Ranking anomalies in data centers

With the advent of cloud computing and online services, large enterprises rely heavily on their data centers to serve end users. Among different server components, hard disk drives are known to contribute significantly to server failures. Disk failures as well as their impact on the performance of storage systems and operating costs are becoming an increasingly important concern for data center designers and operators. However, there is very little understanding on the characteristics of disk failures in data centers. Effective disk failure management and data recovery also requires a deep understanding of the nature of disk failures. In this paper, we present a systematic approach to provide a holistic and insightful view of disk failures. We study a large-scale storage system from a production data center. We categorize disk failures based on their distinctive manifestations and properties. Then we characterize the degradation of disk errors to failures by deriving the degradation signatures for each failure category. The influence of disk health attributes on failure degradation is also quantified. We discuss leveraging the derived degradation signatures to forecast disk failures even in their early stages. To the best of our knowledge, this is the first work that shows how to discover the categories of disk failures and characterize their degradation processes on a production data center.

The modern data centers (DCs) are essential to fulfilling ever-evolving computational demands around cloud computing, big data, and IT infrastructure. These DCs are facilities (Figure 1) that house computer systems and associated components such as networking and storage systems. To operate a DC, power supplies, network connections, environmental controls (e.g., air conditioning, humidity), and security infrastructure are needed. Technology and business challenges such as virtualization, load consolidation, real-time troubleshooting, and service-level guarantees require a robust and adaptive server management plan for enterprise. The majority of DC issues are related to overutilization of resources, application failures, data security, power usage effectiveness (PUE), and infrastructure costs. This requires proactive solutions that are business intelligent and built over a network of sense points that are guaranteed to deliver reliable trends and measurements in a reliable and timely fashion. Since it is expensive to build new DCs, the best option is to improve usage of an existing facility through lower infrastructure overhead to deliver better resource management. An optimal sensor network would perform real-time sensor-data collection and deliver a) improved server rack utilization, b) improved DC cooling, and c) improved loadbalancing through dynamic capping of thermally constrained systems.

Cooling power constitutes a large portion of the total electrical power consumption in data centers. Approximately 25%∼40% of the electricity used within a production data center is consumed by the cooling system. Improving the cooling energy efficiency has attracted a great deal of research attention. Many strategies have been proposed for cutting the data center energy costs. One of the effective strategies for increasing the cooling efficiency is using dynamic thermal management. Another effective strategy is placing cooling devices (heat exchangers) closer to the source of heat. This is the basic design principle of many hybrid cooling systems and liquid cooling systems for data centers. Dynamic thermal management of data centers is a huge challenge, due to the fact that data centers are operated under complex dynamic conditions, even during normal operating conditions. In addition, hybrid cooling systems for data centers introduce additional localized cooling devices, such as in row cooling units and overhead coolers, which significantly increase the complexity of dynamic thermal management. Therefore, it is of paramount importance to characterize the dynamic responses of data centers under variations from different cooling units, such as cooling air flow rate variations. In this study, a detailed computational analysis of an in row cooler based hybrid cooled data center is conducted using a commercially available computational fluid dynamics (CFD) code. A representative CFD model for a raised floor data center with cold aisle-hot aisle arrangement fashion is developed. The hybrid cooling system is designed using perimeter CRAH units and localized in row cooling units. The CRAH unit supplies centralized cooling air to the under floor plenum, and the cooling air enters the cold aisle through perforated tiles. The in row cooling unit is located on the raised floor between the server racks. It supplies the cooling air directly to the cold aisle, and intakes hot air from the back of the racks (hot aisle). Therefore, two different cooling air sources are supplied to the cold aisle, but the ways they are delivered to the cold aisle are different. Several modeling cases are designed to study the transient effects of variations in the flow rates of the two cooling air sources. The server power and the cooling air flow variation combination scenarios are also modeled and studied. The detailed impacts of each modeling case on the rack inlet air temperature and cold aisle air flow distribution are studied. The results presented in this work provide an understanding of the effects of air flow variations on the thermal performance of data centers. The results and corresponding analysis is used for improving the running efficiency of this type of raised floor hybrid data centers using CRAH and IRC units.

Cloud computing (CC) services can offer substantial cost-effective global operational and relationship benefits if the cooperation between logistics and CC services are resilient. Potential vulnerabilities to cooperation of CC and logistics service providers can occur with respect to vital factors such as security and trust. Extant studies have demonstrated CC benefits as well as few challenges associated with CC services application. However, no extant study has examined the inter-organisational benefits based on cooperative resilience between CC and logistics service providers in terms of both capability and trust vulnerability factors. This study examines the cooperative resilience of logistics and CC service providers based on innovation diffusion theory within a supply chain risk assessment framework. Using structural equation modelling techniques, we investigate the relationship between the vulnerability factor (trust), capability factor (security) and collaboration benefits (relationship and operational) offered by CC service providers based on 236 Chinese logistics service firms’ perceptions of CC adoption. The results indicate Chinese logistics companies perceive security impediments as a major factor affecting cooperative resilience between logistics service and CC service providers.

