Cloudward bound

In this paper, we tackle challenges in migrating enterprise services into hybrid cloud-based deployments, where enterprise operations are partly hosted on-premise and partly in the cloud. Such hybrid architectures enable enterprises to benefit from cloud-based architectures, while honoring application performance requirements, and privacy restrictions on what services may be migrated to the cloud. We make several contributions. First, we highlight the complexity inherent in enterprise applications today in terms of their multi-tiered nature, large number of application components, and interdependencies. Second, we have developed a model to explore the benefits of a hybrid migration approach. Our model takes into account enterprise-specific constraints, cost savings, and increased transaction delays and wide-area communication costs that may result from the migration. Evaluations based on real enterprise applications and Azure-based cloud deployments show the benefits of a hybrid migration approach, and the importance of planning which components to migrate. Third, we shed insight on security policies associated with enterprise applications in data centers. We articulate the importance of ensuring assurable reconfiguration of security policies as enterprise applications are migrated to the cloud. We present algorithms to achieve this goal, and demonstrate their efficacy on realistic migration scenarios.

An algorithm for network and data-aware placement of multi-tier applications in cloud data centers

Applications in modern data centers have a wide variety of resource requirements along the four main dimensions of computing, memory, storage, and networking. Data centers must manage these resources separately for each dimension, resulting in highly inefficient allocation of precious resources or even disastrous schemes that contribute to low utilization or over-provisioning of resources. However, concerted efforts to jointly optimize all types of resources in the same framework appears insurmountable due to an exponentially increasing complexity linked to the thousands of fine-grained resources such as CPUs, memory blocks, disk blocks, switches, etc., which results in an astronomically large number of possible resource combinations. In this paper, we present a novel multi-resource scheduling approach that keeps the complexity to a minimum while it provides efficient resource utilization by keeping a sufficient level of granularity. Our scheme is based on the idea of defining a new finest-grain schedulable unit called multi-resource schedulable unit (MRSU) that will be used by the scheduler to allocate resources to applications in the data center. The problem of MRSU-based scheduling is modeled with an optimization problem and several solutions are proposed. Our results show a significant performance improvement of our technique over conventional static schedulers based on virtual machines (VMs) in terms of saved over-allocation and application satisfaction.

With the fast development of information technology (IT) industry, the demand and market volume for off-line power supplies keep increasing, especially those for telecommunication, computer servers and data centers. As the capital expenditure was measured by the square footage occupied rather than power consumption, the development of high power density converter system is of greater interesting. The rising energy prices have resulted in the cost of power and cooling exceeding the purchase cost in less than two years. Therefore, highly efficient power conversion is required for the power converter system. Generally, the power supply unit (PSU) for power distribution system (PDS) in data center and telecom are the standard two-stage approach which normally consists of power factor correction (PFC) circuit and isolated DC-DC converter. The two-stage power conversion has demonstrated excellent performance and high reliability, since the design can be optimized for each stage. However, limitations to prevent the existing two-stage PSU to fulfill future requirements for the PDS in data center and telecom applications are revealed, and a very promising and fundamentally different approach with the single-stage isolated converter is proposed in this dissertation. The development of single-stage converters with the option of placing the energy storage outside of the PSU creates new degrees of freedom regarding e.g. simplified rectifier racks in telecom and data center. This provides tangible benefits in the form of space saving, better airflow for power unit in rectifier racks and improved lifespan. The three-phase isolated buck matrix-type rectifier, capable of achieving high power density and high efficiency, is identified as an excellent candidate for the medium power level (5 kW~10 kW) single-stage power supply design. Nevertheless, the matrix-type rectifiers are known for their relatively complex modulation and commutation techniques, and lack of ride-through capability such as the stringent case of one phase loss operation. This dissertation work provides comprehensive study on the commutation method and modulation scheme design for the isolated buck matrix-type rectifier. It aims to analyze the operation principle of the rectifier and propose viable modulation and commutation schemes for this rectifier under both three-phase and single-phase operation. The method is verified by the hardware experiments of the PSUs with high efficiency (> 98%) and high power density (> 70 W/in3 ) for 54 V and 380 VDC applications. The prototypes demonstrated in the experiments show the effectiveness of the proposed modulation and commutation schemes for industry.

