Effective hybrid load scheduling of online and offline clusters for e-health service

Abstract Hybrid load in e-health services is composed of online e-health service applications and offline jobs. Previous methods overlooked the impact of system performance for the fine-grained service components. In this paper, a hybrid load scheduling scheme is proposed in which scheduling is performed not only at the level of the component, but also within components. To improve both execution efficiency and searching accuracy, the proposed algorithm searches the compressing method of the Lucene index and then filters that index. Simulations are conducted on a Storm platform to evaluate the performance of the proposed scheme. Simulation results demonstrate that the proposed scheme can increase the response speed by 67.79% with an accuracy of 95.94%, and the response speed decreases by 11.6–53.2%.

[1]  Scott Shenker,et al.  Usenix Association 10th Usenix Symposium on Networked Systems Design and Implementation (nsdi '13) 185 Effective Straggler Mitigation: Attack of the Clones , 2022 .

[2]  Christina Delimitrou,et al.  Paragon: QoS-aware scheduling for heterogeneous datacenters , 2013, ASPLOS '13.

[3]  John Kubiatowicz,et al.  Tessellation: Refactoring the OS around explicit resource containers with continuous adaptation , 2013, 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC).

[4]  S. Murphy,et al.  The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. , 2007, American journal of preventive medicine.

[5]  Brighten Godfrey,et al.  More is less: reducing latency via redundancy , 2012, HotNets-XI.

[6]  Chi Lin,et al.  Protecting location privacy and query privacy: a combined clustering approach , 2015, Concurr. Comput. Pract. Exp..

[7]  Luiz André Barroso,et al.  The tail at scale , 2013, CACM.

[8]  B. Eswara Reddy,et al.  Exploiting Geo Distributed datacenters of a cloud for load balancing , 2015, 2015 IEEE International Advance Computing Conference (IACC).

[9]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[10]  Jie Liu,et al.  Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines , 2011, SoCC.

[11]  Christopher Stewart,et al.  Zoolander: Efficiently Meeting Very Strict, Low-Latency SLOs , 2013, ICAC.

[12]  Jianfeng Zhan,et al.  Interference-Aware Component Scheduling for Reducing Tail Latency in Cloud Interactive Services , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems.

[13]  Tony Solomonides.  Review of HealthGrid 2008: "Global HealthGrid: eScience meets Biomedical Informatics" , 2008, 2008 21st IEEE International Symposium on Computer-Based Medical Systems.

[14]  Kevin Klues,et al.  Improving per-node efficiency in the datacenter with new OS abstractions , 2011, SoCC.

[15]  Srikanth Kandula,et al.  Speeding up distributed request-response workflows , 2013, SIGCOMM.

[16]  Chi Lin,et al.  TADP: Enabling temporal and distantial priority scheduling for on-demand charging architecture in wireless rechargeable sensor Networks , 2016, J. Syst. Archit..

[17]  Athanasios V. Vasilakos,et al.  Managing Performance Overhead of Virtual Machines in Cloud Computing: A Survey, State of the Art, and Future Directions , 2014, Proceedings of the IEEE.

[18]  Abhishek Verma,et al.  Large-scale cluster management at Google with Borg , 2015, EuroSys.

[19]  Chan-Hyun Youn,et al.  A Mobile-to-Grid Gateway Model and Load Scheduling Algorithm for e-Health Service in Wireless Grid , 2006, 2006 First International Conference on Communications and Electronics.

[20]  Xiao Zhang,et al.  CPI2: CPU performance isolation for shared compute clusters , 2013, EuroSys '13.

[21]  Cui Li,et al.  A Data Placement Strategy for Data-Intensive Applications in Cloud , 2010 .

[22]  Christoforos E. Kozyrakis,et al.  Resource efficient computing for warehouse-scale datacenters , 2013, 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[23]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[24]  John Mitchell,et al.  A Semi-distributed Access Control Management Scheme for Securing Cloud Environment , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[25]  Mohammad S. Obaidat,et al.  GTCharge: A game theoretical collaborative charging scheme for wireless rechargeable sensor networks , 2016, J. Syst. Softw..

[26]  Mohammad S. Obaidat,et al.  Clustering and splitting charging algorithms for large scaled wireless rechargeable sensor networks , 2016, J. Syst. Softw..

[27]  S. Ravimaran,et al.  Dynamic Resource Parallel Processing and Scheduling by Using Virtual Machine in the Cloud Environment , 2014 .

[28]  Jaehyuk Huh,et al.  Dynamic Virtual Machine Scheduling in Clouds for Architectural Shared Resources , 2012, HotCloud.