Enabling Zero-Downtime Maintenance And Dynamic Load Balancing Through Intelligent Workload Migration In Enterprise Data Centers

Enterprise data centers (EDCs) are critical infrastructure to large enterprises, government agencies, research institutions, etc. They are used to support a variety of off-the-shelf and customized services. EDCs are different from cloud data centers (CDCs) in two major aspects. Firstly, an EDC is usually built over time and consists of old and new equipment. Secondly, the type of services and applications in EDCs are quite different from those in CDCs. Therefore, we expect that the traffic characteristics in EDCs would also be different from those in CDCs. While most existing data center measurements were from CDCs, we performed extensive traffic measurement and analysis in an EDC that provided multiple services to over a million users. We present the data center architecture, measurement methodology, measurement results, and analysis. The results include traffic matrix, traffic distribution, flow characteristics, and TCP characteristics. Our research reveals that the traffic characteristics in the EDC are indeed quite different from the reported results in CDCs. For example, the traffic matrix tends to be sparse rather than all-to-all. Based on the analysis we provide a few guidelines for EDC design, optimization, and anomaly detection. As the first most extensive study on EDC traffic, our work provides valuable information to future EDC design and implementation, and also helps researchers develop insights into the differences and similarities between EDCs and CDCs.

Scientific and enterprise data centers, IT equipment product development, and research data center laboratories typically require continuous cooling to control inlet air temperatures within recommended operating levels for the IT equipment. The consolidation and higher density aggregation of slim computing, storage and networking hardware has resulted in higher power density than what the raised-floor system design, coupled with commonly used computer rack air conditioning (CRAC) units, was originally conceived to handle. Many existing data centers and newly constructed data centers adopt CRAC units, which inherently handle heat transfer within data centers via air as the heat transfer media. This results in energy performance of the ventilation and cooling systems being less than optimal. Understanding the current trends toward higher power density in IT computing, more and more IT equipment manufacturers are designing their equipment to operate in 'conventional' data center environments, while considering provisions of alternative cooling solutions to either their equipment or supplemental cooling in rack or row systems. In the meanwhile, the trend toward higher power density resulting from current and future generations of servers has created significant opportunities for precision cooling suppliers to engineer and manufacture packaged modular and scalable systems. The modular and scalable cooling systems aim at significantly improving efficiency while addressing the thermal challenges, improving reliability, and allowing for future needs and growth. Such pre-engineered and manufactured systems may be a significant improvement over current design; however, without an energy efficiency focus, their applications could also lead to even lower energy efficiencies in the overall data center infrastructure. The overall goal of the project supported by California Energy Commission was to characterize four commercially available, modular cooling systems installed in a data center. Such modular cooling systems are all scalable localized units, and will be evaluated in terms of their operating energy efficiency in a real data center, respectively, as compared to the energy efficiency of traditional legacy data center cooling systems. The technical objective of this project was to evaluate the energy performance of one of the four commercially available modular cooling systems installed in a data center in Sun Microsystems, Inc. This report is the result of a test plan that was developed with the industrial participants input, including specific design and operating characteristics of the selected modular localized cooling solution provided by vendor 3. The technical evaluation included monitoring and measurement of selected parameters, and establishing and calculating energy efficiency metrics for the selected cooling product, which is a modular, scalable liquid-rack cooling system in this study. The scope is to quantify energy performance of the modular cooling unit in operation as it corresponds to a combination of varied server loads and inlet air temperatures, under various chilled-water supply temperatures. The information generated from this testing when combined with documented energy efficiency of the host data center's central chilled water cooling plant can be used to estimate potential energy savings from implementing modular cooling compared to conventional cooling in data centers.

