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数据中心在运转过程中需要消耗大量的电能。但是这其中的很大一部分电能都在工作负荷少时被空闲的服务器消耗了。动态供应技术通过在工作负荷少时，关掉不必要的服务器来节省这一部分的电能。在这篇文章中，我们研究未来工作负荷信息到底能给动态供应带来多大好处。特别地，对于有或没有未来工作负荷信息的两种情况，我们提出了在线的动态供应算法。我们首先发现了离线动态供应的最优解有一个很优美的结构，通过这个优美的结构我们可以以分而治之的方法完全确定离线动态供应的最优解。在这个基础之上我们设计了两个在线算法，它们的竞争比分别为2-α和e/(e - 1 + α),其中α表示标准化的预测未来窗口的长度。在这个预测未来窗口中，未来的工作负荷信息可以精确的获得。一个重要的发现是超出一个完整的预测未来窗口的未来工作负荷信息不会对动态供应的性能有任何提高。我们提出的在线算法是分散的，因此易于实现。最后，我们用真是数据中心的数据测试了我们的在线算法。 / 在设计在线算法的时候，我们利用了未来工作负荷信息。这是因为在很多的现代系统中，短期的未来工作信息可以被精确的估计。我们也测试了我们的算法在有预测噪声时候的性能，结果表明我们的算法在有噪声时，也能很好的工作。我们相信利用未来信息是设计在线算法的一个新的角度。在传统的在线算法设计过程中，我们通常不考虑未来输入信息。在这种情况下，许多在线问题有简单的最优的算法，但是这个最优算法的竞争比却很大。其实未来输入信息在很多在线问题中都能在一定程度上被精确预测，所以我们相信我们可以利用这些未来输入信息去设计竞争比较小的在线算法，这样设计的在线算法具有更多的应用优点，并在理论上也给予我们启发。 / Energy consumption represents a significant cost in data center operation. A large fraction of the energy however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy by turning of unnecessary servers. In this thesis we explore how much gain knowing future workload information can bring to dynamic pro-visioning. In particular we develop online dynamic provisioning solutions with and without future workload information available. We first reveal an elegant structure of the offline dynamic pro-visioning problem which allows us to characterize the optimal solution in a "divide-and-conquer" manner. We then exploit this insight to design two online algorithms with competitive ratios 2 - α and e/ (e - 1+ α), respectively where 0 ≤ α ≤ 1 is the normalized size of a look-ahead window in which future workload information is available. A fundamental observation is that future workload information beyond the full-size look-ahead window (corresponding to α =1) will not improve dynamic provisioning performance. Our algorithms are decentralized and easy to im-plement. We demonstrate their effectiveness in simulations using real-world traces. / When designing online algorithms, we utilize future input information because for many modern systems their short-term future inputs can be predicted by machine learning time-series analysis etc. We also test our algorithms in the presence of prediction errors in future workload information and the results show that our algorithms are robust to prediction errors. We believe that utilizing future information is a new and important degree of freedom in designing online algorithms. In traditional online algo¬rithm design future input information is not taken into account. Many online problems have online algorithms with optimal but large competitive ratios. Since future input information to some extent can be estimated accurately in many problems we believe that we should exploit such information in online algorithm design to achieve better competitive ratio and provide more competitive edge in both practice and theory. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Lu, Tan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 76-81). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Contributions --- p.4 / Chapter 1.3 --- Thesis Organization --- p.5 / Chapter 2 --- Related Work --- p.6 / Chapter 3 --- Problem Formulation --- p.10 / Chapter 3.1 --- Settings and Models --- p.10 / Chapter 3.2 --- Problem Formulation --- p.13 / Chapter 4 --- Optimal Solution and Offline Algorithm --- p.15 / Chapter 4.1 --- Structure of Optimal Solution --- p.15 / Chapter 4.2 --- Intuitions and Observations --- p.17 / Chapter 4.3 --- Offline Algorithm Achieving the Optimal Solution --- p.18 / Chapter 5 --- Online Dynamic Provisioning --- p.21 / Chapter 5.1 --- Dynamic Provisioning without FutureWorkload Information --- p.22 / Chapter 5.2 --- Dynamic Provisioning with Future Workload Information --- p.23 / Chapter 5.3 --- Adapting the Algorithms to Work with Discrete-Time Fluid Workload Model --- p.31 / Chapter 5.4 --- Extending to Case Where Servers Have Setup Time --- p.32 / Chapter 6 --- Experiments --- p.35 / Chapter 6.1 --- Settings --- p.35 / Chapter 6.2 --- Performance of the Proposed Online Algorithms --- p.38 / Chapter 6.3 --- Impact of Prediction Error --- p.39 / Chapter 6.4 --- Impact of Peak-to-Mean Ratio (PMR) --- p.40 / Chapter 6.5 --- Discussion --- p.40 / Chapter 6.6 --- Additional Experiments --- p.41 / Chapter 7 --- A New Degree of Freedom for Designing Online Algorithm --- p.44 / Chapter 7.1 --- The Lost Cow Problem --- p.45 / Chapter 7.2 --- Secretary Problem without Future Information --- p.47 / Chapter 7.3 --- Secretary Problem with Future Information --- p.48 / Chapter 7.4 --- Summary --- p.50 / Chapter 8 --- Conclusion --- p.51 / Chapter A --- Proof --- p.54 / Chapter A.1 --- Proof of Theorem 4.1.1 --- p.54 / Chapter A.2 --- Proof of Theorem 4.3.1 --- p.57 / Chapter A.3 --- Least idle vs last empty --- p.60 / Chapter A.4 --- Proof of Theorem 5.2.2 --- p.61 / Chapter A.5 --- Proof of Corollary 5.4.1 --- p.70 / Chapter A.6 --- Proof of Lemma 7.1.1 --- p.72 / Chapter A.7 --- Proof of Theorem 7.3.1 --- p.74 / Bibliography --- p.76

