@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} } @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} } @proceedings {48, title = {To cloud or not to cloud?: musings on costs and viability}, year = {2011}, month = {01/2011}, publisher = {ACM}, edition = {Article No. 29 }, abstract = {

In this paper we aim to understand the types of applications for which cloud computing is economically tenable, i.e., for which the cost savings associated with cloud placement outweigh any associated deployment costs. We discover two scenarios. (i) In an "unified client" scenario, where the cloud-hosted applications are only accessed by a single cloud customer (or small set of associates), it is important to ensure that the cost savings (mainly computation-related) can offset the often significant client-cloud distance (network costs etc). Today, from a technological, cost-centric point of view, this includes only compute-intensive applications with at least 1000 CPU cycles per each 32 bits of client-cloud transferred data. Naturally a number of other considerations may make clouds attractive even for less compute intensive tasks (services, security, pay-as-you-go nature etc). (ii) In a "multi-client" setting on the other hand, when outsourced applications serve numerous different third parties, we show that clouds begin to act similarly in nature to content-distribution networks - their better network integration is simply too good to pass on, when compared to locally hosting the applications (and incurring associated network costs). Thus, in multi-client scenarios, today{\textquoteright}s compute, energy and general technology costs suggest that outsourcing to clouds is profitable for almost any application. Ultimately, we hope this work will constitute a first step in an objective evaluation of the technological side of costs of outsourcing and computing in general.

}, isbn = {978-1-4503-0976-9 }, doi = {10.1145/2038916.2038945 }, url = {http://dl.acm.org/citation.cfm?doid=2038916.2038945}, author = {Yao Chen and Radu Sion} }