 2309 | 158.10 |