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Standardization Efforts for Traditional Data Center Infrastructure Management: The Big Picture
Abstract
Traditional data center infrastructure suffers from a lack of standard and ubiquitous management solutions. Despite the achieved contributions, existing tools lack interoperability and are hardware dependent. Vendors are already actively participating in the specification and design of new standard software and hardware interfaces within different forums. Nevertheless, the complexity and variety of data center infrastructure components that includes servers, cooling, networking, and power hardware, coupled with the introduction of the software defined data center paradigm, led to the parallel development of a myriad of standardization efforts. In an attempt to shed light on recent works, we survey and discuss the main standardization efforts for traditional data center infrastructure management.
... The [1] traditional data centre is also tightly coupled with management software, creating interoperatabilty issues. To overcome all these problems this chapter provides a broad view on Modern Data centre software-defined solutions to maintain integrated standard management ecosystem or framework [2]. To bring efficiency, agility, energy consumption we are dramatically moving towards Next-Generation Modern data centres for environmental sustainability with recycling efficiency that reduces emissions footprint [3]. ...
Data centres playing a vital role in expanding the business for enterprises with intelligent solutions. They have prominently improved the usability of data as a whole. The existing traditional data centre is still in use at most of the on-premises organization. It is also called "siloed" data centre which mostly relies on Hardware machines with more manual interventions and configurations sometimes lead to error-prone. The storage drive is of monolithic spinning Magnetic-disk based Networked Attached Storage (NAS) or Storage Area Network (SAN) array. These resources also require more flooring space with cooling may consume more power and incur cost. The proposed Modern data centre which is based on software-defined technology which requires less physical resources with unified user interface, highly virtualized, easy configuration, centralized administration and with high speed network fabrics for faster, lossless data transfer with rapid resource provisioning. The SLI (Software-Led infrastructure) can handle dynamic workloads with intelligent automated resource allocation which is highly scalable. The storage is of solid state or flash storage which requires less power to operate on comparing the spinning disk. In Modern data centres Virtualization, consolidation and fluid resource pooling enables efficient better, accurate utilization of resources and provisioning with High Availability (HA) which saves cost and energy also reduces the carbon footprints to preserve the ecosystem for the next generation and improves digital business agility.
... So procuring the new hardware from various vendors may consume time and cost to replace the failed component [2]. Traditional on-premise data centre infrastructure also suffers from standards, lack of interoperatabilty, more hardware dependency and omnipresent management solutions [3]. In on-premises Traditional DC, Enterprises invests costs in more hardware like servers, storage and network equipment's to meet specific application needs required by their organization employees to process their workloads and keep them secure within their datacenter privately. ...
In this Modern Era, Datacenters are the backbone of today's enterprise businesses. To expand their business growth, an organization should first identify whether they are going to stay with on-premises or cloud infrastructure. In this paper we are going to explore the evolution towards on-premises Modern Data Centre (DC) from the basic classical, "siloed" traditional infrastructure. The old traditional data centre relies on more hardware and physical servers, needs more individual team to maintain, consumes more electricity, incurs more cost on buying machines for extra workloads when company grows. Maintaining the data securely is also a crucial task in siloed infrastructure because it needs more configuration and administration task which may lead to error-prone. The Next level of converged infrastructure in which the configurations are of pre-defined bundled templates which cannot be scaled further. The one more level-up the existing on-premise hyper-converged infrastructure in which CPU and storage will be coupled in one plane and network is in another plane and here when scalability is needed extra nodes to be created completely again is an additional overhead. In this paper the proposed Composable Infrastructure which supports all kinds of traditional and modern workloads with fluid pool of independent resource provisioning can be done intelligently through predictive unified API with more scalability, High availability and agility by means of the technologies like virtualization and containerization like the power of cloud infrastructure and services. Moreover, the software industry is highly fluid in this present decade to adapt the old software model to fit with evolving containerized Micro-services based applications which requires greater scalability and fast deployment. Hence the evolved on-premises composable modern infrastructure which is more dynamic in provisioning the resources with unlimited scaling is compared with other existing infrastructures with various parameters like Energy efficiency, High Availability, Cost and Agility.