Scientific and enterprise data centers, IT equipment product development, and research data center laboratories typically require continuous cooling to control inlet air temperatures within recommended operating levels for the IT equipment. The consolidation and higher density aggregation of slim computing, storage and networking hardware has resulted in higher power density than what the raised-floor system design, coupled with commonly used computer rack air conditioning (CRAC) units, was originally conceived to handle. Many existing data centers and newly constructed data centers adopt CRAC units, which inherently handle heat transfer within data centers via air as the heat transfer media. This results in energy performance of the ventilation and cooling systems being less than optimal. Understanding the current trends toward higher power density in IT computing, more and more IT equipment manufacturers are designing their equipment to operate in 'conventional' data center environments, while considering provisions of alternative cooling solutions to either their equipment or supplemental cooling in rack or row systems. Naturally, the trend toward higher power density resulting from current and future generations of servers has, in the meanwhile, created significant opportunities for precision cooling suppliers to engineer and manufacture packaged modular and scalable systems. The modular and scalable cooling systems aim at significantly improving efficiency while addressing the thermal challenges, improving reliability, and allowing for future needs and growth. Such pre-engineered and manufactured systems may be a significant improvement over current design; however, without an energy efficiency focus, their applications could also lead to even lower energy efficiencies in the overall data center infrastructure. The overall goal of the project supported by California Energy Commission was to characterize four commercially available, modular cooling systems installed in a data center. Such modular cooling systems are all scalable localized units, and will be evaluated in terms of their operating energy efficiency in a real data center, respectively, as compared to the energy efficiency of traditional legacy data center cooling systems. The technical objective of this project was to evaluate the energy performance of one of the four commercially available modular cooling systems installed in a data center in Sun Microsystems, Inc. This report is the result of a test plan that was developed with the industrial participants' input, including specific design and operating characteristics of the selected modular localized cooling solution provided by vendor 1. The technical evaluation included monitoring and measurement of selected parameters, and establishing and calculating energy efficiency metrics for the selected cooling product, which is a modular, scalable overhead cooling system. The system was tested in a hot/cold aisle environment without separation, or containment or the hot or cold aisles. The scope of this report is to quantify energy performance of the modular cooling unit in operation as it corresponds to a combination of varied server loads and inlet air temperatures. The information generated from this testing when combined with a concurrent research study to document the energy efficiency of the host data center's central chilled water cooling plant can be used to estimate potential energy savings from implementing modular cooling compared to conventional cooling in data centers.

Scientific and enterprise data centers, IT equipment product development, and research data center laboratories typically require continuous cooling to control inlet air temperatures within recommended operating levels for the IT equipment. The consolidation and higher density aggregation of slim computing, storage and networking hardware has resulted in higher power density than what the raised-floor system design, coupled with commonly used computer rack air conditioning (CRAC) units, was originally conceived to handle. Many existing data centers and newly constructed data centers adopt CRAC units, which inherently handle heat transfer within data centers via air as the heat transfer media. This results in energy performance of the ventilation and cooling systems being less than optimal. Understanding the current trends toward higher power density in IT computing, more and more IT equipment manufacturers are designing their equipment to operate in 'conventional' data center environments, while considering provisions of alternative cooling solutions to either their equipment or supplemental cooling in rack or row systems. Naturally, the trend toward higher power density resulting from current and future generations of servers has, in the meanwhile, created significant opportunities for precision cooling suppliers to engineer and manufacture packaged modular and scalable systems. The modular and scalable cooling systems aim at significantly improving efficiency while addressing the thermal challenges, improving reliability, and allowing for future needs and growth. Such pre-engineered and manufactured systems may be a significant improvement over current design; however, without an energy efficiency focus, their applications could also lead to even lower energy efficiencies in the overall data center infrastructure. The overall goal of the project supported by California Energy Commission was to characterize four commercially available, modular cooling systems installed in a data center. Such modular cooling systems are all scalable localized units, and will be evaluated in terms of their operating energy efficiency in a real data center, respectively, as compared to the energy efficiency of traditional legacy data center cooling systems. The technical objective of this project was to evaluate the energy performance of one of the four commercially available modular cooling systems installed in a data center in Sun Microsystems, Inc. This report is the result of a test plan that was developed with the industrial participants input, including specific design and operating characteristics of the selected modular localized cooling solution provided by vendor 2. The technical evaluation included monitoring and measurement of selected parameters, and establishing and calculating energy efficiency metrics for the selected cooling product, which is a modular, scalable pair of chilled water cooling modules that were tested in a hot/cold aisle environment with hot aisle containment. The scope of this report is to quantify energy performance of the modular cooling unit in operation as it corresponds to a combination of varied server loads and inlet air temperatures. The information generated from this testing when combined with a concurrent research study to document the energy efficiency of the host data center's central chilled water cooling plant can be used to estimate potential energy savings from implementing modular cooling compared to conventional cooling in data centers.

