@conference {60, title = {The Economics of Long-Term Digital Storage}, booktitle = {The Memory of the World in the Digital Age: Digitization and Preservation}, year = {2012}, month = {09/2012}, publisher = {United Nations Educational, Scientific and Cultural Organization (UNESCO)}, organization = {United Nations Educational, Scientific and Cultural Organization (UNESCO)}, address = {Vancouver, Canada}, abstract = {

Paper as the medium for the world{\textquoteright}s memory has one great advantage; it survives benign neglect well. Bits, on the other hand, need continual care, and thus a con- tinual flow of money. A Blue Ribbon panel described economic sustainability as the major issue facing long- term digital preservation. This is despite Kryder{\textquoteright}s Law, the 30-year history of the cost of digital storage media dropping exponentially. If economics are the major con- cern even when Kryder{\textquoteright}s Law holds, what will happen if it slows or stops? We present evidence that it will, and some simulations of the impact on digital preservation costs.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/unesco12/UNESCO2012-storage-econ.pdf}, author = {David S. H. Rosenthal and Daniel C. Rosenthal and Ethan L. Miller and Ian F. Adams and Mark W. Storer and Erez Zadok} } @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 = {

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. \ 

}, 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 {38, title = {Minimizing Data Center SLA Violations and Power Consumption via Hybrid Resource Provisioning}, booktitle = {International Green Computing Conference and Workshops (IGCC {\textquoteright}11)}, year = {2011}, abstract = {

This paper presents a novel approach to correctly allocate resources in data centers, such that SLA violations and energy consumption are minimized. Our approach first analyzes historical workload traces to identify long-term patterns that establish a {\textquotedblleft}base{\textquotedblright} workload. It then employs two \ techniques to dynamically allocate capacity: predictive \ provisioning handles the estimated base workload at coarse \ time scales (e.g., hours or days) 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 costs. 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

}, keywords = {Data centers, performance management, power management, resource allocation}, url = {http://www3.cs.stonybrook.edu/~anshul/igcc_2011.pdf}, author = {Anshul Gandhi and Yuan Chen and Daniel Gmach and Martin Arlitt and Manish Marwah} } @article {42, title = {Server farms with setup costs}, journal = {Performance Evaluation}, volume = {67}, year = {2010}, month = {11/2010}, chapter = {1123}, abstract = {

In this paper we consider server farms with a setup cost. This model is common in manufacturing systems and data centers, where there is a cost to turn servers on. Setup costs always take the form of a time delay, and sometimes there is additionally a power penalty, as in the case of data centers. Any server can be either on, off, or in setup mode. While prior work has analyzed single servers with setup costs, no analytical results are known for multi-server systems. In this paper, we derive the first closed-form solutions and approximations for the mean response time and mean power consumption in server farms with setup costs. We also analyze variants of server farms with setup, such as server farm models with staggered boot up of servers, where at most one server can be in setup mode at a time, or server farms with an infinite number of servers. For some variants, we find that the distribution of response time can be decomposed into the sum of response time for a server farm without setup and the setup time. Finally, we apply our analysis to data centers, where both response time and power consumption are key metrics. Here we analyze policy design questions such as whether it pays to turn servers off when they are idle, whether staggered boot up helps, how to optimally mix policies, and other questions related to the optimal data center size.

}, keywords = {Data centers, M/M/k, power management, Setup times, Staggered boot up}, doi = {10.1016/j.peva.2010.07.004}, url = {http://www3.cs.stonybrook.edu/~anshul/perf_mmk_2010.pdf}, author = {Anshul Gandhi and Mor Harchol-Balter and Ivo Adan} }