With the fast development of information technology (IT) industry, the demand and market volume for off-line power supplies keep increasing, especially those for telecommunication, computer servers and data centers. As the capital expenditure was measured by the square footage occupied rather than power consumption, the development of high power density converter system is of greater interesting. The rising energy prices have resulted in the cost of power and cooling exceeding the purchase cost in less than two years. Therefore, highly efficient power conversion is required for the power converter system. Generally, the power supply unit (PSU) for power distribution system (PDS) in data center and telecom are the standard two-stage approach which normally consists of power factor correction (PFC) circuit and isolated DC-DC converter. The two-stage power conversion has demonstrated excellent performance and high reliability, since the design can be optimized for each stage. However, limitations to prevent the existing two-stage PSU to fulfill future requirements for the PDS in data center and telecom applications are revealed, and a very promising and fundamentally different approach with the single-stage isolated converter is proposed in this dissertation. The development of single-stage converters with the option of placing the energy storage outside of the PSU creates new degrees of freedom regarding e.g. simplified rectifier racks in telecom and data center. This provides tangible benefits in the form of space saving, better airflow for power unit in rectifier racks and improved lifespan. The three-phase isolated buck matrix-type rectifier, capable of achieving high power density and high efficiency, is identified as an excellent candidate for the medium power level (5 kW~10 kW) single-stage power supply design. Nevertheless, the matrix-type rectifiers are known for their relatively complex modulation and commutation techniques, and lack of ride-through capability such as the stringent case of one phase loss operation. This dissertation work provides comprehensive study on the commutation method and modulation scheme design for the isolated buck matrix-type rectifier. It aims to analyze the operation principle of the rectifier and propose viable modulation and commutation schemes for this rectifier under both three-phase and single-phase operation. The method is verified by the hardware experiments of the PSUs with high efficiency (> 98%) and high power density (> 70 W/in3 ) for 54 V and 380 VDC applications. The prototypes demonstrated in the experiments show the effectiveness of the proposed modulation and commutation schemes for industry.

The ability to accurately model transient events is a key consideration in the design and operation of reliable and resilient data centers. While a standard compact transient model exists for IT-equipment thermal mass and transient building-envelope models are well known, there are no existing true "black-box" modeling techniques for data center cooling systems. We propose such a compact model which can be incorporated into simple flow-network and detailed CFD models alike. The model idealizes the cooling system as a heat exchanger in series with additional mass. For most data center applications, a "quasi-steady-state" model is sufficient for the heat exchanger portion; however, we also propose a numerical (discretized) approach which may be employed for the complete range of data center (and other) applications. Finally, we perform transient measurements on a small-scale heat exchanger in order to validate the model.

Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a two-tier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.

Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a two-tier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.

In August 2016, the National Renewable Energy Laboratory (NREL) installed a thermosyphon hybrid cooling system to reduce water usage in its already extremely energy-efficient High-Performance Computing (HPC) Data Center. In its first year of use, the system saved 4,400 m3 (1.16 million gal) of water, and 7,950 m3 (2.10 million gal) during a 2-year period, cutting the use of water in the data center by about one-half. NREL's 930-m2 (10,000-ft2) HPC Data Center is often called the most energy-efficient data center in the world: it has achieved a trailing 12-month average power usage effectiveness of 1.034, and it features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. NREL considered the amount of water used by the cooling towers to be counter to the laboratory's sustainability mission, so a team of researchers from NREL, Sandia National Laboratories (Sandia), and Johnson Controls integrated the BlueStream thermosyphon cooler (TSC) - an advanced dry cooler that uses refrigerant in a passive cycle to dissipate heat - on the roof of NREL's Energy Systems Integration Facility, the building that houses the HPC Data Center. In combination with the existing cooling towers, the TSC forms an extremely water- and cost-efficient cooling system. In its first year of operation, on-site water usage effectiveness (WUE) was 0.70 L/kWh. In comparison, the WUE would be 1.27 L/kWh if NREL had continued using only heat-recovery and cooling towers. This on-site water savings was accomplished without negatively impacting the energy-efficient operation of the HPC Data Center. The TSC system technology has the potential for application in data centers around the world, and it is currently being implemented by Sandia. center by about one-half. NREL's 930-m2 (10,000-ft2) HPC Data Center is often called the most energy-efficient data center in the world: it has achieved a trailing 12-month average power usage effectiveness of 1.034, and it features a chiller-less design, component-level warm-water liquid cooling, and waste heat capture and reuse. NREL considered the amount of water used by the cooling towers to be counter to the laboratory's sustainability mission, so a team of researchers from NREL, Sandia National Laboratories (Sandia), and Johnson Controls integrated the BlueStream thermosyphon cooler (TSC) - an advanced dry cooler that uses refrigerant in a passive cycle to dissipate heat - on the roof of NREL's Energy Systems Integration Facility, the building that houses the HPC Data Center. In combination with the existing cooling towers, the TSC forms an extremely water- and cost-efficient cooling system. In its first year of operation, on-site water usage effectiveness (WUE) was 0.70 L/kWh. In comparison, the WUE would be 1.27 L/kWh if NREL had continued using only heat-recovery and cooling towers. This on-site water savings was accomplished without negatively impacting the energy-efficient operation of the HPC Data Center. The TSC system technology has the potential for application in data centers around the world, and it is currently being implemented by Sandia.