Data center is an excellent power-consuming load in current public service. According to the rapid upgrading rate, the traditional power supplement based on fossil fuels have some side effects like efficiency and environmental influence. Because of its feasibility and low environmental impact, renewable energy is regarded as an ideal substitute for fossil fuels, and it is used as the energy of the data center. This paper aims to explore the application potential of renewable energy in data center operation, which is mainly based on solar energy. The current generated by the photovoltaic panel and the working current of the data center is both direct current (DC). With the application of solar energy, power loss during the conversion between the power grid and data center could be avoided. The second part of this paper is the application example of solar energy utilization in the Tencent T-Block data center.KeywordsRenewable energyDC data centerDC gridSolar energyT-block data center
A data center infrastructure is composed of heterogeneous resources divided into three main subsystems: IT (processor, memory, disk, network, etc.), power (generators, power transformers, uninterruptible power supplies, distribution units, among others), and cooling (water chillers, pipes, and cooling tower). This heterogeneity brings challenges for collecting and gathering data from several devices in the infrastructure. In addition, extracting relevant information is another challenge for data center managers. While seeking to improve the cloud availability, monitoring the entire infrastructure using a variety of (open source and/or commercial) advanced monitoring tools, such as Zabbix, Nagios, Prometheus, CloudWatch, AzureWatch, and others is required. It is often common to use many monitoring systems to collect real‐time data for data center components from different subsystems. Such an environment brings an inherent challenge stemming from the need to aggregate and organize the whole collected infrastructure data and measurements. This first step is necessary prior to obtaining any valuable insights for decision‐making. In this paper, we present the Data Center Availability (DCA) System, a software system that is able to aggregate and analyze data center measurements aimed toward the study of DCA. We also discuss the DCA implementation and illustrate its operation, monitoring a small University research laboratory data center. The DCA System is able to monitor different types of devices using the Zabbix tool, such as servers, switches, and power devices. The DCA System is able to automatically identify the failure time seasonality and trend present in the collected data from different devices of the data center.
Next‐generation cloud data centers are based on software‐defined data center infrastructures that promote flexibility, automation, optimization, and scalability. The Redfish standard and the Intel Rack Scale Design technology enable software‐defined infrastructure and disaggregate bare‐metal compute, storage, and networking resources into virtual pools to dynamically compose resources and create virtual performance‐optimized data centers (vPODs) tailored to workload‐specific demands. This article proposes four chassis design configurations based on Distributed Management Task Force's Redfish industry standard applied to compose vPOD systems, namely, a fully shared design, partially shared homogeneous design, partially shared heterogeneous design, and not shared design; their main difference is based on the used hardware disaggregation level. Furthermore, we propose models that combine reliability block diagram and stochastic Petri net modeling approaches to represent the complexity of the relationship between the pool of disaggregated hardware resources and their power and cooling sources in a vPOD. These four proposed design configurations were analyzed and compared in terms of availability and component's sensitivity indexes by scaling their configurations considering different data center infrastructure. From the obtained results, we can state that, in general, when one increases the hardware disaggregation, availability is improved. However, after a given point, the availability level of the fully shared, partially shared homogeneous, and partially shared heterogeneous configurations remain almost equal, while the not shared configuration is still able to improve its availability.
We present our approach for overcoming the cost, operational complexity, and limited scale endemic to datacenter networks a decade ago. Three themes unify the five generations of datacenter networks detailed in this paper. First, multi-stage Clos topologies built from commodity switch silicon can support cost-effective deployment of building-scale networks. Second, much of the general, but complex, decentralized network routing and management protocols supporting arbitrary deployment scenarios were overkill for single-operator, pre-planned datacenter networks. We built a centralized control mechanism based on a global configuration pushed to all datacenter switches. Third, modular hardware design coupled with simple, robust software allowed our design to also support inter-cluster and wide-area networks. Our datacenter networks run at dozens of sites across the planet, scaling in capacity by 100x over ten years to more than 1Pbps of bisection bandwidth.
Non-intrusive remote monitoring of data centre services should be such that it does not require
(or minimal) modification of legacy code and standard practices. Also, allowing third party
agent to sit on every server in a data centre is a risk from security perspective. Hence, use of
standard such as SNMPv3 is advocated in this kind of environment. There are many tools (open
source or commercial) available which uses SNMP; but we observe that most of the tools do not
have an essential feature for auto-discovery of network. In this paper we present an algorithm
for remote monitoring of services in a data centre. The algorithm has two stages: 1) auto
discovery of network topology and 2) data collection from remote machine. Further, we
compare SNMP with WBEM and identify some other options for remote monitoring of services
and their advantages and disadvantages.
