@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} } @conference {39, title = {Distributed, Robust Auto-Scaling Policies for Power Management in Compute Intensive Server Farms}, booktitle = {Sixth Open Cirrus Summit (OCS {\textquoteright}11)}, year = {2011}, abstract = {

Server farms today often over-provision resources to handle peak demand, resulting in an excessive waste of power. Ideally, server farm capacity should be dynamically adjusted based on the incoming demand. However, the unpredictable and time-varying nature of customer demands makes it very difficult to efficiently scale capacity in server farms. The problem is further exacerbated by the large setup time needed to increase capacity, which can adversely impact response times as well as utilize additional power.

In this paper, we present the design and implementation of a class of Distributed and Robust Auto-Scaling policies (DRA S policies), for power management in compute intensive server farms. Results indicate that the DRAS policies dynamically adjust server farm capacity without requiring any prediction of the future load, or any feedback control. Implementation results on a 21 server test-bed show that the DRAS policies provide near-optimal response time while lowering power consumption by about 30\% when compared to static provisioning policies that employ a fixed number of servers.

}, url = {http://www3.cs.stonybrook.edu/~anshul/opencirrus_2011.pdf}, author = {Anshul Gandhi and Mor Harchol-Balter and Ram Raghunathan and Michael Kozuch} }