As cloud computing becomes more emerging technology, there is increasing attention being paid to the energy consumption pattern across the entire information and communication technology (ICT) sector. With advent of cloud computing, computing functions and storage processing are migrating to remote resources like virtual servers stored in systems which are mostly hosted in the data centers located in various places. Virtualization technology has been used widely in modern data center in order to improve its energy efficiency. Virtual machine (VM) migration has recently emerged technique used as an essential building block for data center and storage systems, mainly due to its service provisioning and energy aware consolidation. We analysed the existing energy consumption model for various types of services in cloud computing environment and we present a new energy-aware provisioning approach by considering energy efficiency as a key factor. Our model uses pre-processed data about the service usage of cloud used for initiating the live migration. Our design encompasses the component called as trigger engine which initiates automatic migration of VM to preserve the computing environment green and energy friendly.

Cooling is a major component in the enormous energy consumption in data centers. Accurate evaluation of cooling inside a data center forms the backbone of all the attempts for improving cooling efficiency. Models based on computational fluid dynamics (CFD) are typically used for accurate evaluation, but have a drawback of high computation time. This paper presents a novel thermal predictor to evaluate data center cooling in seconds. The key idea is to extract information from a single instance of CFD simulation using metrics called as influence indices to build the fast thermal predictor. Then, this predictor can evaluate the cooling for altered operation of data center with comparable accuracy in seconds without the need for repetitive CFD simulations. This paper demonstrates the accuracy of the thermal predictor by comparing with CFD simulations for a sample, but realistic data center. The fast thermal predictor then successfully passed more challenging tests in a real production data center and proved its practical utility. The results of the thermal predictor compared with measurements carried out in the production data center are also presented. This fast thermal predictor is an important milestone in the development of a method for model-based real time control of data center cooling.

With the rapid development of national economy, our demand for power resources has also increased rapidly. The traditional power grid can not be achieved operational status of equipment for effective monitoring and control, we will not be able to ensure the quality and quality of electricity service. Cloudbased build smart grid information platform enables gathering the parameter of grid operation state, greatly increasing the economic efficiency of power enterprises. Firstly, cloud computing and smart grid briefly described, and then pointed out that cloud computing applications in the smart grid information platform, and specialized of cloud-based smart grid information platform, in order for the power grid enterprises to enhance their level of service and provide effective reference. KEYWORD: cloud computing; smart power grids; information platform; Application 4th International Conference on Mechanical Materials and Manufacturing Engineering (MMME 2016) © 2016. The authors Published by Atlantis Press 202 3 THE MAIN PROBLEM IS STILL THE SMART GRID INFORMATION PLATFORM FACING The transition to a cloud computing data center. Our data center because of the different regions and the degree of information presents different characteristics, some data centers already have a basic cloud computing capabilities, but most data centers still remain in the initial level, to ensure the normal operation of the existing data center power under the premise, gradual transition to cloud computing data center in order to meet future requirements of the development of smart grid. Restricting data transmission problem. State Grid Corporation between provincial branches and there are a lot of data needs to be transmitted, will bring massive data transmission efficiency decreases, taking into account the load balancing device between the cloud node, thus building a wide area network channels to resolve data transmission problem. Security of data transmission. Data Center for Cloud Computing power companies to build internal private cloud, capable of self-maintenance and management, system wide area network data transmission services, according to the third security zone defined security partition, and install proprietary equipment production area and external public network isolation . 4 CLOUD COMPUTING APPLICATIONS IN THE SMART GRID INFORMATION POLICY PLATFORM Integrate and optimize the management of heterogeneous resources. Among the smart grid systems, data resources, information systems between various platforms and mutually dispersed, severe heterogeneity, thus leading to the integration of the quality of information sharing to increase difficulty, unable to effectively improve network management efficiency for the power distribution system electrical work quality management and marketing operations system maintenance upgrade will not be able to effectively meet demand (Jie Yang, 2014). Cloud computing technology enables the power system to effectively integrate the various business data and computing resources on the basis of information for business collaboration platform built on effective, able to satisfy the huge demand for smart grid information resource sharing. Cloud computing network virtualization and application virtualization technology, the server virtualization technology and other resources to achieve each service abstraction, and then realize the difference between the system and the software effectively eliminated by the appropriate method to achieve the effective integration of information resources, and then to optimize the management, greatly improving the efficiency of heterogeneous resources management applications. Distributed storage and management of large volume of data. After application of smart grid information platform construction, customer service requests varied, huge data volume information brought it to the storage and management of data provides a great challenge. Cloud computing technology in general face amount of data each smart grid system and unit collected using forms distributed storage of data to achieve the effective storage, greatly to ensure the security and reliability of data storage. GFS effective use of technology with each node, the master server is responsible for the effective management of the whole file system, the file system and complete the work of the metadata stored in the data block is the main server for data storage block sliced, and by saving three or more redundant backup storage. At the same time, client access and complete access to the main block server information. Fast Power System Parallel Computation and Treatment. Smart grids and information platform for the large volume of data to provide stable operation of the power grid data base, but more critical is the analysis and design of each system operating data, planning and decision-making, and even failures and transient stability, state estimation and intelligent a variety of computing and content analysis and decision-making process, its huge computing needs, general computing system can not reach its goal (Wang, Mengxue, 2015). Cloud computing technology through parallel computing technology has greatly enhanced parallel processing capability of the power system, which provides parallel programming model makes more simple and efficient parallel algorithms. MapReduce as Google made an important parallel programming model, and its role in large data sets in parallel computing plays a very prominent. Parallel processing job results by the client to achieve effective user submitted, via decomposition automated master role, the corresponding task scheduling to working nodes and nodes work completed by the specific calculations and perform the appropriate task. 5 SMART GRID INFORMATION PLATFORM CLOUD COMPUTING ANALYSIS Cloud Computing Smart Grid Information Platform Architecture. Smart grid technology information platform architecture cloud computing include the following several levels and aspects (Yan Cui, 2015): First, that the platform infrastructure layer is the integration of the various hardware resources and related management capabilities of virtualized using virtual technology to achieve the corresponding hardware devices such as computers and network storage devices abstraction, and then complete the automation and optimization of resource data and load management; secondly, the main platform for the integration of the various layers of software