Large-scale data centers enable the new era of cloud computing and provide the core infrastructure to meet the computing and storage requirements for both enterprise information technology needs and cloud-based services. To support the ever-growing cloud computing needs, the number of servers in today's data centers are increasing exponentially, which in turn leads to enormous challenges in designing an efficient and cost-effective data center network. With data availability and security at stake, the issues with data center networks are more critical than ever. Motivated by these challenges and critical issues, many novel and creative research works have been proposed in recent years. In this paper, we investigate in data center networks and provide a general overview and analysis of the literature covering various research areas, including data center network interconnection architectures, network protocols for data center networks, and network resource sharing in multitenant cloud data centers. We start with an overview on data center networks and together with its requirements navigate the data center network designs. We then present the research literature related to the aforementioned research topics in the subsequent sections. Finally, we draw the conclusions.
Runtime configuration validation is a critical requirement if we are to build reliable self-adaptive management systems. This paper presents a generic framework that includes a runtime configuration checker built upon a high-level language dedicated to the specification of configurations and validity constraints. In addition, we describe a methodology for using this framework and integrating the configuration checker with existing management systems. In particular, we show how we use the framework to enrich a CIM/WBEM management environment with automatic runtime configuration validation against a defined set of constraints guarding structural correctness and service behavior conformance. Our experiments with management models conforming to the CIM Virtual System profile show viable results demonstrating the feasibility of our approach.
The role of data analytics increases in several application domains to cope with the large amount of captured data. Cloud computing has become one of the key considerations both in academia and industry. Cheap, seemingly unlimited computing resources that can be allocated almost instantaneously and pay-as-you-go pricing schemes are some of the reasons for the success of Cloud computing .In this paper we discuss few aspects of cloud computing and also there area. We also propose a novel approach which is cloud computing mapping and management through class and object hierarchy. In this approach we first design a cloud environment where we can analyze several object oriented aspects based on some assumptions. Then we deduce message passing behavior through a backup files based on the properties of object orient like class and object.
Information and Communication Technology (ICT) devices and services are becoming more and more widespread in all aspects of human life. Following an increased worldwide focus on the environmental impacts of energy consumption in general, there is also a growing attention to the electricity consumption associated with ICT equipment.
In this paper we assess how ICT electricity consumption in the use phase has evolved from 2007 to 2012 based on three main ICT categories: communication networks, personal computers, and data centers. We provide a detailed description of how we calculate the electricity use and evolution in these three categories.
Our estimates show that the yearly growth of all three individual ICT categories (10%, 5%, and 4% respectively) is higher than the growth of worldwide electricity consumption in the same time frame (3%). The relative share of this subset of ICT products and services in the total worldwide electricity consumption has increased from about 3.9% in 2007 to 4.6% in 2012. We find that the absolute electricity consumption of each of the three categories is still roughly equal. This highlights the need for energy-efficiency research across all these domains, rather than focusing on a single one.
Large data centers are complex systems that depend on several generations of hardware and software components, ranging from legacy mainframes and rack-based appliances to modular blade servers and modern rack scale design solutions. To cope with this heterogeneity, the data center manager must coordinate a multitude of tools, protocols, and standards. Currently, data center managers, standardization bodies, and hardware/software manufacturers are joining efforts to develop and promote Redfish as the main hardware management standard for data centers, and even beyond the data center. The authors hope that this article can be used as a starting point to understand how Redfish and its extensions are being targeted as the main management standard for next-generation data centers. This article describes Redfish and the recent collaborations to leverage this standard.
This paper provides an overview of Software-Defined “Hardware” Infrastructures (SDHI). SDHI builds upon the concept of hardware (HW) resource disaggregation. HW resource disaggregation breaks today’s physical server-oriented model where the use of a physical resource (e.g., processor or memory) is constrained to a physical server’s chassis. SDHI extends the definition of Software-Defined Infrastructures (SDI) and brings greater modularity, flexibility, and extensibility to cloud infrastructures, thus allowing cloud operators to employ resources more efficiently and allowing applications not to be bounded by the physical infrastructure’s layout. This paper aims to be an initial introduction to SDHI and its associated technological advancements. The paper starts with an overview of the cloud domain and puts into perspective some of the most prominent efforts in the area. Then, it presents a set of differentiating use-cases that SDHI enables. Next, we state the fundamentals behind SDI and SDHI, and elaborate why SDHI is of great interest today. Moreover, it provides an overview of the functional architecture of a cloud built on SDHI, exploring how the impact of this transformation goes far beyond the cloud infrastructure level in its impact on platforms, execution environments, and applications. Finally, an in-depth assessment is made of the technologies behind SDHI, the impact of these technologies, and the associated challenges and potential future directions of SDHI. IEEE
Traditional management standards are often insufficient to manage modern data centers at large scale, which motivates the community to propose and develop new management standards. The most popular traditional standard for monitoring and controlling the health and functionality of a system at hardware layer is Intelligent Platform Management Interface (IPMI). Redfish is a new hardware-based management technology designed as the next-generation management standard. The goal of this study is to investigate hardware management technologies and to find out if they are powerful enough to meet demands of modern data centers. Particularly, we focused on Redfish and IPMI, and we benchmarked and compared them from four different aspects: latency, scalability, reliability, and security. Our result shows that there is a trade-off between improving the performance of a system and increasing the security and the reliability of that. Our results show that Redfish is more secure and more reliable, but the performance of IPMI tends to be better.
