@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 {23, title = {SOFTScale: Stealing Opportunistically For Transient Scaling}, booktitle = {13th International Middleware Conference (Middleware {\textquoteright}12)}, year = {2012}, abstract = {

Dynamic capacity provisioning is a well studied approach to handling gradual changes in data center load. However, abrupt spikes in load are still problematic in that the work in system rises very quickly during the setup time needed to turn on additional capacity. Performance can be severely affected even if it takes only 5 seconds to bring additional capacity online.

In this paper, we propose SOFTScale, an approach to handling load spikes in multi-tier data centers without having to over-provision resources. SOFTScale works by opportunistically stealing resources from other tiers to alleviate the bottleneck tier, even when the tiers are carefully provisioned at capacity. SOFTScale is especially useful during the transient overload periods when additional capacity is being brought online.

Via implementation on a 28-server multi-tier testbed, we investigate a range of possible load spikes, including an artificial doubling or tripling of load, as well as large spikes in real traces. We find that SOFTScale can meet our stringent response time Service Level Agreement goal of 500ms without using any additional resources even under some extreme load spikes that would normally cause the system (without SOFTScale) to exhibit response times as high as 96 seconds.

}, keywords = {Data centers, load spikes, power management, resource provisioning}, url = {http://www3.cs.stonybrook.edu/~anshul/middleware12.pdf}, author = {Anshul Gandhi and Timothy Zhu and Mor Harchol-Balter and Michael Kozuch} } @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} } @article {42, title = {Server farms with setup costs}, journal = {Performance Evaluation}, volume = {67}, year = {2010}, month = {11/2010}, chapter = {1123}, abstract = {

In this paper we consider server farms with a setup cost. This model is common in manufacturing systems and data centers, where there is a cost to turn servers on. Setup costs always take the form of a time delay, and sometimes there is additionally a power penalty, as in the case of data centers. Any server can be either on, off, or in setup mode. While prior work has analyzed single servers with setup costs, no analytical results are known for multi-server systems. In this paper, we derive the first closed-form solutions and approximations for the mean response time and mean power consumption in server farms with setup costs. We also analyze variants of server farms with setup, such as server farm models with staggered boot up of servers, where at most one server can be in setup mode at a time, or server farms with an infinite number of servers. For some variants, we find that the distribution of response time can be decomposed into the sum of response time for a server farm without setup and the setup time. Finally, we apply our analysis to data centers, where both response time and power consumption are key metrics. Here we analyze policy design questions such as whether it pays to turn servers off when they are idle, whether staggered boot up helps, how to optimally mix policies, and other questions related to the optimal data center size.

}, keywords = {Data centers, M/M/k, power management, Setup times, Staggered boot up}, doi = {10.1016/j.peva.2010.07.004}, url = {http://www3.cs.stonybrook.edu/~anshul/perf_mmk_2010.pdf}, author = {Anshul Gandhi and Mor Harchol-Balter and Ivo Adan} }