@article {30,
	title = {Hybrid Resource Provisioning for Minimizing Data Center SLA Violations and Power Consumption},
	journal = {Sustainable Computing, Informatics and Systems},
	volume = {2},
	year = {2012},
	pages = {14},
	chapter = {91},
	abstract = {<p>This paper presents a practical and systematic approach to correctly provision server resources in data centers, such that SLA violations and energy consumption are minimized. In particular, we describe a hybrid method for server provisioning. Our method first applies a novel discretization technique on his- torical workload traces to identify long-term workload demand patterns that establish a {\textquotedblleft}base{\textquotedblright} load. It then employs two techniques to dynamically allocate capacity: predictive provisioning handles the pre- dicted base load at coarse time scales (e.g., hours) and reactive provisioning handles any excess workload at finer time scales (e.g., minutes). The combination of predictive and reactive provisioning achieves a significant improvement in meeting SLAs, conserving energy and reducing provisioning cost. We implement and evaluate our approach using traces from four production systems. The results show that our approach can provide up to 35\% savings in power consumption and reduce SLA violations by as much as 21\% compared to existing techniques, while avoiding frequent power cycling of servers. \&nbsp;</p>
},
	keywords = {power management, resource provisioning, Sustainable computing, Workload analysis},
	doi = {10.1016/j.suscom.2012.01.005},
	url = {http://www3.cs.stonybrook.edu/~anshul/suscom.pdf},
	author = {Anshul Gandhi and Yuan Chen and Daniel Gmach and Martin Arlitt and Manish Marwah}
}
@conference {40,
	title = {How Data Center Size Impacts the Effectiveness of Dynamic Power Management},
	booktitle = {49th Annual Allerton Conference on Communication, Control, and Computing},
	year = {2011},
	abstract = {<p>Power consumption accounts for a significant portion of a data center{\textquoteright}s operating expenses. Sadly, much of this power is wasted by servers that are left on even when there is no work to do. Dynamic power management aims to reduce power wastage in data centers by turning servers off when they are not needed. However, turning a server back on requires a setup time, which can adversely affect system performance. Thus, it is not obvious whether dynamic power management should be employed in a data center. In this paper, we analyze the effectiveness of dynamic power management in data centers under an M/M/k model via Matrix- analytic methods. We find that the effectiveness of even the simplest dynamic power management policy increases with the data center size, surpassing static power management when the number of servers exceeds 50, under realistic setup costs and server utilizations. Furthermore, we find that a small enhancement to traditional dynamic power management, involving delaying the time until a server turns off, can yield benefits over static power management even for data center sizes as small as 4 servers. \&nbsp;</p>
},
	url = {http://www3.cs.stonybrook.edu/~anshul/allerton_2011.pdf},
	author = {Anshul Gandhi and Mor Harchol-Balter}
}
