The datastore is object oriented, with each object stored as chunks of data. The Policy Manager is a distributed policy repository responsible for the definition and deployment of the policy-based configuration of the Cisco ACI. This is a collection of policies and rules applied to existing or hypothetical not yet created endpoints. The endpoint registry is a subset of the Policy Manager that tracks endpoints connecting to the Cisco ACI and their assignment to endpoint groups as defined by the policy repository.
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The physical topology is based on the information discovered by the Link Layer Discovery Protocol LLDP and the routing topology of the fabric as reported by protocols modified intermediate system to intermediate system [IS-IS] running within the fabric infrastructure space. A global view of time-accurate topology information is available in the Topology Manager, including:.
The Cisco APIC inventory management subfunction provides full identification, including model and serial number, as well as user-defined asset tags for ease of correlation with asset and inventory management systems for all ports, line cards, switches, chassis, etc. It also functions as the address allocation authority for the infrastructure network, which allows the Cisco APIC and the spine and leaf nodes to communicate. Switches then start advertising this new AV. A minimum of three controllers are initially installed for control of the scale-out Cisco ACI Figure 9.
Any controller in the cluster is able to service any user for any operation, and a controller can be seamlessly added to or removed from the Cisco APIC cluster. It also listens to hypervisor events such as VM mobility.
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The Appliance Element is a monitor for the local appliance. To understand the sharding concept, consider the concept of database partitioning. Sharding is a result of the evolution of what is called horizontal partitioning of a database. In this partitioning, the rows of the database are held separately instead of being normalized and split vertically into columns. Sharding goes further than horizontal partitioning, also partitioning the database across multiple instances of the schema. In addition to increasing redundancy, sharding increases performance because the search load for a large partitioned table can be split across multiple database servers, not just multiple indexes on the same logical server.
With sharding, large partitionable tables are split across the servers, and smaller tables are replicated as complete units. After a table is sharded, each shard can reside in a completely separated logical and physical server, data center, physical location, etc.
There is no ongoing need to retain shared access between the shards to the other unpartitioned tables located in other shards. Sharding makes replication across multiple servers easy, unlike with horizontal partitioning. Sharding, for example, reduces the number of data center interconnect links needed for database querying.
Sharding requires a notification and replication mechanism between schema instances, to help ensure that the unpartitioned tables remain as synchronized as the applications require. In situations in which distributed computing is used to separate loads between multiple servers, a shard approach offers a strong advantage. Figure 10 shows the proportion of data that is lost when the nth appliance is lost out of a total of 5 appliances and a variable replication factor of K. As the replication factor K increases, no data loss occurs unless at least K appliances are lost; also, the data loss is gradual and starts at a smaller value.
In Figure 11, L represents the number of appliances, starting with a minimum of three. Only when the third Cisco APIC appliance is lost does data loss occur, and it will be a complete loss. Increasing the number of appliances significantly and rapidly improves resilience. For example, with four appliances, as shown in the figure, losing the third appliance means a loss of 25 percent of the data.
With 12 appliances, the loss of the third appliance means only a 0.
With sharding, increasing the number of appliances can very quickly reduce the likelihood of data loss. Full replication is not needed to achieve a very high rate of data protection. The sharding technology provides scalability and reliability to the data sets generated and processed by the Distributed Policy Repository, the endpoint registry, the Observer, and the Topology Manager Figure The data for these Cisco APIC functions is partitioned into logically bounded subsets called shards analogous to database shards.
A shard is a unit of data management, and all of the above data sets are placed into shards:. One or more shards are located on each Cisco APIC appliance and processed by a controller instance located on that appliance. The shard data assignments are based on a predetermined hash function, and a static shard layout determines the assignment of shards to appliances.
Each replica in the shard has a use preference, and writes occur on the replica that is elected leader. Other replicas are followers and do not allow writes. In the case of a split-brain condition, automatic reconciliation is performed based on timestamps. An integrated Python-based scripting interface is supported that allows user-defined commands to attach to the command tree as if they were native platform-supported commands. Both class-level and tree-oriented data access is provided by the API.
