@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 {82, title = {Autoscaling for Hadoop Clusters}, year = {2016}, abstract = {

Unforeseen events such as node failures and re- source contention can have a severe impact on the performance of data processing frameworks, such as Hadoop, especially in cloud environments where such incidents are common. SLA compliance in the presence of such events requires the ability to quickly and dynamically resize infrastructure resources. Unfortunately, the distributed and stateful nature of data processing frameworks makes it challenging to accurately scale the system at run-time. In this paper, we present the design and implementation of a model-driven autoscaling solution for Hadoop clusters. We first develop novel gray-box performance models for Hadoop workloads that specifically relate job execution times to resource allocation and workload parameters. We then employ these models to dynamically determine the resources required to successfully complete the Hadoop jobs as per the user-specified SLA under various scenarios including node failures and multi-job executions. Our experimental results on three different Hadoop cloud clusters and across different workloads demonstrate the efficacy of our models and highlight their autoscaling capabilities.

}, author = {Anshul Gandhi and Sidhartha Thota and Parijat Dube and Andrzej Kochut and Li Zhang} } @conference {29, title = {Are sleep states effective in data centers?}, booktitle = {Third International Green Computing Conference (IGCC {\textquoteright}12)}, year = {2012}, abstract = {

While sleep states have existed for mobile devices and workstations for some time, these sleep states have not been incorporated into most of the servers in today{\textquoteright}s data centers. High setup times make data center administrators fearful of any form of dynamic power management, whereby servers are suspended or shut down when load drops. This general reluctance has stalled research into whether there might be some feasible sleep state (with sufficiently low setup overhead and/or sufficiently low power) that would actually be beneficial in data centers.

This paper investigates the regime of sleep states that would be advantageous in data centers. We consider the benefits of sleep states across three orthogonal dimensions: (i) the variability in the workload trace, (ii) the type of dynamic power management policy employed, and (iii) the size of the data center.

Our implementation results on a 24-server multi-tier test bed indicate that under many traces, sleep states greatly enhance dynamic power management. In fact, given the right sleep states, even a na{\i}ve policy that simply tries to match capacity with demand, can be very effective. By contrast, we characterize certain types of traces for which even the {\textquotedblleft}best{\textquotedblright} sleep state under consideration is ineffective. Our simulation results suggest that sleep states are even more beneficial for larger data centers. \ 

}, url = {http://www3.cs.stonybrook.edu/~anshul/igcc_2012.pdf}, author = {Anshul Gandhi and Mor Harchol-Balter and Michael Kozuch} } @article {27, title = {AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers}, journal = {ACM Transactions on Computer Systems (TOCS {\textquoteright}12)}, volume = {30}, year = {2012}, month = {11/2012}, abstract = {

Energy costs for data centers continue to rise, already exceeding $15 billion yearly. Sadly much of this power is\ wasted. Servers are only busy 10-30\% of the time on average, but they are often left on, while idle, utilizing 60\% or more of peak power when in the idle state.

We introduce a dynamic capacity management policy,\ AutoScale, that greatly reduces the number of servers needed in data centers driven by unpredictable, time-varying load, while meeting response time SLAs.\ AutoScale\ scales the data center capacity, adding or removing servers as needed.\ AutoScale\ has two key features: (i) it autonomically maintains just the right amount of spare capacity to handle bursts in the request rate; and (ii) it is robust not just to changes in the request rate of real-world traces, but also\ request size\ and\ server efficiency.

We evaluate our dynamic capacity management approach via implementation on a 38-server multitier data center, serving a web site of the type seen in Facebook or Amazon, with a key-value store workload. We demonstrate that\ AutoScale\ vastly improves upon existing dynamic capacity management policies with respect to meeting SLAs and robustness.

}, keywords = {Data centers, power management, resource provisioning}, doi = {10.1145/2382553.2382556}, url = {http://www3.cs.stonybrook.edu/~anshul/tocs12.pdf}, author = {Anshul Gandhi and Mor Harchol-Balter and Ram Raghunathan and Michael Kozuch} }