@article {83, title = {Analyzing the Power Consumption of the Mobile Page Load}, year = {2016}, author = {Yi Cao and Javad Nejati and Pavan Maguluri and Aruna Balasubramanian and Anshul Gandhi} } @conference {59, title = {On the Importance of Evaluating Storage Systems{\textquoteright} $Costs}, booktitle = {The 6th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage {\textquoteright}14)}, year = {2014}, month = {06/2014}, address = {Philadelphia, PA}, abstract = {

Modern storage systems are becoming more complex, combining different storage technologies with different behaviors. \ Performance alone is not enough\  to charac- terize storage systems: energy efficiency, durability, and more are becoming equally\  important. We posit that one must evaluate storage systems from a monetary cost per- spective as\  well as performance. \ We believe that cost should consider the workloads used over the storage sys-\  tems{\textquoteright} expected lifetime. \ We designed and developed a versatile hybrid storage system under Linux\  that com- bines HDD and SSD. The SSD can be used as cache or as primary storage for hot data. Our\  system includes tun- able parameters to enable trading off performance, en- ergy use, and\  durability. We built a cost model and eval- uated our system under a variety of workloads and pa-\  rameters, to illustrate the importance of cost evaluations of storage systems.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/hotstorage14-cost/cost.pdf}, author = {Zhichao Li and Amanpreet Mukker and Erez Zadok} } @article {57, title = {Visual Correlation Analysis of Numerical and Categorical Data on the Correlation Map}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG {\textquoteright}14)}, year = {2014}, month = {12/2014}, abstract = {

Correlation analysis can reveal the complex relationships that often exist among the variables in multivariate data.\  However, as the number of variables grows, it can be difficult to gain a good understanding of the correlation landscape and\  important intricate relationships might be missed. We previously introduced a technique that arranged the variables into a 2D\  layout, encoding their pairwise correlations. We then used this layout as a network for the interactive ordering of axes in parallel\  coordinate displays. Our current work expresses the layout as a correlation map and employs it for visual correlation analysis. In\  contrast to matrix displays where correlations are indicated at intersections of rows and columns, our map conveys correlations\  by spatial proximity which is more direct and more focused on the variables in play. We make the following new contributions,\  some unique to our map: (1) we devise mechanisms that handle both categorical and numerical variables within a unified\  framework, (2) we achieve scalability for large numbers of variables via a multi-scale semantic zooming approach, (3) we\  provide interactive techniques for exploring the impact of value bracketing on correlations, and (4) we visualize data relations\  within the sub-spaces spanned by correlated variables by projecting the data into a corresponding tessellation of the map.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/tvcg14/TVCG_Correlation_MinorRev_Final.pdf}, author = {Zhiyuan Zhang and Kevin T. McDonnell and Erez Zadok and Klaus Mueller} } @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} } @conference {62, title = {Model Discovery for Energy-Aware Computing Systems: An Experimental Evaluation}, booktitle = {1st Workshop on Energy Consumption and Reliability of Storage Systems (ERSS {\textquoteright}11)}, year = {2011}, month = {07/2011}, address = {Orlando, FL}, abstract = {
We present a model-discovery methodology for energy-aware computing systems that achieves high prediction accuracy. Model discovery, or system identification, is a critical first step in designing advanced controllers that can dynamically manage the energy-performance trade-off in an optimal manner. Our methodology favors Multiple-Inputs-Multiple-Outputs (MIMO) models over a collection of Single-Input-Single-Output (SISO) models, when the inputs and outputs of the system are coupled in a nontrivial way. In such cases, MIMO is generally more accurate than SISO over a wide range of inputs in predicting system behavior. Our experimental evaluation, carried out on a representative server workload, validates our approach. We obtained an average prediction accuracy of 77\% and 76\% for MIMO power and performance, respectively. We also show that MIMO models are consistently more accurate than SISO ones.
}, url = {http://www.fsl.cs.stonybrook.edu/docs/erss11model/erss11model.pdf}, author = {Zhichao Li and Radu Grosu and Koundinya Muppalla and Scott A. Smolka and Scott D. Stoller and Erez Zadok} }