Guaranteeing high levels of availability is a huge challenge for cloud providers. The authors look at the causes of cloud failures and recommend ways to prevent them and to minimize their effects when they occur.
The operation of today’s data centers increasingly relies on environmental data collection and analysis to operate the cooling infrastructure as efficiently as possible and to maintain the reliability of IT equipment. This in turn emphasizes the importance of the quality of the data collected and their relevance to the overall operation of the data center. This study presents an experimentally based analysis and comparison between two different approaches for environmental data collection; one using a discrete sensor network, and another using available data from installed IT equipment through their Intelligent Platform Management Interface (IPMI). The comparison considers the quality and relevance of the data collected and investigates their effect on key performance and operational metrics. The results have shown the large variation of server inlet temperatures provided by the IPMI interface. On the other hand, the discrete sensor measurements showed much more reliable results where the server inlet temperatures had minimal variation inside the cold aisle. These results highlight the potential difficulty in using IPMI inlet temperature data to evaluate the thermal environment inside the contained cold aisle.
The study also focuses on how industry common methods for cooling efficiency management and control can be affected by the data collection approach. Results have shown that using preheated IPMI inlet temperature data can lead to unnecessarily lower cooling set points, which in turn minimizes the potential cooling energy savings. It was shown in one case that using discrete sensor data for control provides 20% more energy savings than using IPMI inlet temperature data.
Today's data centers increasingly rely on environmental data collection and analysis to operate the cooling infrastructure as efficiently as possible and to maintain the reliability of IT equipment. This in turn emphasizes the importance of the quality of data collected and its relevance to the overall operation of the data center. This study presents an experiment based analysis and comparison of environmental and power data collection using two different approaches; one uses a discrete sensor network and smart PDUs, and another uses available data from the installed IT equipment (IPMI data). The comparison looks deeply into the effect of both approaches that are adopted to control data center cooling. In addition, the effect that the Supply Air Temperature (SAT) from the Computer Room Air Handler (CRAH) unit had on the IT equipment was investigated in fully sealed Cold Aisle Containment (CAC) with 100% CPU utilization. It can be observed that the difference between the discrete and IPMI inlet temperature of the IT equipment increased as SAT increased due to the IT fans increasing speed in an attempt to get more cooling and the resulting in negative pressure differential build up inside the containment. Furthermore, the authors identified a value of the supply air temperature at which IT equipment started to ramp up for both approaches of data center cooling and control. The novelty of this study may aid data center operators when making the decision of what monitoring or control scheme to use.
In today's digital era with the development of various types of gadgets causes the growth of traffic and the number of internet service on the network. To provide the maximum monitoring system that can determine service availability, network conditions, uptime and downtime. Protocol monitoring system used in this research is the Simple Network Management Protocol (SNMP). Due to the use of SNMP protocol generates monitoring data in the form raw data, it is necessary to use intermediaries applications so in order to make process monitoring become more efficient with the appearance of the graph. To optimize the monitoring system this research design, manufacture and testing the technique for all interfaces that exist on the agent. The aim of testing this new technique is to determine the availability of devices and system availability with multiple testing, ie; the test of application, network mapping, network conditions, TCP trafic, bandwidth optimization and sms notification.
The rapid growth of cloud computing, both in terms of the spectrum and volume of cloud workloads, necessitate re-visiting the traditional rack-mountable servers based datacenter design. Next generation datacenters need to offer enhanced support for: (i) fast changing system configuration requirements due to workload constraints, (ii) timely adoption of emerging hardware technologies, and (iii) maximal sharing of systems and subsystems in order to lower costs. Disaggregated datacenters, constructed as a collection of individual resources such as CPU, memory, disks etc., and composed into workload execution units on demand, are an interesting new trend that can address the above challenges. In this paper, we demonstrated the feasibility of composable systems through building a rack scale composable system prototype using PCIe switch. Through empirical approaches, we develop assessment of the opportunities and challenges for leveraging the composable architecture for rack scale cloud datacenters with a focus on big data and NoSQL workloads. In particular, we compare and contrast the programming models that can be used to access the composable resources, and developed the implications for the network and resource provisioning and management for rack scale architecture.