PurposeThe purpose of this paper is to develop a collaborative agent-based web architecture and an agent-based green IS assessment tool to aid information technology (IT) practitioners in data centers assess their current green information systems (IS) practice toward attaining sustainability.Design/methodology/approachThe methodology comprises that the collaborative agent-based web architecture, agents’ algorithm and the green IS assessment tool, which is validated by employing focus group questionnaire targeting IT practitioners in seven Malaysian-based enterprises that have an in-house data centers. With 105 valid samples at hand, descriptive analysis and exploratory factor analysis was utilized to determine the applicability of the implemented agent-based green IS assessment tool.FindingsFindings reveal that the agent-based green IS assessment tool possesses the capability to evaluate benchmark and rate enterprise data centers current green IS practice. Additional findings indicate that the agent-based green IS assessment tool provide suggestions on how green IS practice can be improved in enterprise data centers.Research limitations/implicationsThis study only collected data from 105 IT practitioners in enterprise data centers based in Malaysia; as such results from this research cannot be generalized to other countries. Moreover, the developed collaborative agents for green IS practice assessment can only be fully deployed after domain experts has added green IS practice assessment questions and alternative answers.Practical implicationsThis study presents an autonomous agent-based green IS assessment tool that supports the assessment of enterprise toward inclusion of sustainability considerations to enhance enterprise environmental performance.Social implicationsThis study provides empirical evidence for data centers efficacy leading toward a greener society for environmental conservation for future generations to come.Originality/valueThis study creates awareness by presenting the green IS practice to be implemented by IT practitioners in data centers. In addition, the agent-based green IS assessment tool provides a web-based platform for promoting environmental sustainability by supporting data centers toward evaluating, benchmarking and rating their current green IS practices.

As modern enterprises scale their digital operations, data centers face increasing demands to provide reliable, high-performance networking solutions. The complexities of managing extensive networks—spanning critical primary links, underutilized backup paths, and dynamic traffic patterns—pose challenges such as performance degradation, delayed fault resolution, and operational inefficiencies. Traditional, manual approaches to network management are insufficient to address these issues on a scale, necessitating the adoption of network automation platforms. This case study explores the implementation of a comprehensive Network Automation Platform designed to optimize operational efficiency in a multinational enterprise's data center environment. The solution integrates cutting-edge tools such as Cisco DNA Center (DNAC) for real-time telemetry, ThousandEyes for advanced path monitoring, Grafana for visualization and alerting, and ServiceNow for streamlined incident management. Automation technologies including Ansible, Terraform, and custom Python workflows enable proactive traffic rerouting, efficient secondary path utilization, and rapid fault remediation. Key use cases are presented to demonstrate the platform's capabilities: Dynamic Traffic Management: Automatic diversion of traffic from congested primary links to underutilized secondary paths ensures optimal resource usage and prevents performance bottlenecks. Load Balancing: Continuous monitoring and redistribution of traffic across backup paths maintain network stability and prevent overloads. Failure Response: Seamless failover mechanisms and automated ticketing in ServiceNow reduce Mean Time to Resolution (MTTR) during outages. The solution was validated using simulated traffic congestion, link failures, and load balancing scenarios, achieving measurable improvements in uptime, latency, and operational efficiency. The platform can reduce MTTR by 40%, optimize backup link utilization by 30%, and automate 80% of repetitive network tasks. This study provides a structured framework for building and implementing such platforms, addressing both technical and operational challenges. Future recommendations include leveraging AI for predictive analytics, integrating SD-WAN controllers for application-aware routing, and expanding monitoring to edge and cloud environments. This approach offers a scalable, resilient, and cost-effective strategy for transforming network operations in data centers, setting a benchmark for enterprises aiming to modernize their IT infrastructure

Big data processing environments such as Apache Spark are prominently deployed for applications with large scale workloads. New storage technologies such as Non-Volatile Memory Express Solid State Drives (NVMe SSDs) provide higher throughput comparing to the traditional Hard Disk Drives (HDDs). Therefore, NVMe SSDs are rapidly substituting HDDs in modern data centers. In this paper, we explore whether it is critically necessary to use NVMe SSD for a large workload running on the Spark big data framework. Specifically, we investigate what are the influential factors of application design and Spark data processing framework to exploit the benefits of NVMe SSDs. Our real experimental results reveal that some applications even with large workloads cannot fully utilize NVMe SSDs to obtain high I/O throughput. Interestingly, we find out that characteristics of Spark data processing framework such as shuffling (i.e., the volume of transition data generated by an application), and parallelism (i.e., the number of concurrently running tasks) has very crucial impacts on the performance of big data applications running on NVMe SSDs.