Tu, Jinlong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 92-95). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

The growing number of mobile devices and data-intensive applications pose unique challenges for wireless access networks as well as datacenter networks that enable modern cloud-based services. With the enormous increase in volume and complexity of traffic from applications such as video streaming and cloud computing, the interconnection networks have become a major performance bottleneck. In this thesis, we study algorithms and architectures spanning several layers of the networking protocol stack that enable and accelerate novel applications and that are easily deployable and scalable. The design of these algorithms and architectures is motivated by measurements and observations in real world or experimental testbeds. In the first part of this thesis, we address the challenge of wireless content delivery in crowded areas. We present the AMuSe system, whose objective is to enable scalable and adaptive WiFi multicast. AMuSe is based on accurate receiver feedback and incurs a small control overhead. This feedback information can be used by the multicast sender to optimize multicast service quality, e.g., by dynamically adjusting transmission bitrate. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes which periodically send information about the channel quality to the multicast sender. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe's feedback to optimally tune the physical layer multicast rate. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. We implemented the AMuSe system on the ORBIT testbed and evaluated its performance in large groups with approximately 200 WiFi nodes. Our extensive experiments demonstrate that AMuSe can provide accurate feedback in a dense multicast environment. It outperforms several alternatives even in the case of external interference and changing network conditions. Further, our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. As an example application, MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality. Next, we specifically focus on ensuring high Quality of Experience (QoE) for video streaming over WiFi multicast. We formulate the problem of joint adaptation of multicast transmission rate and video rate for ensuring high video QoE as a utility maximization problem and propose an online control algorithm called DYVR which is based on Lyapunov optimization techniques. We evaluated the performance of DYVR through analysis, simulations, and experiments using a testbed composed of Android devices and o the shelf APs. Our evaluation shows that DYVR can ensure high video rates while guaranteeing a low but acceptable number of segment losses, buffer underflows, and video rate switches. We leverage the lessons learnt from AMuSe for WiFi to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance. DyMo employs eMBMS for broadcasting instructions which indicate the reporting rates as a function of the observed Quality of Service (QoS) for each UE. This simple feedback mechanism collects very limited QoS reports which can be used for network optimization. We evaluated the performance of DyMo analytically and via simulations. DyMo infers the optimal eMBMS settings with extremely low overhead, while meeting strict QoS requirements under different UE mobility patterns and presence of network component failures. In the second part of the thesis, we study datacenter networks which are key enablers of the end-user applications such as video streaming and storage. Datacenter applications such as distributed file systems, one-to-many virtual machine migrations, and large-scale data processing involve bulk multicast flows. We propose a hardware and software system for enabling physical layer optical multicast in datacenter networks using passive optical splitters. We built a prototype and developed a simulation environment to evaluate the performance of the system for bulk multicasting. Our evaluation shows that the optical multicast architecture can achieve higher throughput and lower latency than IP multicast and peer-to-peer multicast schemes with lower switching energy consumption. Finally, we study the problem of congestion control in datacenter networks. Quantized Congestion Control (QCN), a switch-supported standard, utilizes direct multi-bit feedback from the network for hardware rate limiting. Although QCN has been shown to be fast-reacting and effective, being a Layer-2 technology limits its adoption in IP-routed Layer 3 datacenters. We address several design challenges to overcome QCN feedback's Layer- 2 limitation and use it to design window-based congestion control (QCN-CC) and load balancing (QCN-LB) schemes. Our extensive simulations, based on real world workloads, demonstrate the advantages of explicit, multi-bit congestion feedback, especially in a typical environment where intra-datacenter traffic with short Round Trip Times (RTT: tens of s) run in conjunction with web-facing traffic with long RTTs (tens of milliseconds).