We present our approach for overcoming the cost, operational complexity, and limited scale endemic to datacenter networks a decade ago. Three themes unify the five generations of datacenter networks detailed in this paper. First, multi-stage Clos topologies built from commodity switch silicon can support cost-effective deployment of buildingscale networks. Second, much of the general, but complex, decentralized network routing and management protocols supporting arbitrary deployment scenarios were overkill for single-operator, pre-planned datacenter networks. We built a centralized control mechanism based on a global configuration pushed to all datacenter switches. Third, modular hardware design coupled with simple, robust software allowed our design to also support inter-cluster and wide-Area networks. Our datacenter networks run at dozens of sites across the planet, scaling in capacity by 100x over 10 years to more than 1 Pbps of bisection bandwidth. A more detailed version of this paper is available at Ref.21.
This article reports experimental and numerical testing performed to characterize the operation and reliability of
the open compute (OC) storage system in contained environment from server to aisle levels. The study is comprised
of three parts. The first part is an experimental analysis of the high density (HD) 3D array storage unit thermal and
utilization responses during airflow imbalances. This is done with the stress test proposed for IT in containment to
mimic possible mismatch and cascade failure scenarios. It is found that downstream HDDs are most prone to
overheating and loss in utilization during an airflow imbalance. This was proven to undermine the storage capacity
of the hard disk drives. An IT level airflow prediction model is discussed for the storage unit and validated for
different fan speeds. In the second part, a computational fluid dynamics model is created for a high density open
rack based on the active flow curve method. Here, the measured airflow response curves for the open compute IT
(storage and compute servers) are used to build compact models and run rack level testing for IT air systems
sensitivity and create a rack level AFC (active flow curve) airflow demand prediction model. Finally, the
experimental characterization data is used to build an aisle level model (POD) that incorporates IT fan control
systems (FCS). This modeling approach yields shorter uptime during chiller failure due to increased recirculation
induced by increased IT airflow demand during cases such as chiller failure or high economizer temperatures.
This column completes a two-part exploration into features of application programming interfaces (APIs) that are useful in clouds. The discussion contrasts APIs with other types of interfaces and describes variations on protocols and calling methods, giving examples from physical hardware control to illustrate important features of cloud API design.
Network devices will always be heterogeneous, both in the functionality they provide and in the way they represent and use management data. This adversely affects interoperability and makes management of networks and networked applications more difficult. This chapter describes the motivation and design of the FOCALE autonomic networking architecture. FOCALE is based on the following core principles: (1) use a combination of information and data models to establish a common "lingua franca" to map vendor- and technology-specific functionality to a common platform-, technology-, and language independent form, (2) augment this with ontologies to attach formally defined meaning and semantics to the facts defined in the models, (3) use the combination of models and ontologies to discover and program semantically similar functionality for heterogeneous devices independent of the data and language used by each device, (4) use context-aware policy management to govern the resources and services provided, (5) use multiple-control loops to provide adaptive control to changing context, and (6) use multiple machine learning algorithms to enable FOCALE to be aware of both itself and of its environment in order to reduce the amount of work required by human administrators. This chapter first motivates the need for autonomic systems and explains why a well-known but simple example of an autonomic control loop is not sufficient for network management purposes. It uses these deficiencies as motivation to explain the rationale behind the original FOCALE autonomic architecture. The chapter concludes with a discussion of how knowledge is represented in FOCALE.
This column provides examples of data access patterns ranging from completely local user-driven methods to cloud-based tools to illustrate concepts related to application programmer interface (API) design, and the use and role of API concepts in cloud computing.
Network Topology Discovery and Inventory Listing are two of the primary features of modern network monitoring systems (NMS). Current NMSs rely heavily on active scanning techniques for discovering and mapping network information. Although this approach works, it introduces some major drawbacks such as the performance impact it can exact, specially in larger network environments. As a consequence, scans are often run less frequently which can result in stale information being presented and used by the network monitoring system. Alternatively, some NMSs rely on their agents being deployed on the hosts they monitor. In this article, we present a new approach to Network Topology Discovery and Network Inventory Listing using only passive monitoring and scanning techniques. The proposed techniques rely solely on the event logs produced by the hosts and network devices present within a network. Finally, we discuss some of the advantages and disadvantages of our approach.
