Common Information Model (computing)

Summary

The Common Information Model (CIM) is an open standard that defines how managed elements in an IT environment are represented as a common set of objects and relationships between them. The Distributed Management Task Force maintains the CIM to allow consistent management of these managed elements, independent of their manufacturer or provider. Overview One way to describe CIM is to say that it allows multiple parties to exchange management information about these managed elements. However, this falls short of fully capturing CIM's ability not only to describe these managed elements and the management information, but also to actively control and manage them. By using a common model of information, management software can be written once and work with many implementations of the common model without complex and costly conversion operations or loss of information. The CIM standard is defined and published by the Distributed Management Task Force (DMTF). A related standard

Official source

https://en.wikipedia.org/wiki/Common_Information_Model_(computing)

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A Gaussian Source Coding Perspective on Caching and Total Correlation

Communication technology has advanced up to a point where children are getting unfamiliar with themost iconic symbol in IT: the loading icon. We no longer wait for something to come on TV, nor for a download to complete. All the content we desire is available in instantaneous and personalized streams. Whereas users benefit tremendously from the increased freedom, the network suffers. Not only do personalized data streams increase the load overall, the instantaneous aspect concentrates traffic around peak hours. The heaviest (mostly video) applications are used predominantly during the evening hours. Caching is a tool to balance traffic without compromising the ‘on-demand’ aspect of content delivery; by sending data in advance a server can avoid peak traffic. The challenge is, of course, that in advance the server has no clue what data the user might be interested in. We study this problem in a lossy source coding setting with Gaussian sources specifically, using amodel based on the Gray–Wyner network. Ultimately caching is a trade-off between anticipating the precise demand through user habits versus ‘more bang for buck’ by exploiting correlation among the files in the database. For two Gaussian sources and using Gaussian codebooks we derive this trade-off completely. Particularly interesting is the case when the user has no preference for some content a-priori, caching then becomes an application of the concepts ofWyner’s common information and Watanabe’s total correlation. We study these concepts in databases of more than two sources where we derive that caching all of the information shared by multiple Gaussians is easy, whereas caching some is hard. We characterize the former, provide an inner bound for the latter and conjecture for which class of Gaussians it is tight. Later we also study how to most efficiently capture the total correlation that exists between two sets of Gaussians. As a final chapter, we study the applicability of caching of discrete information sources by actually building such algorithms, using convolutional codes to ‘cache and compress’. We provide a proof of concept of the practicality for doubly symmetric and circularly symmetric binary sources. Lastly we provide a discussion on challenges to be overcome for generalizing such algorithms.

EPFL2017

Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular networks including channel coding such as Gaussian multiple access channel with feedback and source coding such as lossy Gaussian Gray-Wyner network. On one part, we establish the sum-Capacity of the Gaussian multiple-access channel with feedback. The converse bounds that are derived from the dependence-balance argument of Hekstra and Willems meet the achievable scheme introduced by Kramer. Even though the problem is not convex, the factorization of lower convex envelope method that is introduced by Geng and Nair, combined with a Gaussian property are invoked to compute the sum-Capacity. Additionally, we characterize the rate region of lossy Gaussian Gray-Wyner network for symmetric distortion. The problem is not convex, thus the method of factorization of lower convex envelope is used to show the Gaussian optimality of the auxiliaries. Both of the networks, are a long-standing open problem. On the other part, we consider the common information that is introduced by Wyner and the natural relaxation of Wyner's common information. Wyner's common information is a measure that quantifies and assesses the commonality between two random variables. The operational significance of the newly introduced quantity is in Gray-Wyner network. Thus, computing the relaxed Wyner's common information is directly connected with computing the rate region in Gray-Wyner network. We derive a lower bound to Wyner's common information for any given source. The bound meets the exact Wyner's common information for sources that are expressed as sum of a common random variable and Gaussian noises. Moreover, we derive an upper bound on an extended variant of information bottleneck. Finally, we use Wyner's common information and its relaxation as a tool to extract common information between datasets. Thus, we introduce a novel procedure to construct features from data, referred to as Common Information Components Analysis (CICA). We establish that in the case of Gaussian statistics, CICA precisely reduces to Canonical Correlation Analysis (CCA), where the relaxing parameter determines the number of CCA components that are extracted. In this sense, we establish a novel rigorous connection between information measures and CCA, and CICA is a strict generalization of the latter. Moreover, we show that CICA has several desirable features, including a natural extension to beyond just two data sets.

EPFL2021

Caching Gaussians: Minimizing Total Correlation on the Gray–Wyner Network

Michael Christoph Gastpar

We study a caching problem that resembles a lossy Gray–Wyner network: A source produces vector samples from a Gaussian distribution, but the user is interested in the samples of only one component. The encoder first sends a cache message without any knowledge of the user’s preference. Upon learning her request, a second message is provided in the update phase so as to attain the desired fidelity on that component. The cache is efficient if it exploits as much of the correlation in the source as possible, which connects to the notions of Wyner’s common information (for high cache rates) and Watanabe’s total correlation (for low cache rates). For the former, we extend known results for 2 Gaussians to multivariates by showing that common information is a simple linear program, which can be solved analytically for circulant correlation matrices. Total correlation in a Gaussian setting is less well-studied. We show that for bivariates and using Gaussian auxiliaries it is captured in the dominant eigenvalue of the correlation matrix. For multivariates the problem is a more difficult optimization over a non-convex domain, but we conjecture that circulant matrices may again be analytically solvable.

Ieee2016

EPFL2021

A Gaussian Source Coding Perspective on Caching and Total Correlation

EPFL2017