Resource administration is basically an important dire function in the data center that may be affect Service level agreement and operation cost provided by the data center known as OPEX and SLA. Efficient resource is the key factor of resource utilization and provided guarantee SLA to each application to maximize the resources in data center. Accurate prediction support each application in data center is the key requirement to provision an efficient resource management. However, Under-estimating and Overestimation in the application workload result shows the resource under provision or overestimating. In this paper, our approach to apply ARIMA model for workload applications in data center, it is forecasting technique and capture autocorrelation in the series by modeling it directly. The key concept of ARIMA model is ordering and differencing only with linear data capturing when data graph in straight line. We applies different operation model to fit applications workload time series. Performance of ARIMA can be tested by MATLAB simulation. We are using the ARIMA model parameters to find out prediction errors during the day and month calculated (i.e. Day = 7.01% and Month = 6.73%) to provide accuracy of ARIMA prediction model.

The online detection of anomalies is a vital task in data centers, potentially incurring high personnel costs. Causes of anomalies range from hardware/software failures, to resource over- or under-provisioning, to application misbehaviors. This paper develops new methods and an associated utility for online anomaly detection, termed EbAT, entropy based anomaly tester, which can efficiently detect anomalies. This is done without the need for operator interaction, analysis intervention, or predefineed system models or rules. EbAT also offers ways to dasiazoom inpsila on detected anomalies, the intent being to localize anomalies to certain components of the data center's applications or facility. EbAT is implemented in the context of virtual machine monitors, using Xen as a representative platform, and it is used to detect anomalous behaviors on such platforms running multi-tier enterprise and map-reduce applications.

ThermoBench: A thermal efficiency benchmark for clusters in data centers

The energy efficiency of a data center depends on the cooling cost of clusters in the data center. Enhancing thermal efficiency of clusters is a practical approach to reducing energy consumption cost, optimizing scalability, and improving reliability. In this paper, we propose ThermoBench to evaluate the thermal efficiency of computing and storage clusters deployed in data centers. We shed light on the criteria and challenges of developing a thermal efficiency benchmark. We pay particular attention on clusters running scalable client-server enterprise applications in data centers. We characterize workload conditions in such a cluster computing environment in forms of client sessions of multiple requests. To resemble real-world applications, ThermoBench makes use of the TPCW benchmark to changes transaction mix and load over time. We apply ThermoBench to evaluate the thermal efficiency of a real-world cluster. Experimental results show that ThermalBench provides a simple yet powerful benchmark solution for assessing thermal behaviors of computing clusters in data centers.

A typical data center application requires the processor cycles of thousands of machines. Even a single-digit performance improvement can significantly reduce the cost and power consumption of a data center. Unfortunately, achieving sustained improvement, even if modest, is difficult. Data centers are dynamic environments where applications are frequently released and servers are continually upgraded. For maintainability and fault tolerance, the physical capabilities and configuration of the servers are abstracted from the application programmer.We study application performance under different processor prefetch configurations. These configurations are largely transparent to the programmer, yet we observe a wide range of performance when comparing the worst and best configurations, with relative performance improvement ranging from 1.4% to 75.1%. Alarmingly, one application that consumes many processor cycles has a 23.6% improvement.Default prefetch configurations favor aggressively prefetching memory, which benefits most applications, but some data center applications have highly tuned memory behavior and aggressive prefetching severely decreases performance. We develop a tuning framework which attempts to predict the optimal configuration based on hardware performance counters. It applies to a large number of performance-critical data center applications without modifying the source codeor binaries. The framework achieves performance within 1% of the best performance of a suite of important data center applications.

Thermal predictions in data centers have been utilized to reduce the electric consumption of thermal equipment in data centers. While most of the optimization of data center temperature has been performed through the utilization of Computational Fluid Dynamics (CFD) and heuristic methods, data driven modeling techniques are now also being used to optimize the data center temperatures. Some data driven models have been used on a static data set to obtain the steady state temperature predictions for given input variables while other data driven models have been trained to provide temperature predictions at live time. This paper aims to investigate the transient temperature prediction capabilities of two data driven models — Long-Short Term Memory (LSTM) and Nonlinear Autoregressive Neural Network with External Input (NARX). While these two methods have been previously studied on data center applications, they have not been compared with each other for transient temperature predictions for normal operations. The study also utilizes ensembles to provide better temperature prediction accuracy for smaller data sets. The study compared these two models based on an experimentally obtained data set and found that NARX outperforms LSTM for normal operations and that the data driven models are able to provide relatively good predictions even if the input variables are slightly outside the training domain.