Barack Obama assumed office at a time when the discourse of American exceptionalism had become dysfunctional in international affairs even though it still had broad appeal among the American people Obama tried to move beyond exceptionalism and chart a new course for American foreign policy in a series of speeches during 2008 and 2009 There is little evidence that Americans paid much attention to these speeches, but if they had, they likely would have seen them as undermining American strategic interests Obama might mitigate this dilemma by trying to redefine exceptionalism, focusing on its challenges and responsibilities rather than its gifts, This approach would involve recovering a recessive strain in the history of American public discourse

The U.S. and the world

A review of thermal management and innovative cooling strategies for data center

Network function virtualization (NFV) brings great conveniences and benefits for the enterprises to outsource their network functions to the cloud datacenter. In this paper, we address the virtual network function (VNF) placement problem in cloud datacenter considering users’ service function chain requests (SFCRs). To optimize the resource utilization, we take two less-considered factors into consideration, which are the time-varying workloads, and the basic resource consumptions (BRCs) when instantiating VNFs in physical machines (PMs). Then the VNF placement problem is formulated as an integer linear programming (ILP) model with the aim of minimizing the number of used PMs. Afterwards, a Two-StAge heurisTic solution (T-SAT) is designed to solve the ILP. T-SAT consists of a correlation-based greedy algorithm for SFCR mapping (first stage) and a further adjustment algorithm for virtual network function requests (VNFRs) in each SFCR (second stage). Finally, we evaluate T-SAT with the artificial data we compose with Gaussian function and trace data derived from Google's datacenters. The simulation results demonstrate that the number of used PMs derived by T-SAT is near to the optimal results and much smaller than the benchmarks. Besides, it improves the network resource utilization significantly.

Virtual Network Function Placement Considering Resource Optimization and SFC Requests in Cloud Datacenter

Data center (DC) plays an important role to support services, such as e-commerce and cloud computing. The resulting energy consumption from this growing market has drawn significant attention, and noticeably almost half of the energy cost is used to cool the DC to a particular temperature. It is thus an critical operational challenge to curb the cooling energy cost without sacrificing the thermal safety of a DC. The existing solutions typically follow a two-step approach, in which the system is first modeled based on expert knowledge and, thus, the operational actions are determined with heuristics and/or best practices. These approaches are often hard to generalize and might result in suboptimal performances due to intrinsic model errors for large-scale systems. In this paper, we propose optimizing the DC cooling control via the emerging deep reinforcement learning (DRL) framework. Compared to the existing approaches, our solution lends itself an end-to-end cooling control algorithm (CCA) via an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm, in which an evaluation network is trained to predict the DC energy cost along with resulting cooling effects, and a policy network is trained to gauge optimized control settings. Moreover, we introduce a de-underestimation (DUE) validation mechanism for the critic network to reduce the potential underestimation of the risk caused by neural approximation. Our proposed algorithm is evaluated on an EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. The resulting numerical results show that the proposed CCA can achieve up to 11% cooling cost reduction on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study of conservative nature, the proposed algorithm can achieve about 15% cooling energy savings on the NSCC data trace. Our pioneering approach can shed new light on the application of DRL to optimize and automate DC operations and management, potentially revolutionizing digital infrastructure management with intelligence.

Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

Software-Defined Networking not only addresses the shortcoming of traditional network technologies in dealing with frequent and immediate changes in cloud data centers but also made network resource management open and innovation-friendly To further accelerate the innovation pace, accessible and easy-to-learn testbeds are required which estimate and measure the performance of network and host capacity provisioning approaches simultaneously within a data center This is a challenging task and is often costly if accomplished in a physical environment Thus, a lightweight and scalable simulation environment is necessary to evaluate the network allocation capacity policies while avoiding such a complicated and expensive facility This paper introduces CloudSimSDN, a simulation framework for SDN-enabled cloud environments based on CloudSim This paper develops and presents the overall architecture and features of the framework and provides several use cases Moreover, we empirically validate the accuracy and effectiveness of CloudSimSDN through a number of simulations of a cloud-based three-tier web application

CloudSimSDN: modeling and simulation of software-defined cloud data centers

Data center power optimization has recently received a great deal of research attention. For example, server consolidation has been demonstrated as one of the most effective energy saving methodologies. Likewise, traffic consolidation has also been recently proposed to save energy for data center networks (DCNs). However, current research on data center power optimization focuses on servers and DCN separately. As a result, the optimization results are often inferior, because server consolidation without considering the DCN may cause traffic congestion and thus degraded network performance. On the other hand, server consolidation may change the DCN topology, allowing new opportunities for energy savings. In this paper, we propose PowerNetS, a power optimization strategy that leverages workload correlation analysis to jointly minimize the total power consumption of servers and the DCN. The design of PowerNetS is based on the key observations that the workloads of different servers and DCN traffic flows do not peak at exactly the same time. Thus, more energy savings can be achieved if the workload correlations are considered in server and traffic consolidations. In addition, PowerNetS considers the DCN topology during server consolidation, which leads to less inter-server traffic and thus more energy savings and shorter network delays. We implement PowerNetS on a hardware testbed composed of 10 virtual switches configured with a production 48port OpenFlow switch and 6 servers. Our empirical results with Wikipedia, Yahoo!, and IBM traces demonstrate that PowerNetS can save up to 51.6% of energy for a data center. PowerNetS also outperforms two state-of-the-art baselines by 44.3% and 15.8% on energy savings, respectively. Our simulation results with 72 switches and 122 servers also show the superior energy efficiency of PowerNetS over the baselines.

