CRED: Cloud Right-Sizing with Execution Deadlines and Data Locality
| Autor: | Maotong Xu, Sultan Alamro, Suresh Subramaniam, Tian Lan |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Optimization problem
Distributed database business.industry Computer science Distributed computing Locality Cloud computing 02 engineering and technology Scheduling (computing) Data modeling Computational Theory and Mathematics Hardware and Architecture 020204 information systems Server Service level Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Heuristics business Computer network |
| Zdroj: | IEEE Transactions on Parallel and Distributed Systems. 28:3389-3400 |
| ISSN: | 1045-9219 |
| DOI: | 10.1109/tpds.2017.2726071 |
| Popis: | As demands for cloud-based data processing continue to grow, cloud providers seek effective techniques that deliver value to the businesses without violating Service Level Agreements (SLAs). Cloud right-sizing has emerged as a very promising technique for making cloud services more cost-effective. In this paper, we present CRED, a novel framework for cloud right-sizing with execution deadlines and data locality constraints. CRED jointly optimizes data placement and task scheduling in data centers with the aim of minimizing the number of nodes needed while meeting users’ SLA requirements. We formulate CRED as an integer optimization problem and present a heuristic algorithm with provable performance guarantees to solve the problem. Competitive ratios of the proposed algorithm are quantified in closed form for arbitrary task parameters and cloud configurations. We also extend our work to obtain a resilient solution, which allows successful recovery at run time from any single node failure and is guaranteed to meet both deadline and locality constraints. Simulation results using Google trace show that our proposed algorithm significantly outperforms existing heuristics such as first-fit by reducing the number of required active servers by up to 47 percent, and achieves near-optimal performance. We also show that our algorithm can significantly improve utilization of both computational resources and storage space by up to 28 and 15 percent, respectively. |
| Databáze: | OpenAIRE |
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