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Reliability and energy efficient workflow scheduling in cloud environment
- Source :
- Cluster Computing. 22:1283-1297
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
Abstract
- Cloud data centers consume huge amounts of electrical energy which results in an increased operational cost, decreased system reliability and carbon dioxide footprints. Thus, it is highly important to develop scheduling strategy to reduce energy consumption. Dynamic voltage and frequency scaling (DVFS) has been recognized as an efficient technique for reducing energy consumption. However, there is negative impact of DVFS on the reliability of system as it increases the transient faults during the application execution. Hence, it is essential to address the issue of reliability for mission critical applications. Recent studies on workflow scheduling in distributed environment have not considered reliability while minimizing the energy consumption. In this paper, we propose a new scheduling algorithm called the reliability and energy efficient workflow scheduling algorithm which jointly optimizes lifetime reliability of application and energy consumption and guarantees the user specified QoS constraint. The proposed algorithm works in four phases: priority calculation, clustering of tasks, distribution of target time and assigning the cluster to processing element with appropriate voltage/frequency levels. The simulation results obtained by using randomly generated task graphs and Gaussian Elimination task graphs shows that the proposed approach is effective in joint optimization of lifetime reliability of system and energy consumption compared to existing algorithms.
- Subjects :
- Computer Networks and Communications
business.industry
Computer science
Distributed computing
Quality of service
Mission critical
020206 networking & telecommunications
Cloud computing
02 engineering and technology
Energy consumption
Scheduling (computing)
chemistry.chemical_compound
chemistry
Carbon dioxide
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Cluster analysis
business
Frequency scaling
Computer Science::Operating Systems
Software
Efficient energy use
Subjects
Details
- ISSN :
- 15737543 and 13867857
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- Cluster Computing
- Accession number :
- edsair.doi...........70229571dde2dbb759f5a2093ec961dc