http://www2.ece.ohio-state.edu/~xwang/papers/infocom14.pdf

Joint Power Optimization of Data Center Network and Servers with Correlation Analysis

Data centers are seeking more efficient cooling techniques to reduce their operating expenses, because cooling can account for 30-40% of the power consumption of a data center. Recently, liquid cooling has emerged as a promising alternative to traditional air cooling, because it can help eliminate undesired air recirculation. Another emerging technology is free air cooling, which saves chiller power by utilizing outside cold air for cooling. Some existing data centers have already started to adopt both liquid and free air cooling techniques for significantly improved cooling efficiency and more data centers are expected to follow. In this paper, we propose SmartCool, a power optimization scheme that effectively coordinates different cooling techniques and dynamically manages workload allocation for jointly optimized cooling and server power. In sharp contrast to the existing work that addresses different cooling techniques in an isolated manner, SmartCool systematically formulates the integration of different cooling systems as a constrained optimization problem. Furthermore, since geo-distributed data centers have different ambient temperatures, SmartCool dynamically dispatches the incoming requests among a network of data centers with heterogeneous cooling systems to best leverage the high efficiency of free cooling. A light-weight heuristic algorithm is proposed to achieve a near-optimal solution with a low run-time overhead. We evaluate SmartCool both in simulation and on a hardware testbed. The results show that SmartCool outperforms two state-of-the-art baselines by having a 38% more power savings.

/pdf/coordinating-liquid-and-free-air-cooling-with-workload-118gg081bb.pdf

Coordinating Liquid and Free Air Cooling with Workload Allocation for Data Center Power Minimization

Data centers have been growing rapidly in recent years to meet the surging demand of cloud services. However, the expanding scale and powerful servers generate a great amount of heat, resulting in significant cooling costs. A trend in modern data centers is to raise the temperature and maintain all servers in a relatively hot environment. While this can save on cooling costs given benign workloads running in servers, the hot environment increases the risk of cooling failure. In this paper, we unveil a new vulnerability of existing data centers with aggressive cooling energy saving policies. Such a vulnerability might be exploited to launch thermal attacks that could severely worsen the thermal conditions in a data center. Specifically, we conduct thermal measurements and uncover effective thermal attack vectors at the server, rack, and data center levels. We also present damage assessments of thermal attacks. Our results demonstrate that thermal attacks can (1) largely increase the temperature of victim servers degrading their performance and reliability, (2) negatively impact on thermal conditions of neighboring servers causing local hotspots, (3) raise the cooling cost, and (4) even lead to cooling failures. Finally, we propose effective defenses to prevent thermal attacks from becoming a serious security threat to data centers.

Reduced Cooling Redundancy: A New Security Vulnerability in a Hot Data Center.

Data center power minimization with placement optimization of liquid-cooled servers and free air cooling ☆

Recently, a lot of research efforts have been made to optimize the large amounts of energy consumed by different devices in data centers, including servers, cooling, and the data center network (DCN). Unfortunately, current research addresses these devices mostly in a separate manner, leading to inferior optimization results. This paper proposes PowerNetS, a power optimization framework that coordinates servers and DCN, as well as cooling, for minimized power consumption of a data center. PowerNetS leverages workload correlation analysis for more energy savings during server and traffic consolidations. More importantly, PowerNetS tries to change the DCN topology during server consolidation, in order to have more intra-server traffic and shorter flows that go through fewer switches. For example, two virtual machines previously located on two different servers can now be migrated to the same server, so that the flow between them no longer needs to use switches, which allows more devices to sleep for energy savings without network performance degradation. PowerNetS has been implemented on a physical testbed with 6 servers and 10 virtual switches that are configured using a production 48-port OpenFlow switch. Our evaluation with Wikipedia, Yahoo!, and IBM traces shows that PowerNetS can save up to 51.6% of energy by coordinating servers and DCN, which is 44.3% and 15.8% more than two state-of-the-art baselines, respectively. By further coordinating with cooling to utilize different cooling efficiencies at different locations within a data center, PowerNetS can achieve 8.8%–14.6% additional energy savings.

Li Li

Papers

Joint Power Optimization of Data Center Network and Servers with Correlation Analysis

Coordinating Liquid and Free Air Cooling with Workload Allocation for Data Center Power Minimization

Reduced Cooling Redundancy: A New Security Vulnerability in a Hot Data Center.

Data center power minimization with placement optimization of liquid-cooled servers and free air cooling ☆

PowerNetS: Coordinating Data Center Network With Servers and Cooling for Power Optimization