@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} } @conference {28, title = {Saving Cash by Using Less Cache}, booktitle = {4th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud {\textquoteright}12)}, year = {2012}, abstract = {
Everyone loves a large caching tier in their multi-tier cloud-based web service because it both alleviates\ database load and provides lower request latencies. Even\ when load drops severely, administrators are reluctant to\ scale down their caching tier. This paper makes the case\ that (i) scaling down the caching tier is viable with re-\ spect to performance, and (ii) the savings are potentially\ huge; e.g., a 4x drop in load can result in 90\% savings in\ the caching tier size.
}, url = {http://www3.cs.stonybrook.edu/~anshul/hotcloud_2012.pdf}, author = {Timothy Zhu and Anshul Gandhi and Mor Harchol-Balter 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 {37, title = {The case for sleep states in servers}, booktitle = {4th Workshop on Power-Aware Computing (HotPower {\textquoteright}11)}, year = {2011}, abstract = {

While sleep states have existed for mobile devices and workstations for some time, these sleep states have largely not been incorporated into the servers in today{\textquoteright}s data centers.

Chip designers have been unmotivated to design sleep states because data center administrators haven{\textquoteright}t expressed any desire to have them. High setup times make 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 uses both experimentation and theory to investigate the regime of sleep states that should be advantageous in data centers. Implementation experiments involve a 24-server multi-tier test bed, serving a web site of the type seen in Facebook or Amazon with key-value workload and a range of hypothetical sleep states. Analytical modeling is used to understand the effect of scaling up to larger data centers. The goal of this research is to encourage data center administrators to consider dynamic power management and to spur chip designers to develop useful sleep states for servers. \ 

}, url = {http://www3.cs.stonybrook.edu/~anshul/hotpower_2011.pdf}, author = {Anshul Gandhi and Mor Harchol-Balter 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} } @article {43, title = {Optimality analysis of energy-performance trade-off for server farm management}, journal = {Performance Evaluation}, volume = {67}, year = {2010}, month = {11/2010}, chapter = {1155}, abstract = {

A central question in designing server farms today is how to efficiently provision the number of servers to extract the best performance under unpredictable demand patterns while not wasting energy. While one would like to turn servers off when they become idle to save energy, the large setup cost (both, in terms of setup time and energy penalty) needed to switch the server back on can adversely affect performance. The problem is made more complex by the fact that today{\textquoteright}s servers provide multiple sleep or standby states which trade off the setup cost with the power consumed while the server is {\textquoteleft}sleeping{\textquoteright}. With so many controls, finding the optimal server farm management policy is an almost intractable problem {\textemdash} How many servers should be on at any given time, how many should be off, and how many should be in some sleep state?

In this paper, we employ the popular metric of Energy-Response time Product (ERP) to capture the energy-performance trade-off, and present the first theoretical results on the optimality of server farm management policies. For a stationary demand pattern, we prove that there exists a very small, natural class of policies that always contains the optimal policy for a single server, and conjecture it to contain a near-optimal policy for multi-server systems. For time-varying demand patterns, we propose a simple, traffic-oblivious policy and provide analytical and empirical evidence for its near-optimality.

}, doi = {10.1016/j.peva.2010.08.009}, url = {http://www3.cs.stonybrook.edu/~anshul/perf_power_2010.pdf}, author = {Anshul Gandhi and Varun Gupta and Mor Harchol-Balter and Michael Kozuch} }