Academic literature on the topic 'Compute as a Commodity (CaaC)'

This paper shows how to parallelize a compute intensive application in mathematics (Group Theory) for an institutional Desktop Grid platform coordinated by a meta-grid middleware named BonjourGrid. The paper is twofold: it shows how to parallelize a sequential program for a multicore CPU which participates in the computation; and it demonstrates the effort for launching multiple instances of the solutions for the mathematical problem with the BonjourGrid middleware. BonjourGrid is a fully decentralized Desktop Grid middleware. The main results of the paper are: a) an efficient multi-threaded version of a sequential program to compute Littlewood-Richardson coefficients, namely the Multi-LR program and b) a proof of concept, centered around the user needs, for the BonjourGrid middleware dedicated to coordinate multiple instances of programsfor Desktop Grids and with the help of Multi-LR. In this paper, the scientific work consists in starting from a model for the solution of a compute intensive problem in mathematics, to incorporate the concrete model into a middleware and running it on commodity PCs platform managed by an innovative meta Desktop Grid middleware.

SDS along with SDN and software-defined compute (SDC; where in computing is virtualized and software defined) creates software-defined infrastructure (SDI). SDI is the set of three components—SDN, SDS, and SDC—making a new kind of software-defined IT infrastructure where centralization and virtualization are the main focus. SDI is proposed to have infrastructure developed over commodity hardware and software stack defined over it. SDS is exploiting the same concept of decoupling and centralization in reference to storage solutions as in SDN. The SDN works on decoupling the control plane with the data plane from a layer, three switches, or router, and makes a centralized decision point called the controller. The SDS works in a similar way by moving the decision making from the storage hardware to a centralized server. It helps in developing new and existing storage solutions over the commodity storage devices. The centralization helps to create a better dynamic solution for satisfying the customized user need. The solutions are expected to be cheaper due to the use of commodity hardware.

Core inflation has a significant role in monetary policy decisions. Core inflation is determined by removing the temporary price changes and retaining permanent (core) price changes of the headline inflation commodity basket. Forecasting inflation is of vital interest to policymakers. Different methodologies are constructed for measuring core inflation like exclusion based method, symmetric trim method, asymmetric trim method, weighted median method, and moving average process. The inflation, a time series, was estimated by applying the ARIMA model (Iqbal et al., 2016; Habibah et al., 2017). Long memory properties of inflation are studied globally, and ARFIMA models are suggested (Hassler & Wolters, 1995; Baillie & Morano, 2012). In this study, we concentrate on the characteristics of the headline and core inflation rather than the methods to determine the core inflation. Various properties of core inflation are presented in the literature (Marques, 2003). Previous studies reported the presence of LRD behaviour in the inflation of some countries. Inspired by these facts, we examined India's monthly CPI headline inflation to check the self-similarity. Besides that, we try to identify whether the headline inflation series belong to SRD or LRD based on the Hurst exponent. The current study to compute the Hurst index is based on different approaches methods like the R/S method, Variance-Time method, Higuchi’s method and Average Periodogram method. The Hurst parameter estimate gives an idea about the strength of the self-similar nature in CPI headline inflation of India. The chapter presents a criteria for core inflation measures in terms of Hurst exponent. Then core inflation indicators for CPI inflation are selected based on the exclusion approach and examined their Hurst exponents along with other properties of core inflation for the possibility of being a core inflation measure. The present study plays a prominent role in the determination of core inflation in the Indian context. The empirical investigation has been conducted using monthly Combined CPI time series data considering the period: Jan 2012-April 2019 (base year: 2012). The CPI headline Y-o-Y inflation is computed and checked for the self-similarity behaviour by computing Hurst exponent. R programming and MS Excel tools are used for performing the analysis. The remaining work of the chapter has been planned as follows: Section 2.2 presents the overview of the time series concepts. Section 2.3 discusses the preliminaries of self-similarity, its definition and usage in various fields. Section 2.4 offers the literature review in the context of self-similarity applications. Section 2.5 discusses the different methods to compute the Hurst exponent. Section 2.6 presents the numerical results of the Hurst exponent for the CPI headline inflation. Section 2.7 establishes new criteria for core inflation based on the Hurst exponent and applies it to the conventional CPI exclusion measures. Section 2.9 presents the conclusions of the chapter.