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The paper aims to capture the rack-level thermal dynamics in data center. It proposes the rack-level response experiments as well as transient Computational Fluid Dynamics (CFD) analysis to characterize the local thermal environment of the system.

A single sever simulator rack and its two neighboring racks with its cold and hot aisle containment have been modeled with known cold air supply temperature and flow rate for transient CFD analysis. The heat load was kept constant initially and varied case-to-case basis, which includes capturing the rack-level response with respect to changes in input. However, the response experiments on simulator rack were performed for 14 h by variation of server heat loads as step and ramp input.

The paper provides the detailed transient CFD analysis of data center racks. The local cold air flow rates and temperature at the vicinity of the racks showed significant effect due to changes in input. It was concluded that the rack-level dynamics impacts the thermal environment of data center and hence cannot be ignored.

The high computing devices and faster internet demands have led to major thermal management concerns for data center operators. To tackle this issue, capturing the system thermal dynamics is imperative. However, the system-level CFD analysis is computationally expensive. Therefore, this paper deals with the rack-level transient CFD study using commercial tool STAR CCM+.

This paper includes the modeling of the servers as a porous media as well as the multigrid method to enhance the computational speed. The successful implementation of this approach validated through experiments. This would help to establish a base for research in any type of data center.

This paper provides the porous media approach to model servers and multigrid method to enhance the computational speed. At the same time, the thought of characterizing the local dynamics at the vicinity of data center racks is unique.

This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied.

Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots.

Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center.

The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized.

The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption.

This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities.

The purpose of this paper is to review the available reduced order modeling approaches in the literature for predicting the flow and specially temperature fields inside data centers in terms of the involved design parameters.

This paper begins with a motivation for flow/thermal modeling needs for designing an energy‐efficient thermal management system in data centers. Recent studies on air velocity and temperature field simulations in data centers through computational fluid dynamics/heat transfer (CFD/HT) are reviewed. Meta‐modeling and reduced order modeling are tools to generate accurate and rapid surrogate models for a complex system. These tools, with a focus on low‐dimensional models of turbulent flows are reviewed. Reduced order modeling techniques based on turbulent coherent structures identification, in particular the proper orthogonal decomposition (POD) are explained and reviewed in more details. Then, the available approaches for rapid thermal modeling of data centers are reviewed. Finally, recent studies on generating POD‐based reduced order thermal models of data centers are reviewed and representative results are presented and compared for a case study.

It is concluded that low‐dimensional models are needed in order to predict the multi‐parameter dependent thermal behavior of data centers accurately and rapidly for design and control purposes. POD‐based techniques have shown great approximation for multi‐parameter thermal modeling of data centers. It is believed that wavelet‐based techniques due to the their ability to separate between coherent and incoherent structures – something that POD cannot do – can be considered as new promising tools for reduced order thermal modeling of complex electronic systems such as data centers

The paper reviews different numerical methods and provides the reader with some insight for reduced order thermal modeling of complex convective systems such as data centers.

Information technology (IT) systems are already ubiquitous, and their future growth is expected to drive the global economy for the next several decades. However, energy consumption by these systems is growing rapidly, and their sustained growth requires curbing the energy consumption, and the associated heat removal requirements. Currently, 20-50 percent of the incoming electrical power is used to meet the cooling demands of IT facilities. Careful co-optimization of electrical power and thermal management is essential for reducing energy consumption requirements of IT equipment. Such modeling based co-optimization is complicated by the presence of several decades of spatial and temporal scales. The purpose of this paper is to review recent approaches for handling these challenges.

In this paper, the authors illustrate the challenges and possible modeling approaches by considering three examples. The multi-scale modeling of chip level transient heating using a combination of Progressive Zoom-in, and proper orthogonal decomposition (POD) is an effective approach for chip level electrical/thermal co-design for mitigation of reliability concerns, such as Joule heating driven electromigration. In the second example, the authors will illustrate the optimal microfluidic thermal management of hot spots, and large background heat fluxes associated with future high-performance microprocessors. In the third example, data center facility level energy usage reduction through a transient measurements based POD modeling framework will be illustrated.

Through modeling based electrical/thermal co-design, dramatic savings in energy usage for cooling are possible.

The multi-scale nature of the thermal modeling of IT systems is an important challenge. This paper reviews some of the approaches employed to meet this challenge.

