Scheduling Method of Data-Intensive Applications in Cloud Computing Environments

Xiong, Fei; Yeliang, Cang; Xiang-rong, Zhu; Deng, Song

doi:10.1155/2015/605439

Cited by 3 publications

(1 citation statement)

References 18 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recent proposals, such as resource-aware adaptive scheduling [26] and cost effective resource provisioning [27], have introduced resource-aware job schedulers to the MapReduce framework. Reference [28] mentions the problem of task assignment with the consideration of the data locality in cloud computing. Reference [29] focuses on the scheduling with the consideration of the data locality to minimize the cost caused by accessing remote files.…”

Section: Related Workmentioning

confidence: 99%

Optimized Speculative Execution to Improve Performance of MapReduce Jobs on Virtualized Computing Environment

Yang

Zhang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Recently, virtualization has become more and more important in the cloud computing to support efficient flexible resource provisioning. However, the performance interference among virtual machines may affect the efficiency of the resource provisioning. In a virtualized environment, where multiple MapReduce applications are deployed, the performance interference can also affect the performance of the Map and Reduce tasks resulting in the performance degradation of the MapReduce jobs. Then, in order to ensure the performance of the MapReduce jobs, a framework for scheduling the MapReduce jobs with the consideration of the performance interference among the virtual machines is proposed. The core of the framework is to identify the straggler tasks in a job and back up these tasks to make the backed up one overtake the original tasks in order to reduce the overall response time of the job. Then, to identify the straggler task, this paper uses a method for predicting the performance interference degree. A method for scheduling the backing-up tasks is presented. To verify the effectiveness of our framework, a set of experiments are done. The experiments show that the proposed framework has better performance in the virtual cluster compared with the current speculative execution framework.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimized Speculative Execution to Improve Performance of MapReduce Jobs on Virtualized Computing Environment

Yang

Zhang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

Deadline based scheduling for data-intensive applications in clouds

Xiong

Yeliang

Zhu

et al. 2016

The Journal of China Universities of Posts and Telecommunicatio

View full text Add to dashboard Cite

Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

Yang

Zhang

Zhao

et al. 2017

Int. J. Mod. Phys. C

View full text Add to dashboard Cite

Identifying influential spreaders is crucial for developing strategies to control the spreading process on complex networks. Following the well-known K-Shell (KS) decomposition, several improved measures are proposed. However, these measures cannot identify the most influential spreaders accurately. In this paper, we define a Local K-Shell Sum (LKSS) by calculating the sum of the K-Shell indices of the neighbors within 2-hops of a given node. Based on the LKSS, we propose an Extended Local K-Shell Sum (ELKSS) centrality to rank spreaders. The ELKSS is defined as the sum of the LKSS of the nearest neighbors of a given node. By assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performance between the ELKSS centrality and other six measures. The results show that the ELKSS centrality has a better performance than the six measures to distinguish the spreading ability of nodes and to identify the most influential spreaders accurately.

show abstract

Scheduling Method of Data-Intensive Applications in Cloud Computing Environments

Cited by 3 publications

References 18 publications

Optimized Speculative Execution to Improve Performance of MapReduce Jobs on Virtualized Computing Environment

Optimized Speculative Execution to Improve Performance of MapReduce Jobs on Virtualized Computing Environment

Deadline based scheduling for data-intensive applications in clouds

Identifying the most influential spreaders in complex networks by an Extended Local K-Shell Sum

Contact Info

Product

Resources

About