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A CIM-based approach for managing computing
servers and hypervisors acting as active network
elements
Dimitris Kontoudis, Panayotis Fouliras
University of Macedonia
Department of Applied Informatics
156 Egnatia str., 54006, Greece
{kontoudis, pfoul}@uom.gr
Abstract. Communication network implementations present an ever increasing
use of computing servers and hypervisors assuming the role of active network elements. This fact, along with the virtualization concept which has, also, been adopted in the computer networks field, introduces new challenges that need to be accounted for when managing such networks. In this paper we propose a new modular and extensible information model which can be applied to most scenarios of network architectures where hypervisors are involved, facilitating the efficient representation and management of the provisioned computing server resources.
Keywords: Hypervisor, Network Virtualization, Modeling, Management, CIM,
SPC, Statistical Process Control
1
Introduction
The convergence of communications and computing to a common design and opera-tional entity is an inevitable reality, introducing the network engineers with new ele-ments and technologies that need to be taken into account when designing and im-plementing network architectures. The latter are increasingly based on virtualized infrastructures – both from the networking as well as from the computing realms. Some of these new elements can be quite novel and totally outside of the traditional network design scope and expertise.
Communication networks in particular implementations, for example in “testbeds” [1], in network simulation environments [2] and in scenario-based infrastructure man-agement [3], increasingly rely on the use of computing servers to act as active net-work nodes (Fig.1), as these servers allow for flexible experimentation on new archi-tectures, protocols and services. This has been made possible due to advances in serv-er virtualization (also refserv-erenced to as system or machine virtualization, implemented by a specific thin software layer, the hypervisor, also known as the virtual machine monitor/VMM). The network’s “last-hop switch” has, consequently, been shifted from the pure active network elements to become a characteristic of the hypervisor or of the physical server’s hardware [4]. In the Cloud Computing and in the Networking and Computing Infrastructure as a Service (IaaS) approaches [5], for example, the
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server loads – thus, now, the active network elements – can and should dynamically shift between physical servers in the same data center or, even, in data centers at dif-ferent geographical locations. The core networking support in server virtualization environments is based on the IEEE 802.1Q VLAN implementation. Here, virtual network segments are created on top of the server’s hardware features (the hypervisor works as a virtual Ethernet switch and supports queues for each VLAN in the sys-tem’s memory). In this way it is possible to establish network communication across different virtual servers without routing network traffic outside the physical system that hosts them, performing the virtualization.
Fig. 1. Communication networks using virtualized computing server resources
New issues are, therefore, raised and need to be considered in network design, opera-tion, monitoring and administration: the involved physical servers’ hardware re-sources (capacity of processors, memory, virtual switch etc.), the optimization of these resources so as to reach a required system performance and behavior, etc. To tackle these problems, from standardization and management points of view, it is essential that the network’s architectural, technological and operational complexity is semantically represented in a formal way, allowing for virtualization and hypervisor support. Our research has been motivated by the aforementioned issues and we worked towards designing a model that is concise and extensible so as to be able to capture virtualized components and their characteristics in a variety of architectures. We propose the “KF model” based on DMTF’s CIM standard [6]. Our proposed work can, conceptually, capture any physical or logical element that can be instrumented by CIM (thus, allowing the object’s management too), be that a networking, a computing system or other resource.
The remainder of this paper is organized as follows: section 2 provides an over-view of related research. Section 3 describes the proposed model. We conclude in section 4 by summarizing the findings of our research and presenting future direc-tions.
2
Related work
Many parties, industrial, commercial and academic alike, are actively involved in network virtualization research on a wide variety of topics, ranging from very specific
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technical issues (interfacing, signaling and bootstrapping, resource and topology dis-covery, resource allocation, admission control, virtual nodes and virtual links, naming and addressing, mobility management, monitoring, configuration and failure han-dling, security and privacy, interoperability) to large scale network implementations, like GENI [1], EmuLab [7], PlanetLab [8], PanLab [9], VINI [10][11], commonly referred to as “testbeds”. A concise survey, from a general perspective, of network virtualization research is provided in [12] and [13].
Virtualization hypervisor concepts in a modeling and management context (wheth-er networking-related or not) are, largely, ov(wheth-erlooked. Sporadic support can be found in some proposals but is limited in scope compared to the complexity and details in-volved in managing a hypervisor. Current information models treat hypervisors as transparent elements of the virtualization layer and begin abstraction form the virtual system or virtual network point. Partial and indirect support can only be found in the Common Information Model (CIM) [6] (proposed by the Distributed Management Task Force [14]), the DEN-ng model [15] as well as in Management Information Bases (MIBs) [16]. In CIM, a hypervisor (not a virtual machine) can be instantiated as a subclass via the OperatingSystem class and the built-in hypervisor virtual switch, respectively, via the UnitaryComputerSystem class. Although CIM (in the System Virtualization Model [17]) elaborates on modeling and management actions on a vir-tual machine and on its host computer system, it does not account for the hypervisor layer. DEN-ng, in a similar fashion, could be extended via subclassing from either the PhysicalResource and LogicalResource or the VirtualSystem and VirtualImage clas-ses. In the MIBs domain the only relevant references are the VM-MIB [18] and the VMM-MIB [19], both at IETF draft status. These MIB objects can store basic hyper-visor information (list of guest virtual machines, virtual CPU information and map-pings of logical storage and network interfaces). Current hypervisor technologies are very complex and incorporate several details and operational specifics than what can be abstracted and managed by current models.
