Multi-level Scheduling Algorithm Based on Storm = Multi-level Scheduling Algorithm Based on Storm

다국어 초록 (Multilingual Abstract)

Hybrid deployment under current cloud data centers is a combination of online and offline services, which improves the utilization of the cluster resources. However, the performance of the cluster is often affected by the online services in the hybrid deployment environment. To improve the response time of online service (e.g. search engine), an effective scheduling algorithm based on Storm is proposed. At the component level, the algorithm dispatches the component with more influence to the optimal performance node. Inside the component, a reasonable resource allocation strategy is used. By searching the compressed index first and then filtering the complete index, the execution speed of the component is improved with similar accuracy. Experiments show that our algorithm can guarantee search accuracy of 95.94%, while increasing the response speed by 68.03%.

참고문헌 (Reference)

1 C. Stewart., "Zoolander : Efficiently meeting very strict, low-latency SLOs" 265-277, 2013

2 H. Kasture., "Ubik : efficient cache sharing with strict qos for latency-critical workloads" 42 : 729-742, 2014

3 J. Dean J., "The tail at scale" 56 (56): 74-90, 2013

4 J. A. Colmenares., "Tessellation : Refactoring the OS around explicit resource containers with continuous adaptation" 1-10, 2013

5 V. Jalaparti., "Speeding up distributed request-response workflows" 219-230, 2013

6 R. Han., "SARP : producing approximate results with small correctness losses for cloud interactive services" ACM 2015

7 C. Kozyrakis, "Resource efficient computing for warehouse-scale datacenters" 1351-1356, 2013

8 C. Delimitrou., "Paragon : QoS-aware scheduling for heterogeneous datacenters" 48 (48): 77-88, 2013

9 D. Xu., "On Mitigating Memory Bandwidth Contention through Bandwidth-Aware Scheduling" 237-248, 2010

10 A. Vulimiri., "More is less : reducing latency via redundancy" 13-18, 2012

11 J. Dean., "MapReduce : simplified data processing on large clusters" 51 (51): 107-113, 2008

12 B. Rhoden., "Improving per-node efficiency in the datacenter with new OS abstractions" 25-32, 2011

13 C. Reiss., "Heterogeneity and dynamicity of clouds at scale : Google trace analysis" 7-, 2012

14 G. Ananthanarayanan., "Effective straggler mitigation : attack of the clones" 21 (21): 185-198, 2013

15 J. Ahn., "Dynamic virtual machine scheduling in clouds for architectural shared resources" 1-5, 2012

16 J.Wilkes., "Details of the ClusterData-2011-1 trace"

17 S. Govindan., "Cuanta : quantifying effects of shared on-chip resource interference for consolidated virtual machines" 22-35, 2011

18 Zhang X., "CPI 2 : CPU performance isolation for shared compute clusters" 379-391, 2013

19 H. Yang., "Bubble-flux : Precise online qos management for increased utilization in warehouse scale computers" 607-618, 2013