The functionality of personal mobile electronics continues to increase, in turn driving the demand for higher logic-to-memory bandwidth. However, the number of inputs/outputs supported by the current packaging technology is limited by the smallest achievable electrical line spacing, and the associated noise performance. Also, a growing trend in mobile systems is for the memory chips to be stacked to address the growing demand for memory bandwidth, which in turn gives rise to heat removal challenges. The glass interposer substrate is a promising packaging technology to address these emerging demands, because of its many advantages over the traditional organic substrate technology. However, glass has a fundamental limitation, namely low thermal conductivity (∼1 W/m K). The purpose of this paper is to quantify the thermal performance of glass interposer-based electronic packages by solving a multi-scale heat transfer problem for an interposer structure. Also, this paper studies the possible improvement in thermal performance by integrating a fluidic heat spreader or vapor chamber within the interposer.

This paper illustrates the multi-scale modeling approach applied for different components of the interposer, including Through Package Vias (TPVs) and copper traces. For geometrically intricate and repeating structures, such as interconnects and TPVs, the unit cell effective thermal conductivity approach was used. For non-repeating patterns, such as copper traces in redistribution layer, CAD drawing-based thermal resistance network analysis was used. At the end, the thermal performance of vapor chamber integrated within a glass interposer was estimated by using an enhanced effective thermal conductivity, calculated from the published thermal resistance data, in conjunction with the analytical expression for thermal resistance for a given geometry of the vapor chamber.

The limitations arising from the low thermal conductivity of glass can be addressed by using copper structures and vapor chamber technology.

A few reports can be found on thermal performance of glass interposers. However thermal characteristics of glass interposer with advanced cooling technology have not been reported.

Questions about the role and composition of the middle class have been examined and debated in the academy and in the political sphere for more than 100 years. In analyses of the Indian middle class specifically, two questions, both addressed by Diane Davis, seem to excite the most attention. The first has to do with the definition of a middle class, a term that has its origins in a very different social formation as well as its potentially mediating function in democracy. The second and more recent question has to do with what is variously called the “new” or “emerging” middle classes – in short, the middle classes of a liberalizing India.

The paper examines the succession management strategies and the preparation level of heirs in the context of family-owned educational institutions in Nepal.

Sixteen in-depth, semi-structured interviews with the institution's leader were conducted. Each interview was transcribed using content analysis. Several themes and new items emerged that define the institutional strategies in succession management.

The paper provides insight into the challenges of implementing effective succession management strategies. The identified themes are traits, processes, challenging aspects and effective plans. The study's findings show the lack of awareness about the importance of succession planning among the institution owners due to the availability of limited resources. The paper also provides some insights into how family ownership and management are done and the lack of formal processes in succession management strategies.

This paper offers readers the chance to think about succession planning strategies. Also, it adds value in their critical analysis of the succession plan. The study advised the learners to consider additional elements that can impact succession planning, such as experience, educational requirements and their desire to work. It will aid researchers in considering the societal perspective of the successor, which is also a significant worry.

It focuses on a specific context, private schools in Nepal, and examines the challenges they face in implementing succession management strategies. The paper tries to identify the approach that may reveal potential solutions that have not been considered. The paper aims to clearly articulate the unique contributions of the study and explain how it advances the existing literature on succession management.

Strategic Marketing, Marketing Management, Services Marketing.

MBA and Executive MBA.

The case talks about the inception and growth of OYO Rooms, a company that originally started as ORAVEL Stays Ltd. in 2012, as a platform for booking budget and premium accommodations, but graduated to become OYO Rooms, an online aggregator of hotels, with a unique business model of “managing the partial inventory of rooms” in hotels and offering a proposition of affordable, consistent, quality experience to business, leisure and pilgrim travellers. The company received rounds of funding from Greenoaks Capital, Lightspeed Ventures, Sequoia Capital and DSG Consumer Partners. Moreover, unlike its competitors, OYO adapted itself to the fast-changing consumer preference and grew at an enviable pace and by 2016, was present across 190 cities through a network of 6,500 hotels. However, OYO Rooms had to face a multitude of challenges both from the consumer and hotel owners’ ends, primarily service quality concerns from the customers and majorly concerns out of payment irregularities or non-abidance to written contracts from the hoteliers’ end. The dissatisfaction levels increased to an extent that experts started raising questions on the viability of the business. OYO was growing at an aggressive rate but breakeven point was yet to be achieved. Moreover, growing dissatisfaction and switching amongst its customers as well as hoteliers threatened the very existence of the model. The case allows the students to critically analyse the strategies of OYO for deliberation on whether the business model was sustainable in the long run. It also encourages the students to deliberate on the possible growth strategies for OYO as also on the service recovery strategies for OYO.

The case has been positioned around the following modules: industry analysis; value of a two-sided business model to both parties; sustainability of a unique business model, against the challenges that it faces; applying the VRIO framework (resource-based view); complaint handling and service recovery strategies; applying the Ansoff’s grid for possible growth options.

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CSS 11: Strategy.