Name
Abstract | ||
|---|---|---|
There is an increasing need for cloud service performance that can be tailored to customer requirements. In the context of jobs submitted to cloud computing clusters, a crucial requirement is the specification of job completion-times. A natural way to model this specification, is through client/job utility functions that are dependent on job completion-times. We present a method to allocate and schedule heterogeneous resources to jointly optimize the utilities of jobs in a cloud. Specifically: (i) we formulate a completion-time optimal resource allocation (CORA) problem to apportion cluster resources across the jobs that enforces max-min fairness among job utilities, and (ii) starting with an integer programming problem, we perform a series of steps to transform it into an equivalent linear programming problem, and (iii) we implement the proposed framework as a utility-aware resource scheduler in the widely used Hadoop data processing framework, and finally (iv) through extensive experiments with real-world datasets, we show that our prototype achieves significant performance improvement over existing resource-allocation policies. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (INFOCOM) | Customer requirements,Data processing,Computer science,Resource allocation,Integer programming,Linear programming,Job scheduler,Operating system,Distributed computing,Cloud computing,Performance improvement |
DocType | ISSN | Citations |
Conference | 0743-166X | 10 |
PageRank | References | Authors |
0.54 | 16 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhe Huang | 1 | 69 | 8.22 |
| Bharath Balasubramanian | 2 | 50 | 11.96 |
| Michael Wang | 3 | 130 | 7.38 |
| Tian Lan | 4 | 359 | 18.82 |
| Mung Chiang | 5 | 7303 | 486.32 |
| Danny H. K. Tsang | 6 | 945 | 95.24 |