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REST has emerged over the past few years as a predominant web services design model. In other words, any concept that might be the target of an author's hypertext reference must fit within the definition of a resource. A resource is a conceptual mapping to a set of entities, not the entity that corresponds to the mapping at any particular point in time. More precisely, a resource R is a temporally varying membership function M R t , which for time t maps to a set of entities, or values, which are equivalent. A resource can map to the empty set, which allows references to be made to a concept before any realization of that concept exists -- a notion that was foreign to most hypertext systems prior to the Web [ 61 ].
Some resources are static in the sense that, when examined at any time after their creation, they always correspond to the same value set.
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Others have a high degree of variance in their value over time. The only thing that is required to be static for a resource is the semantics of the mapping, since the semantics is what distinguishes one resource from another. For example, the "authors' preferred version" of an academic paper is a mapping whose value changes over time, whereas a mapping to "the paper published in the proceedings of conference X" is static.
These are two distinct resources, even if they both map to the same value at some point in time. The distinction is necessary so that both resources can be identified and referenced independently. A similar example from software engineering is the separate identification of a version-controlled source code file when referring to the "latest revision", "revision number 1. This abstract definition of a resource enables key features of the Web architecture. First, it provides generality by encompassing many sources of information without artificially distinguishing them by type or implementation.
Second, it allows late binding of the reference to a representation, enabling content negotiation to take place based on characteristics of the request.
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Finally, it allows an author to reference the concept rather than some singular representation of that concept, thus removing the need to change all existing links whenever the representation changes assuming the author used the right identifier. REST uses a resource identifier to identify the particular resource involved in an interaction between components. REST connectors provide a generic interface for accessing and manipulating the value set of a resource, regardless of how the membership function is defined or the type of software that is handling the request. The naming authority that assigned the resource identifier, making it possible to reference the resource, is responsible for maintaining the semantic validity of the mapping over time i.
Traditional hypertext systems [ 61 ], which typically operate in a closed or local environment, use unique node or document identifiers that change every time the information changes, relying on link servers to maintain references separately from the content [ ]. Since centralized link servers are an anathema to the immense scale and multi-organizational domain requirements of the Web, REST relies instead on the author choosing a resource identifier that best fits the nature of the concept being identified.
Naturally, the quality of an identifier is often proportional to the amount of money spent to retain its validity, which leads to broken links as ephemeral or poorly supported information moves or disappears over time. REST components perform actions on a resource by using a representation to capture the current or intended state of that resource and transferring that representation between components. A representation is a sequence of bytes, plus representation metadata to describe those bytes.
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Other commonly used but less precise names for a representation include: document, file, and HTTP message entity, instance, or variant. A representation consists of data, metadata describing the data, and, on occasion, metadata to describe the metadata usually for the purpose of verifying message integrity. Metadata is in the form of name-value pairs, where the name corresponds to a standard that defines the value's structure and semantics.
Response messages may include both representation metadata and resource metadata: information about the resource that is not specific to the supplied representation. Control data defines the purpose of a message between components, such as the action being requested or the meaning of a response.
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It is also used to parameterize requests and override the default behavior of some connecting elements. For example, cache behavior can be modified by control data included in the request or response message. Depending on the message control data, a given representation may indicate the current state of the requested resource, the desired state for the requested resource, or the value of some other resource, such as a representation of the input data within a client's query form, or a representation of some error condition for a response.
For example, remote authoring of a resource requires that the author send a representation to the server, thus establishing a value for that resource that can be retrieved by later requests. If the value set of a resource at a given time consists of multiple representations, content negotiation may be used to select the best representation for inclusion in a given message. The data format of a representation is known as a media type [ 48 ]. A representation can be included in a message and processed by the recipient according to the control data of the message and the nature of the media type.
Some media types are intended for automated processing, some are intended to be rendered for viewing by a user, and a few are capable of both.