Energy consumption

Quantum mechanics

Workflow

Engineering

Frequency scaling

Ecology

Reliability engineering

Efficient energy use

Biology

Real-time computing

Scheduling (production processes)

Operating system

Quality of service

Power (physics)

Computer science

Cloud computing

Mathematical optimization

Distributed computing

Computer network

Physics

Database

Rate-monotonic scheduling

Mathematics

Dynamic priority scheduling

Reliability (semiconductor)

Cluster analysis

Earliest deadline first scheduling

Machine learning

Electrical engineering

Cluster Computing

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.

Reliability and energy efficient workflow scheduling in cloud environment