@conference {80, title = {Minimizing Electricity Cost for Geo-Distributed Interactive Services with Tail Latency Constraint}, year = {2016}, abstract = {

Cross-border data movement has become increas- ingly more costly, and even been prohibited due to data sovereignty requirements. Consequently, geo-distributed inter- active services, which rely on geographically distributed data sets, is quickly emerging as an important class of workloads in data centers and resulting in soaring electricity costs. While numerous geographic load balancing (GLB) techniques exist to exploit differences in electricity prices for cost savings, they do not apply to emerging geo-distributed interactive services due to two major limitations. First, they assume that each request is processed only in one data center, whereas each geo- distributed interactive request must be processed at multiple data centers simultaneously. Second, they primarily focus on meeting average latency constraints, whereas tail latencies (i.e., high- percentile latencies) are more suitable to ensure a consistently good user experience. In this paper, we make an early effort to optimize GLB decisions for geo-distributed interactive services, exploiting spatial diversity of electricity prices to minimize the total electricity cost while meeting a tail latency constraint. Our solution employs a novel data-driven approach to determine the tail latency performance for different GLB decisions, by profiling the network latency and data center latency at a low complexity. We run trace-based discrete-event simulations to validate our design, showing that it can reduce the electricity cost by more than 7\% while meeting the tail latency constraint compared to the performance-aware but cost-oblivious approach.

}, author = {Mohammad A. Islam and Anshul Gandhi and Shaolei Ren} } @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} }