Practical Data Center Research Articles

Distributionally robust planning for data center park considering operational economy and reliability

Digitization has dramatically facilitated the development of internet data centers (IDCs), which have become necessary demand response resources for distribution networks. To improve equipment utilization, integrated coordination of IDCs, photovoltaics, energy storage systems, and flexible substations is essential. This also ensures power supply reliability and the operational economy of IDC park. This paper proposes a distributionally robust planning method for an IDC park considering operational economy and reliability. First, a coordinated planning model of an IDC park is proposed with multiple resources, in which the workload dispatch of the IDC is incorporated under fault conditions. The reliability index of the IDC park is designed to quantify its reliability cost. Then, considering the fluctuation of distributed generators and workload, a distributionally robust planning model is established. A planning scheme for the IDC park under the worst scenario probability distribution is obtained. Furthermore, the influence of uncertainties on the planning scheme is analyzed. Finally, a practical data center park is used for case studies. The established planning scheme effectively balances the operational economy, reliability, and planning conservatism.

Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers

The widespread deployment of data centers has contributed to a dramatic increase in energy consumption and carbon emissions. To solve this problem, this study proposes a distributed multi-energy system (DMES) driven by solar, wind, geothermal and natural gas and its life-cycle-based multi-objective optimization model considered energy, economy, and environment. Moreover, a novel operation strategy based on load characteristics is presented for energy flow allocation and a multi-objective grasshopper optimization algorithm is improved for model solving. Case studies are conducted on a practical data center in Qinghai, China. Based on the DMES capacity configuration and scheduling results, the cooling, heating and power supply can satisfy the demand of the data center while generating over 30% of the power from renewable sources. In addition, carbon tax is considered in the optimization model, which accounts for 4% of the life-cycle cost. Furthermore, the sensitivity of carbon tax, equipment lifespan and predicted mean vote is analyzed, obtaining that these factors have some influence on the performance of the system. Finally, the uncertainty impact on system planning is studied, concluding that the designed system exhibits high robustness levels when renewable energy forecast error is less than 20% or load forecast error is less than 10%.

Why Some Like It Loud

The common practice of power infrastructure oversubscription in data centers exposes dangerous vulnerabilities to well-timed power attacks (i.e., maliciously timed power loads), possibly creating outages and resulting in multimillion-dollar losses. In this paper, we focus on the emerging threat of power attacks in a multi-tenant data center, where a malicious tenant (i.e., attacker) aims at compromising the data center availability by launching power attacks and overloading the power capacity. We discover a novel acoustic side channel resulting from servers' cooling fan noise, which can help the attacker time power attacks at the moments when benign tenants' power usage is high. Concretely, we exploit the acoustic side channel by: (1) employing a high-pass filter to filter out the air conditioner's noise; (2) applying non-negative matrix factorization with sparsity constraint to demix the received aggregate noise and detect periods of high power usage by benign tenants; and (3) designing a state machine to guide power attacks. We run experiments in a practical data center environment as well as simulation studies, and demonstrate that the acoustic side channel can assist the attacker with detecting more than 50% of all attack opportunities, representing state-of-the-art timing accuracy.

Why Some Like It Loud

The common practice of power infrastructure oversubscription in data centers exposes dangerous vulnerabilities to well-timed power attacks (i.e., maliciously timed power loads to overload the infrastructure capacity), possibly creating outages and resulting in multimillion-dollar losses. In this paper, we focus on the emerging threat of power attacks in a multi-tenant data center, where a malicious tenant (i.e., attacker) aims at compromising the data center availability through power attacks. We discover a novel acoustic side channel resulting from servers' cooling fan noise, which can help the attacker time power attacks at the moments when benign tenants' power usage is high. Concretely, we exploit the acoustic side channel by: (1) employing a high-pass filter to filter out the air conditioner's noise; (2) applying non-negative matrix factorization with sparsity constraint to demix the received aggregate noise and detect periods of high power usage by benign tenants; and (3) designing a state machine to guide power attacks. We run experiments in a practical data center environment as well as simulation studies, and demonstrate that the acoustic side channel can assist the attacker with detecting more than 50% of all attack opportunities, representing state-of-the-art timing accuracy.