3
The proposed information model
To meet the need for semantic representation of virtualized computing server re-sources provisioned to computer networks we propose the KF model, a novel CIM-based conceptual representation of the different components that constitute a virtual machine-based network. The KF model can cover the physical and logical compo-nents supporting the virtual network along with its settings, modes of operation and statistical elements of the hypervisors and virtual machines involved. The KF model is extensible so as to include, nearly, any new element that needs to be introduced, in a hardware-agnostic way. As a result, the model can be applied to a wide variety of scenarios and is not depended on any particular hardware implementation. This model semantically incorporates, at the logical level, a virtual network spanning a number of virtual server hosts (which act as active network elements and provide its core re-sources) along with the virtualization techniques (physical nodes, hypervisors, virtual machines – VMs) [20] employed in such a design. System provisioned resources
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(such as CPUs, memory and I/O) as well as other relevant operational parameters are included in the model. The model’s representation of the virtual machine includes methods for the control of the provisioned resources based on statistical analysis of their performance. Given the agnostic nature of the model all virtualization platforms are supported as long as proper providers are developed adhering to the CIM ap-proach.
3.1 Conceptual Design
The KF model consists of ten classes (Fig.2) representing the virtual machine, net-work, configuration and operation parts of the NVE architecture1. These classes, along with the extensibility characteristics of the model, are adequate for representing the basic design of any network employing virtual machines hosted on a hypervisor (this approach is of similar nature regardless the choice of hypervisor). Additional features and facilities can be abstracted by extending the model, thus eliminating the need for initial classes overcommit which would incur difficulties in the model de-sign.
Fig. 2. KF model sample classes and associations1
An exclusive namespace has been applied to class naming by which each class name is prefixed with a “KF_” convention. In the current version of the model the following elements of a virtual host based network are referenced: computing (sys-tems) nodes, hypervisors, hypervisor virtual switches, virtual machines, processes, applications, virtual networks along with their settings and statistics. All these ele-ments are semantically represented at the logical level and the virtual network envi-ronment, as a whole entity, is conceived as a collection of the referenced entities. This design approach allows for the simplification of handling and for the consolidation of global characteristics, such as settings, statistics, naming etc. The virtual machine part uses six classes handling the physical server infrastructure as an hardware node with a running hypervisor, a number of participating VMs, and the processes and applica-tions running on these VMs. Intra-node networking is, in part, represented by a spe-cific “KF_HypervisorVirtualSwitch” class which details the hypervisor’s in-memory
1
Detailed UML diagrams, complete textual class descriptions as well as MOFs are available at http://users.uom.gr/~kontoudis/research/
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IEEE802.1Q VLAN compatible virtual switch. Networking information is, also, shared with the “KF_Network” class which, also, includes the properties needed for mapping a virtual host based network notion as a whole entity. A special class is used for handling statistical data resulting in a total of three classes dealing with network-ing information. A number of associations have been designed which, benetwork-ing double-ended references, return specific operational data depending on the invocation method (i.e. reporting how may virtual Ethernet adapters are allocated per VM, which is the physical node’s running hypervisor, which applications operate per VM and per virtu-al network, etc.)
The KF model design allows for the inclusion of any manageable entity by imple-menting proper extensions which can augment the model’s scope and, thus, the man-aging application’s (that makes use of the model) functionality. For example, suppose that the need arises for the handling of transaction-based performance characteristics or for the management of a virtual router instantiated by a virtual server. The former need can be tackled by a CIM_UnitOfWork derivative subclass [21] whereas the lat-ter need via extending the KF_VM class to include the required management meth-ods. This extensibility of the KF model derives from its design logic and allows for the easy inclusion of new features and elements. CIM schemas are expressed in UML and their syntax description is composed in the Managed Object Format (MOF) [22] (a .mof file is a text file that defines the class name and attributes of a managed re-source).
4
Conclusions and Future Work
Recent technological advancements, where virtualized computing play an integral part in computer networks, complicate the network’s architecture, operation and man-agement, introducing new aspects that need to be considered for proper end-to-end service delivery. In this paper we introduced the KF model, a CIM-based approach showing that standardization of the representation of virtual networks where compu-ting servers are involved is, indeed, feasible. The model allows for the conceptual representation of involved components and for the introduction of targeted actions against them. Our ongoing work focuses on enhancing the KF model with semantics for performance patterns and characteristics of network and system performance. Furthermore, we investigate the application of statistical process control, by means of operating system techniques, for creating dynamically adaptive virtual machine per-formance, adhering to SLA-specified constraints.
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