Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy eff... View more
 R. Brown, E. Masanet, B. Nordman et al., Report to Congress on Server and Data Center Energy Efficiency: Public Law, 109-431 Berkeley: Lawrence Berkeley National Laboratory, technical report, 2008.
 K. Jonathan, , 2011; http://www.analyticspress.com/datacenters.html  L. Christian, Projecting Annual new Datacenter Construction Market This paper presents an in-depth model capturing the oper- Size Technique report, Microsoft Corp., 2010.
ational and thermal characteristics of Cloud datacenters.  L. A. Barroso, J. Clidaras, U. Hölzle, The Datacenter as a Computer: An We detail the methodology used to identify key parame- Introduction to the Design of Warehouse-Scale Machines Synthesis lecters for cooling model construction and validate its accu- tures on computer architecture, 2013, 8(3): 1-154.
racy in simulation using realistic datacenter operational  Y. C. Lee and A. Y. Zomaya, Energy Efficient Utilization of Resources conditions. This model forms the foundation to propose a in Cloud Computing System The Journal of Supercomputing, 2012, vol.
greedy-based VM scheduling algorithm named GRANITE. 60(2), pp. 268-280.
It comprises two separated components: initial placement  A. Murtazaev, S. Oh, Sercon: Server Consolidation Algorithm Using and dynamic live migration that targets reducing total en- Live Migration of Virtual Machines for Green Computing IEEE Techergy cost of cooling and servers while minimizing the like- nical Review, 2011, 28(3): 212-231.
lihood of SLA violation. The algorithm is evaluated against  T. C. Ferreto, M. Netto, R. Calheiros et al., Server Consolidation With numerous algorithms within CloudSim - a well-known Migration Control for Virtualized Data Future Generation tool for simulating Cloud datacenter. From the observa- Computer Systems, 2011, 27(8): 1027-1034.
tions and experiment results presented within this paper,  J. Moore, J. Chase, P. Ranganathan et al., Making Scheduling cool : we draw the following conclusions. Temperature-Aware Workload Placement in Data Centers USENIX To our knowledge, this is the first paper that captures Annual Technical Conf., 2005.
CRAC cooling, datacenter thermal characteristics, CPU  R. Zhou, Z. Wang, C. Bash et al., Data Center Cooling Management temperature and workload in a fine-grained manner. Our and Analysis-A Model Based Approach . Semiconductor Thermal Measmodel is capable of modeling server temperature by cap- urement and Management Symposium (SEMI-THERM), 2012: 98-103.
turing thermal characteristics of datacenter holistically.  S. Shrivastava, B. Sammakia, R. Schmidt R et al., Comparative AnalyThis model can be used by researchers in order to evaluate sis of Different Data Center Airflow Management Configurations Proc.