Toward A Scalable, Fault-Tolerant, High-Performance Optical Data Center Architecture

Optical data center networks (DCNs) are becoming increasingly attractive due to their technological strengths compared with the traditional electrical networks. However, existing optical DCNs are either hard to scale, vulnerable to single point of failure, or provide limited network bisection bandwidth for many practical data center workloads. To this end, we present WaveCube, a scalable, fault-tolerant, high-performance optical DCN architecture. To scale, WaveCube removes MEMS, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> a potential bottleneck, from its design. WaveCube is fault-tolerant, since it does not have single point of failure and there are multiple node-disjoint parallel paths between any pair of top-of-rack switches. WaveCube delivers high performance by exploiting multi-pathing and dynamic link bandwidth along the path. For example, our evaluation results show that, in terms of network bisection bandwidth, WaveCube outperforms prior optical DCNs by up to 400% and is 70%-85% of the ideal non-blocking network (ı.e., theoretical upper bound) under both realistic and synthetic traffic patterns. WaveCube's performance degrades gracefully under failures-it drops 20% even with 20% links cut. WaveCube also holds promise in practice-its wiring complexity is orders of magnitude lower than Fattree, BCube, and c-Through at scale, and its power consumption is 35% of them.

Towards Interactive Medical Content Delivery Between Simulated Body Sensor Networks and Practical Data Center.

With the development of IoT (Internet of Thing), big data analysis and cloud computing, traditional medical information system integrates with these new technologies. The establishment of cloud-based smart healthcare application gets more and more attention. In this paper, semi-physical simulation technology is applied to cloud-based smart healthcare system. The Body sensor network (BSN) of system transmit has two ways of data collection and transmission. The one is using practical BSN to collect data and transmitting it to the data center. The other is transmitting real medical data to practical data center by simulating BSN. In order to transmit real medical data to practical data center by simulating BSN under semi-physical simulation environment, this paper designs an OPNET packet structure, defines a gateway node model between simulating BSN and practical data center and builds a custom protocol stack. Moreover, this paper conducts a large amount of simulation on the real data transmission through simulation network connecting with practical network. The simulation result can provides a reference for parameter settings of fully practical network and reduces the cost of devices and personnel involved.

Analysis of transient and hysteresis behavior of cross-flow heat exchangers under variable fluid mass flow rate for data center cooling applications

Effective thermal management of data centers is an important aspect of reducing the energy required for the reliable operation of data processing and communications equipment. Liquid and hybrid (air/liquid) cooling approaches are becoming more widely used in today's large and complex data center facilities. Examples of these approaches include rear door heat exchangers, in-row and overhead coolers and direct liquid cooled servers. Heat exchangers are primary components of liquid and hybrid cooling systems, and the effectiveness of a heat exchanger strongly influences the thermal performance of a cooling system. Characterizing and modeling the dynamic behavior of heat exchangers is important for the design of cooling systems, especially for control strategies to improve energy efficiency. In this study, a dynamic thermal model is solved numerically in order to predict the transient response of an unmixed–unmixed crossflow heat exchanger, of the type that is widely used in data center cooling equipment. The transient response to step and ramp changes in the mass flow rate of both the hot and cold fluid is investigated. Five model parameters are varied over specific ranges to characterize the transient performance. The parameter range investigated is based on available heat exchanger data. The thermal response to the magnitude, time period and initial and final conditions of the transient input functions is studied in detail. Also, the hysteresis associated with the fluid mass flow rate variation is investigated. The modeling results and performance data are used to analyze specific dynamic performance of heat exchangers used in practical data center cooling applications.