Research Problem. Aiming at the problems such as fast update and iteration speed, high development cost, and isolated enterprise data in hydrogen equipment manufacturing industry, a low code programming system based on enterprise data center and business center was proposed and designed. Research Method. This paper designs the data center service and business center architecture of hydrogen equipment manufacturing industry. The data center collects, mines, and integrates massive data into unified standard structured data and stores them. The business center provides architectural support for the establishment of enterprise resource management system. With the data center service as the system data interaction center and the business center as the model to build the system architecture, the hydrogen equipment manufacturing industry resource management system is constructed through the steps of data interaction, process modeling, and UI editing. Research Results. According to the specific requirements of hydrogen equipment manufacturing industry, the business process of hydrogen enterprise resource management system is constructed, and the construction and development of low code system are studied and discussed by integrating data center and business center. Conclusions. Low code is not just a tool to increase productivity, it has the potential to be a catalyst for technology and culture to promote enterprise innovation and business agility, and ERP systems for hydrogen equipment manufacturing based on low coding technology are needed for the industry of the future. This article contributes to the hydrogen equipment manufacturing industry of low code programming system, users can according to their own needs, through the combination of components, quickly generate a complete management system, update system iteration, greatly reduce the development cost, solve the problem of enterprise data isolation, reduce the development cost, and realize the seamless integration of modern software development practice, so as to promote enterprise innovation and business agility.

IPv6 for Enterprise Networks The practical guide to deploying IPv6 in campus, WAN/branch, data center, and virtualized environments Shannon McFarland, CCIENo. 5245 Muninder Sambi, CCIE No. 13915 Nikhil Sharma, CCIE No. 21273 Sanjay Hooda, CCIE No. 11737 IPv6 for Enterprise Networks brings together all the information you need to successfully deploy IPv6 in any campus, WAN/branch, data center, or virtualized environment. Four leading Cisco IPv6 experts present a practical approach to organizing and executing your large-scale IPv6 implementation. They show how IPv6 affects existing network designs, describe common IPv4/IPv6 coexistence mechanisms, guide you in planning, and present validated configuration examples for building labs, pilots, and production networks. The authors first review some of the drivers behind the acceleration of IPv6 deployment in the enterprise. Next, they introduce powerful new IPv6 services for routing, QoS, multicast, and management, comparing them with familiar IPv4 features and behavior. Finally, they translate IPv6 concepts into usable configurations. Up-to-date and practical, IPv6 for Enterprise Networks is an indispensable resource for every network engineer, architect, manager, and consultant who must evaluate, plan, migrate to, or manage IPv6 networks. Shannon McFarland, CCIE No. 5245, is a Corporate Consulting Engineer for Cisco serving as a technical consultant for enterprise IPv6 deployment and data center design with a focus on application deployment and virtual desktop infrastructure. For more than 16 years, he has worked on large-scale enterprise campus, WAN/branch, and data center network design and optimization. For more than a decade, he has spoken at IPv6 events worldwide, including Cisco Live. Muninder Sambi, CCIE No. 13915, is a Product Line Manager for Cisco Catalyst 4500/4900 series platform, is a core member of the Cisco IPv6 development council, and a key participant in IETFs IPv6 areas of focus. Nikhil Sharma, CCIE No. 21273, is a Technical Marketing Engineer at Cisco Systems where he is responsible for defining new features for both hardware and software for the Catalyst 4500 product line. Sanjay Hooda, CCIE No. 11737, a Technical Leader at Cisco, works with embedded systems, and helps to define new product architectures. His current areas of focus include high availability and messaging in large-scale distributed switching systems. n Identify how IPv6 affects enterprises n Understand IPv6 services and the IPv6 features that make them possible n Review the most common tranisition mechanisms including dual-stack (IPv4/IPv6) networks, IPv6 over IPv4 tunnels, and IPv6 over MPLS n Create IPv6 network designs that reflect proven principles of modularity, hierarchy, and resiliency n Select the best implementation options for your organization n Build IPv6 lab environments n Configure IPv6 step-by-step in campus, WAN/branch, and data center networks n Integrate production-quality IPv6 services into IPv4 networks n Implement virtualized IPv6 networks n Deploy IPv6 for remote access n Manage IPv6 networks efficiently and cost-effectively This book is part of the Networking Technology Series from Cisco Press, which offers networking professionals valuable information for constructing efficient networks, understanding new technologies, and building successful careers.