The magnitude and complexity of electricity usage patterns from large data centers are having significant impacts on the operation and dynamics of the power grid; grid operators and planners require a range of specialized data center models to properly evaluate these impacts and specify technical solutions as needed. Towards addressing this need, Pacific Northwest National Laboratory (PNNL) has developed a library of electromagnetic transient (EMT) models for grid-level studies of data centers called the data center model library (DML). This report describes how the DML was created and how it may properly be used. The models present in the DML are generic models; subject matter expertise and additional technical data are needed to modify these models before they can represent any real data center. However, they will significantly reduce the level of effort required to develop site-specific models and can serve as a common starting point to guide industry towards a more refined consensus. Most of the models within DML are dedicated to representing the power electronics interfaces commonly used in modern data centers, such as double-conversion uninterruptible power supplies and single-phase power factor correction converters. These models are intended for use in grid-level studies and are a simplified aggregation of many small components. That said, background material on the physical and electrical design of large data centers is provided as companion material so that users can be aware of many of the details which have been omitted or streamlined as a matter of practical necessity. Additionally, guidance on the application of EMT analysis for data center interconnection studies is provided, which aids users in identifying when the DML is necessary and what sort of additional model development may be necessary for conducting real-world studies.

Today's data centers use substantial amounts of the world's electrical supply. However, in line with circular economy concepts, much of this energy can be reused. Such reuse includes the heating of buildings, but also commodity dehydration, electricity production and energy storage. This multi-disciplinary paper presents several novel applications for data center waste heat. Next, the paper accounts for three case studies, taken from three different societal contexts: urban Malaysia, rural Costa Rica and semi-urban Sweden. A discussion on data center energy metrics leads to the development of a new metric, Datacenter Energy Sustainability Score (DESS), which is evaluated within the three use cases. Last, it is shown how a material social view on metrics provides a way past a problem that has haunted the data center industry for the last 15 years, whilst benefitting both data center owners who want to compete through sustainability as well as stakeholders from governments on local, regional and national levels. The paper makes clear that a sustainability strategy should be based on a material social view and stretch beyond the building itself. In fact, and as demonstrated by the relevance of DESS, modern data centers are so energy-efficient that data center sustainability is no longer mainly an engineering issue, but a matter requiring multi-disciplinary insights, approaches and collaboration.