Research Project (M.B.A.) - Simon Fraser University, 2005. / Research Project (Faculty of Business Administration) / Simon Fraser University. Senior supervisor : Dr. Ed Bukszar. EMBA Program. Also issued in digital format and available on the World Wide Web.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Yogendra K. Joshi; Committee Member: Mostafa Ghiaasiaan; Committee Member: Sheldon Jeter. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph.D.)--Ohio University, June, 2007. / Title from PDF t.p. Includes bibliographical references.

Thesis (Ph.D.)--Mechanical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Dr. Yogendra Joshi; Committee Member: Dr. Ruchi Choudhary; Committee Member: Dr. Srinivas Garimella

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Dr. Yogendra Joshi; Committee Member: Dr. S. Mostafa Ghiaasiaan; Committee Member: Dr. Sheldon Jeter. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (MBA)--Stellenbosch University, 2002. / ENGLISH ABSTRACT: In this case study, the writer proposes a methodology for the re-architecting of Telkom’s Information Technology data centres to achieve business alignment and improve IT asset efficiency. The methodology advocated begins with the defining of Telkom’s high-level business domains and maps against these the current deployment of IT infrastructure in the company’s data centres. Next, a Future Mode of Operation (FMO) architecture is proposed, together with the establishment of deployment principles and guidelines to ensure that ‘best practices’ are leveraged in future IT infrastructure deployment. After addressing implementations considerations, the writer discusses the benefits of implementing the FMO architecture, and suggests some success measures. The work is concluded with recommendations for further development. / AFRIKAANSE OPSOMMING: In hierdie gevallestudie het die skrywer 'n metodologie voorgestel om Telkom se Inligtingstegnologie (IT) datasentrums te herskep ten einde dit met die besigheidsprosesse in lyn te bring en die batedoeltreffendheid daarvan te verbeter. Die metodologie, soos voorgestel, begin met die definiëring van Telkom se hoëvlak besigheidsdomeine en karteer hierteenoor die huidige ontplooïng van die IT infrastruktuur in die maatskappy se datasentrums. Hierna, word 'n Toekomstige Modus Operandi (TMO) argitektuur voorgestel tesame met die daarstelling van ontplooïngsbeginsels en riglyne ten einde te verseker dat die “beste praktyk” beginsels ingebou word in 'n toekomstige IT infrastruktuur. Nadat implementeringsoorwegings aangespreek is, bespreek die skrywer die voordele van die TMO argitektuur en stel seker suksesmaatstawwe voor. Die werkstuk word afgesluit by wyse van aanbevelings rakende verdere ontwikkeling.