The critical inter-dependencies between Enterprise Services and Enterprise Data are often not given due consideration. With the advent of Cloud Computing, it is becoming increasingly important for organizations to understand the relationships between them, in order to formulate strategies to jointly manage and coordinate enterprise services and data to improve business value and reduce risk to the enterprise. Enterprise Services encompass Service-driven applications deployed on-premises in the enterprise data centers as well as in the Cloud for the “extended enterprise.” Enterprise Data Management encompasses the cross-application enterprise-level perspective of data in an information-sharing enterprise, and the critical business data that is created, maintained, enriched, and shared outside the traditional enterprise firewall. This chapter discusses and proposes best practice strategies for coordinating the enterprise SOA & EDM approaches for mutual success. Primary coordination aspects discussed include: Service & Data Governance, Master Data Management, Service-driven & EDM Architecture Roadmaps, Service Portfolio Management, Enterprise Information Architecture, and the Enterprise Data Model. It recommends a facilitative Service-driven Data Architecture Framework & Capability Maturity Model to help enterprises evaluate and optimize overall effectiveness of their coordinated Service-driven & EDM strategies.

Modern day enterprises have a large IT infrastructure comprising thousands of applications running on servers housed in tens of data centers geographically spread out. These enterprises periodically perform a transformation of their entire IT infrastructure to simplify, decrease operational costs and enable easier management. However, the large number of different kinds of applications and data centers involved and the variety of constraints make the task of data center transformation challenging. The state-of-the-art technique for performing this transformation is simplistic, often unable to account for all but the simplest of constraints. We present eTransform, a system for generating a transformation and consolidation plan for the IT infrastructure of large scale enterprises. We devise a linear programming based approach that simultaneously optimizes all the costs involved in enterprise data centers taking into account the constraints of applications groups. Our algorithm handles the various idiosyncrasies of enterprise data centers like volume discounts in pricing, wide-area network costs, traffic matrices, latency constraints, distribution of users accessing the data etc. We include a disaster recovery (DR) plan, so that eTransform, thus provides an integrated disaster recovery and consolidation plan to transform the enterprise IT infrastructure. We use eTransform to perform case studies based on real data from three different large scale enterprises. In our experiments, eTransform is able to suggest a plan to reduce the operational costs by more than 50% from the "as-is" state of these enterprise to the consolidated enterprise IT environment. Even including the DR capability, eTransform is still able to reduce the operational costs by more than 25% from the simple "as-is" state. In our experiments, eTransform is able to simultaneously optimize multiple parameters and constraints and discover solutions that are 7x cheaper than other solutions.

Rapid growth of large-scale applications and their widespread use in research and industry has led to dramatic increases in energy consumption in enterprise data centers and large-scale distributed systems such as Grids. Any attempt at reducing the energy consumption without concern for performance can be destructive and deteriorate the overall efficiency of data centers and large-scale distributed systems running such applications. In this paper, we present an optimization model for resource management in virtualized distributed systems to minimize power costs automatically while satisfying performance constraints. The objective of our model is to keep the utilization of servers near to an optimum point to prevent performance degradation. The model includes two objective functions, one for power costs and another for performance. Using the objective functions, we present a scheduling algorithm to place a set of virtual machines on a set of servers dynamically so that to integrate power management with performance management. We show experimentally that the proposed scheduler consumes approximately 24% less energy than static power management techniques while maintaining comparable performance.Keywordspower managementperformancevirtualization technologyconsolidation

Building an enterprise data center is the need of modern enterprise management and decision-making information, SQL SERVER 2005 Integration Services provides for the establishment of a complete enterprise data center solutions. This article discusses the SSIS-based enterprise data center construction process, focusing on the working principle of the SSIS data centers and the establishment of key technologies, including data extraction, transformation for cleaning, and loading.

Chapter 5 - Session Interception Design and Deployment

Enterprise IT infrastructures and data centers are at risk from advanced cyber threats like zero-day exploits, fileless malware, insider misuse, and privilege escalation.Antivirus software and signature-based intrusion prevention are examples of traditional endpoint security solutions that still work against known attacks.However, they have trouble with new, behavior-based threats and are hard to understand.This survey looks at the latest developments in endpoint protection, including zeroday detection, insider monitoring, privilege abuse analysis, multimodal data correlation, explainable AI techniques, and adaptive model refinement through analyst feedback and deception.Profiling, ensemble anomaly detection, and deceptionenabled frameworks are used to look at these methods.