In this paper we propose a monitoring system of a data center that is able to infer when the data center is getting into an anomalous behavior by analyzing the power consumption at each server and the data center network traffic. The monitoring system is non-intrusive in the sense that there is no need to install software on the data center servers. The monitoring architecture embeds two Elman Recurrent Networks (RNNs) to predict power consumed by each data center component starting from data center network traffic and viceversa. Results obtained along six mounts of experiments, within a data center, show that the architecture is able to classify anomalous system behaviors and normal ones by analyzing the error between the actual values of power consumption and network traffic and the ones inferred by the two RNNs.

Cloud computing is next generation of Internet technology and data centers. As such, Information security (IS) has become one of the most essential issues of data center security on the cloud computing, but we cannot rely solely on single solution to support all of the necessary features of IS. Therefore, we need to a new information security service model, called Security Operation Center (SOC). It can support online interactions and remote monitoring via network operations in data centers. The SOC is required to integrate related IS technologies based on security policies. In this paper, we propose the service level agreement (SLA) strategy for designing the SOC platform to meet the IS requirements of any organization in cloud computing. Therefore, the main purposes of this paper are to propose the framework of SLA strategy and the integration technology for designing an SOC platform. The SOC platform with SLA is to handle and supervise all the security service processes under different levels of cloud security enforcements according to data center scales, business properties, and existing IS functions. Consequently, the SLA strategy and integrating technology of IS can be the reference model for designing a proposed SOC platform in data centers on the cloud computing.

Receiver operating characteristics (ROC) has been widely used as a performance evaluation tool to measure effectiveness of medical modalities. It is derived from a standard detection theory with false alarm and detection power interpreted as false positive (FP) and true positive (TP) respectively in terms of medical diagnosis. The ROC curve is plotted based on TP versus FP via hard decisions. This paper presents a three dimensional (3D) ROC analysis which extends the traditional two-dimensional (2D) ROC analysis by including a threshold parameter in a third dimension resulting from soft decisions, (SD). As a result, a 3D ROC curve can be plotted based on three parameters, TP, FP and SD. By virtue of such a 3D ROC curve three two-dimensional (2D) ROC curves can be derived, one of which is the traditional 2D ROC curve of TP versus FP with SD reduced to hard decision. In order to illustrate its utility in medical diagnosis, its application to magnetic resonance (MR) image classification is demonstrated.

The identification of encrypted Instant Messaging (IM) channels between users is made difficult by the presence of variable and high levels of uncorrelated background traffic. In this paper, we propose a novel Cross-correlation Outlier Detector (CCOD) to identify communicating end-users in a large group of users. Our technique uses traffic flow traces between individual users and IM service provider's data center. We evaluate the CCOD on a data set of Yahoo! IM traffic traces with an average SNR of −6.11dB (data set includes ground truth). Results show that our technique provides 88% true positives (TP) rate, 3% false positives (FP) rate and 96% ROC area. Performance of the previous correlation-based schemes on the same data set was limited to 63% TP rate, 4% FP rate and 85% ROC area.

Data centers consume enormous energy, presently reported up to 2% of the world’s electricity, with most of this energy then rejected into the atmosphere as waste heat. Meanwhile there is a global scarcity of safe drinking water. The UN states that 20% of the world’s population lives in regions affected by scarcity of drinking water. In this paper, we discuss an on-going research initiative that investigates the reuse of “free” waste heat energy from data centers in coastal cities and island countries, and in modular data centers that are deployed in coastal regions, through a low-pressure desalination process that converts sea water into safe drinking and irrigation water supplies, while significantly improving the Power Utilization Efficiency (PUE) for the data centers. In this paper, we discuss a work-in-progress experimental setup with the purpose of demonstrating that heat removed via common fluid-cooled rack heat exchangers in modern data centers can be re-used via a controlled-low-pressure desalination technique to turn salt water into drinking water at zero added carbon cost for the desalination, and significantly reduced carbon cost for the data center operations.

In modern data centres (DCs), energy consumption and resource management are challenging tasks. So in large virtualized DCs, performance of applications is highly dependent on energy-aware DC architecture and smooth network communication between virtual machines (VMs), while minimizing the communication burden to avoid congestion, latency etc. The communication cost of a network can be reduced by minimizing the VMs migration between physical machines (PMs). Therefore customer as well as service providers need cloud computing solutions that not only minimize operational costs, but also total network load. Minimizing the use of energy/network communication overhead with maximum resource utilization in large DCs is a challenging task as computing applications and data are growing so quickly for which increasing larger servers and disks are required to process them fast enough within the required time period. In order to minimize network load and maximize resource utilization in cloud DCs, we simulated our proposed network load-aware scheduling algorithm that ensures minimum VMs migration while delivering the negotiated Quality-of-Service (QoS). Our simulation also shows results with different bit rates.