Abstract

Cloud computing is a novel computing paradigm, which connects plenty of computing resources and storage resources via Internet. Cloud computing provides a number of high-quality services, such as cloud storage, outsourcing computing, and on-demand self-service, which have been widely accepted by the public. In cloud computing, by submitting their tasks to cloud, plenty of applications share huge computation and storage resources. However, how to schedule resource efficiently is a big challenge in cloud computing.In this paper, we propose a SLA-aware resource algorithm to enable cloud storage more efficiently. In our scheme, we take advantage of the back-end node space utilization and I/O throughput comprehensively simultaneously. We compare and contrast the existing scheduling storage policies by implementing those algorithms. The extensive tests show that our algorithm achieves a considerable improvement in terms of violation rate and the number of used hosts.

Highlights

  • Cloud computing is the development and fusion of the grid computing, parallel processing, and distributed computing, and it uses Internet to connect lots of computing resources and storage resources [1]

  • Our contributions: In this paper, to solve the problem of resources scheduling in cloud computing, we propose a novel SLA-aware resource scheduling algorithm for block storage

  • 5 Conclusion In this paper, we investigate the Cinder weight algorithm in OpenStack, which is a SLA-aware scheduling algorithm based on the OpenStack Cinder scheduling module

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Summary

Introduction

Cloud computing is the development and fusion of the grid computing, parallel processing, and distributed computing, and it uses Internet to connect lots of computing resources and storage resources [1]. To solve the problem of resource scheduling based on SLA, Li and Guo [31] presented a new method by using stochastic integer programming in 2010 In their scheme, they extend Minimised Geometric Buchberger Algorithm(MGBA), and combined the Grobner bases theory to address the stochastic integer programming firstly. They extend Minimised Geometric Buchberger Algorithm(MGBA), and combined the Grobner bases theory to address the stochastic integer programming firstly They presented a method for the optimal model of SLA-based resource schedule issue. Yao et al [34] proposed a Modified Vector Best Fit Decreasing algorithm (MVBFD) to address the volume allocation problem for cloud storage systems in 2015 They chose the proper storage node according to multiple resources, the volume requests and so on.

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