Cooling provisioning and reliability implications for cost-efficient datacenters
Creators: name: Manousakis, Ioannis
Publisher: name: No Publisher Supplied
Year: 2018

description: Today and for the foreseeable future, demand for datacenter capacity is constantly  increasing worldwide, primarily driven by cloud computing. Large cloud providers  such as Amazon, Microsoft and Google are currently deploying multi-gigawatt facilities  annually. This staggering growth is costly. Just for datacenter infrastructure, excluding  IT equipment, energy and maintenance costs, these companies spend approximately ten  dollars to house each usefulWatt of IT equipment. Due to the huge monetary incentives,  cloud providers have made significant strides in reducing both capital (provisioning) and  operations costs of their datacenters primarily via the adaptation of cost-efficient cooling  technologies. However, significant opportunities still exist. Thus, this dissertation is  dedicated to cost-efficient methods for provisioning the datacenter cooling infrastructure  and the implications to server reliability.    In the  first part of this dissertation, we propose a method to reduce cooling costs by  underprovisioning the cooling infrastructure of datacenters. Cooling costs still represent  a signi cant capital and operational expense, mainly because cloud providers typically  provision their cooling infrastructure for the worst-case scenario (i.e., very high load  and outside temperature at the same time). Since extreme conditions occur very rarely,  it is cost efficient to provision for less capacity (under-provision) and manage the rare  instances with workload management policies. To determine the ideal type and amount  of cooling, we introduce CoolProvision, an optimization and simulation framework for  selecting the cheapest provisioning within performance constraints de ned by the datacenter  operator. CoolProvision leverages an abstract trace of the expected workload,  as well as cooling, performance, power, reliability, and cost models to explore the space  of potential provisionings. Using data from a real small free-cooled datacenter, our  results suggest that CoolProvision can reduce the capital cooling costs by up to 55%.  We extrapolate our experience and results to larger cloud datacenters as well.    Using cheap (and/or under-provisioned) cooling techniques (e.g., free-cooling) lowers  datacenter costs significantly, but may also expose servers to higher and more variable  temperatures and relative humidities. The question naturally arises whether these environmental  conditions have a significant impact on hardware component reliability. To  answer this question, we use data from nine hyperscale datacenters to study the impact  of environmental conditions on the reliability of server hardware, with a particular focus  on disk drives and free cooling. Based on this study, we derive and validate a new  model of disk lifetime as a function of environmental conditions in modern datacenters.  Furthermore, we quantify the tradeoffs between energy consumption, environmental  conditions, component reliability, and datacenter costs. Finally, based on our analyses  and model, we derive server and datacenter design lessons.    Our main observations are (1) relative humidity seems to have a dominant impact  on component failures; (2) disk failures increase significantly when operating at high  relative humidity, due to controller/adaptor malfunction; and (3) though higher relative  humidity increases component failures, software availability techniques can mask  them and enable free-cooled operation, resulting in significantly lower infrastructure  and energy costs that far outweigh the cost of the extra component failures.  In summary, the methods proposed in this dissertation allow datacenter operators  to reduce their capital costs by provisioning reduced cooling capacity with minimal  reliability and performance implications. The same methods can further be used to  reduce their operational costs and environmental footprint by operating the energy demanding  cooling equipment at lower levels and at the same time maintain similar  reliability and performance levels. Finally, the reliability models developed can be used  by the research community and industry to improve server reliability, understand the  implications of various datacenter environmental conditions and improve application  robustness.