The growing popularity of multi-tenant, cloud-based computing platforms is increasing
interest in resource allocation models that permit flexible sharing of the underlying
infrastructure. This thesis introduces a novel IO resource allocation model
that better captures the requirements of paying tenants sharing a physical infrastructure.
The model addresses a major concern regarding application performance
stability when clients migrate from a dedicated to a shared platform. Specifically,
while clients would like their applications to behave similarly in both situations, traditional
models of fairness, like proportional share allocation, do not exhibit this
behavior in the context of modern multi-tiered storage architectures.
We also present a scheduling algorithm, the Reward Scheduler, that implements
the new allocation policy, by rewarding clients with better runtime characteristics,
resulting in benefits to both the clients and the service provider. Moreover, the Reward
scheduler also supports weight-based capacity allocation subject to a minimum
reservation and maximum limitation on the IO allocation for each task. Experimental
results indicate that the proposed algorithm proportionally allocates the system
capacity in proportion to their entitlements.
| Identifer | oai:union.ndltd.org:RICE/oai:scholarship.rice.edu:1911/64697 |
| Date | 06 September 2012 |
| Creators | Elnably, Ahmed |
| Contributors | Varman, Peter |
| Source Sets | Rice University |
| Language | English |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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