This chapter discusses the conclusions of the work presented in this thesis. The chapter ends with a discussion of the future direction which thesis work can take.
6.1 Conclusion
As the prevalence of Cloud Computing continues to rise, the need for power saving mechanisms and reducing CO2 footprint within the Cloud is increasing. In this thesis, a new technique “Temperature-Aware Virtual Machine Scheduling in Green Clouds” is presented and is implemented on test bed (which is discussed in chapter 5) in order to improve energy efficiency of a datacenter.
To demonstrate the potential of our proposed approach, we have presented result in previous chapter. In each test case our, proposed Temperature-Aware Virtual Machine Scheduling in Green Clouds gives result as per expectation, which can be verified by comparing with existing algorithms. Thus, through our proposed technique, we have found new ways to save vast amounts of energy, (by avoiding extensive cooling of data center) while minimally impacting performance. Apart from saving energy, greenhouse gas emissions can be reduced as overheating of data center is checked through proposed technique.
6.2 Future Scope
Future opportunities could explore following
Providing concrete conception to calculate the cost of migration of VMs.
Develop scheduling system that is both Power-Aware and Temperature-Aware to maximize energy savings both from physical servers and the cooling systems used. And at last study the effect of migration of VMs on network infrastructure.
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