The increase in complexity of computer networks and their services have boosted the development of standardizations, models, and solutions for network management over the years. Lately, the Distributed Management Task Force (DMTF) defined the Common Information Model (CIM) for describing computational entities and businesses on the Internet. This paper proposes an extension of the CIM for Software-Defined Networking (SDN) by adding new elements (Controllers, Apps, Slices and others) to improve the system management performance. Furthermore, we define a metamodel to help the process of creating and understanding the proposed model. The proposal was validated by creating a script that generates the FlowVisor configuration file using the network model as input and using Object Constraint Language (OCL) to find inconsistencies in the network.
The NOVI Information Model (IM) and the corresponding data models are the glue between the software components in the NOVI Service Layer. The IM enables the communication among the various components of the NOVI Architecture and supports the various functionalities it offers. The NOVI IM consists of three main ontologies: resource, monitoring and policy ontology that have evolved over time to accommodate the emerging requirements of the NOVI architecture. This article presents the NOVI IM and its ontologies, together with an overview of how the NOVI software prototypes have benefited from using the IM.
The recently emerged Cloud Computing paradigm poses new management challenges because of its complex, heterogeneous infrastructure. A cloud contains infrastructure (Servers, Storage, Networks), applications (web apps, database, backup etc.) from various vendors. Generally, different vendor products are managed (discovery, provisioning, monitoring etc.) by their own proprietary management software. Today, in clouds there is no standard way to manage infrastructure and applications using a single management framework. This will cause cloud management a complex task and creates interoperability issues. The Cloud infrastructure cannot be easily replaced due to dependency on the management software. In this paper we will present various independent CIM (Common Information Model) based Management models available as today, their applicability to cloud infrastructure, advantages etc.
Virtualized environments today allow managing and migrating workloads more flexibly such that goals of minimizing power usage in data centers can be pursued. Automated closed-loop controllers are often used for exercising control over workload placement and migration in a data center. The combination with power, airflow and temperature control can even more contribute to energy efficiency in a data center crossing the traditionally separated domains of IT management and facility management. These Power/Workload Control Systems (PWCS) are actively managing IT systems and their behaviors - changes that have impact on other IT management systems in the data center. Consequently, PWCS should be carefully integrated into an overall data center IT management architecture such that changes affected by the PWCS are properly propagated to other IT management systems and vice versa, definitions for the PWCS (e.g. about their control domain and their control policies) are obtained from centrally managed repositories such as CMDB. The reality, however, is that autonomous control systems are constructed and operated in isolation from other IT management systems in a data center. This paper describes how an autonomous PWCS can be integrated into an IT management architecture and can be connected with other management systems that are used in a data center.
Ever increasing data center complexity poses a significant burden on IT administrators. This burden can become unbearable without the help of self-managed systems that monitor themselves and automatically modify their state in order to carry out business processes according to high level objectives set by service level agreements (SLA) and policies. Among the key IT management tasks that must be automated and enhanced to realize the idea of an autonomic and highly dynamic data center, are discovery, configuration, and provisioning of new servers. In this direction, this paper describes pre-boot capabilities endowing the bare metal server with the ability to be discovered, queried, configured, and provisioned at time zero using industry standards like Common Information Model (CIM), CIM-XML, and Service Location Protocol (SLP). The capabilities are implemented as a payload of an Intel® Extensible Firmware Interface (EFI)-compliant BIOS, the Intel® Rapid Boot Toolkit (IRBT), allowing a resource manager to discover a new server during pre-boot, possibly in a bare-metal state, and then perform an asset inventory, configure the server including CPU-specific settings, and provision it with the most appropriate image. All these tasks may be carried out based on decisions taken by the resource manager according to server capabilities, application requirements, SLAs, and high-level policies. Additionally, this system uses reliable protocols, thus minimizing error possibilities.
Interoperability between management systems is significantly altered by an overload of data, along with syntactic and semantic dissonance of management information. New service-oriented architectures have been proposed to facilitate the decentralization of the control and to add more flexibility in the management process. However, a little was done towards the reuse of legacy information in nowadays modeling framework. In this paper, we demonstrate how existing information definition can be reused and integrated using semantic Web technologies. Our objective is to enhance SNMP and WBEM solutions with semantic Web services technologies, thus, allowing interoperability between heterogeneous systems and truly facilitating the design, the deployment and the maintenance of network management systems.
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