A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit <a rel="external noopener" href="https://core.ac.uk/download/pdf/38026649.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s77bfh66ena2znlnuhuruo464a" style="color: black;">2013 IEEE 33rd International Conference on Distributed Computing Systems</a>
To tackle soaring power costs, significant carbon emission and unexpected power outage, Cloud Service Providers (CSPs) typically equip their Datacenters with a Power Supply System (DPSS) nurtured by multiple sources: (1) smart grid with time-varying electricity prices, (2) uninterrupted power supply (UPS), and (3) renewable energy with intermittent and uncertain supply. It remains a significant challenge how to operate among multiple power supply sources in a complementary manner, to deliver<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdcs.2013.59">doi:10.1109/icdcs.2013.59</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icdcs/DengLJW13.html">dblp:conf/icdcs/DengLJW13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2owahcqpj5aq5kdyyfj4pud6vm">fatcat:2owahcqpj5aq5kdyyfj4pud6vm</a> </span>
more »... iable energy to datacenter users with arbitrary demand over time, while minimizing a CSP's operation cost over the long run. This paper proposes an efficient, online control algorithm for DPSS, SmartDPSS, based on the two-timescale Lyapunov optimization techniques. Without requiring a priori knowledge of system statistics, SmartDPSS allows CSPs to make online decisions on how much power demand, including delay-sensitive demand and delay-tolerant demand, to serve at each time, the amount of power to purchase from the long-term-ahead and realtime grid markets, and charging and discharging of UPS over time, in order to fully leverage the available renewable energy and time-varying prices from the grid markets, for minimum operational cost. We thoroughly analyze the performance of our online control algorithm with rigorous theoretical analysis. We also demonstrate its optimality in terms of operational cost, demand service delay, datacenter availability, system robustness and scalability, using extensive simulations based on one-month worth of traces from live power systems.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171130062643/https://core.ac.uk/download/pdf/38026649.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7c/4b/7c4b8aa0bc26936ea00a452994306b1921d18034.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icdcs.2013.59"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>