@conference {80, title = {Minimizing Electricity Cost for Geo-Distributed Interactive Services with Tail Latency Constraint}, year = {2016}, abstract = {

Cross-border data movement has become increas- ingly more costly, and even been prohibited due to data sovereignty requirements. Consequently, geo-distributed inter- active services, which rely on geographically distributed data sets, is quickly emerging as an important class of workloads in data centers and resulting in soaring electricity costs. While numerous geographic load balancing (GLB) techniques exist to exploit differences in electricity prices for cost savings, they do not apply to emerging geo-distributed interactive services due to two major limitations. First, they assume that each request is processed only in one data center, whereas each geo- distributed interactive request must be processed at multiple data centers simultaneously. Second, they primarily focus on meeting average latency constraints, whereas tail latencies (i.e., high- percentile latencies) are more suitable to ensure a consistently good user experience. In this paper, we make an early effort to optimize GLB decisions for geo-distributed interactive services, exploiting spatial diversity of electricity prices to minimize the total electricity cost while meeting a tail latency constraint. Our solution employs a novel data-driven approach to determine the tail latency performance for different GLB decisions, by profiling the network latency and data center latency at a low complexity. We run trace-based discrete-event simulations to validate our design, showing that it can reduce the electricity cost by more than 7\% while meeting the tail latency constraint compared to the performance-aware but cost-oblivious approach.

}, author = {Mohammad A. Islam and Anshul Gandhi and Shaolei Ren} } @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 {60, title = {The Economics of Long-Term Digital Storage}, booktitle = {The Memory of the World in the Digital Age: Digitization and Preservation}, year = {2012}, month = {09/2012}, publisher = {United Nations Educational, Scientific and Cultural Organization (UNESCO)}, organization = {United Nations Educational, Scientific and Cultural Organization (UNESCO)}, address = {Vancouver, Canada}, abstract = {

Paper as the medium for the world{\textquoteright}s memory has one great advantage; it survives benign neglect well. Bits, on the other hand, need continual care, and thus a con- tinual flow of money. A Blue Ribbon panel described economic sustainability as the major issue facing long- term digital preservation. This is despite Kryder{\textquoteright}s Law, the 30-year history of the cost of digital storage media dropping exponentially. If economics are the major con- cern even when Kryder{\textquoteright}s Law holds, what will happen if it slows or stops? We present evidence that it will, and some simulations of the impact on digital preservation costs.

}, url = {http://www.fsl.cs.stonybrook.edu/docs/unesco12/UNESCO2012-storage-econ.pdf}, author = {David S. H. Rosenthal and Daniel C. Rosenthal and Ethan L. Miller and Ian F. Adams and Mark W. Storer and Erez Zadok} } @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} }