Распределенные горизонтально масштабируемые решения для управления данными
Many modern applications (such as large-scale Web-sites, social networks, research projects, business analytics, etc.) have to deal with very large data volumes (also referred to as “big data”) and high read/write loads. These applications require underlying data management systems to scale well in order to accommodate data growth and increasing workloads. High throughput, low latencies and data availability are also very important, as well as data consistency guarantees. Traditional SQL-oriented DBMSs, despite their popularity, ACID transactions and rich features, do not scale well and thus are not suitable in certain cases. A number of new data management systems and approaches have emerged over the last decade intended to resolve scalability issues. This paper reviews several classes of such systems and key problems they are able to solve. A large variety of systems and approaches due to the general trend toward specialization in the field of SMS: every data management system has been adapted to solve a certain class of problems. Thus, the selection of specific solutions due to the specific problem to be solved: the expected load, the intensity ratio of read and write, the form of data storage and query types, the desired level of consistency, reliability requirements, the availability of client libraries for the selected language, etc.
Big data challenged traditional storage and analysis systems in several new ways. In this paper we try to figure out how to overcome this challenges, why it's not possible to make it efficiently and describe three modern approaches to big data handling: NoSQL, MapReduce and real-time stream processing. The first section of the paper is the introduction. The second section discuss main issues of Big Data: volume, diversity, velocity, and value. The third section describes different approaches to solving the problem of Big Data. Traditionally one might use a relational DBMS. The paper propose some steps that allow to continue RDBMS using when it’s capacity becomes not enough. Another way is to use a NoSQL approach. The basic ideas of the NoSQL approach are: simplification, high throughput, and unlimited scaling out. Different kinds of NoSQL stores allow to use such systems in different applications of Big Data. MapReduce and it’s free implementation Hadoop may be used to provide scaling out Big Data analytics. Finally, several data management products support real time stream processing under Big Data. The paper briefly overviews these products. The final section of the paper is the conclusion.
In this paper, we present an approach to scalable co-scheduling in distributed computing for complex sets of interrelated tasks(jobs). The scalability means that schedules are formed for job models with various levels of task granularity, data replication policies, and processor resource and memory can be upgraded. The necessary of guaranteed job execution at the required quality of service causes taking into account the distributed environment dynamics, namely, changes in the number of jobs for servicing, volumes of computations, possible failures of processor nodes, etc. At a consequence, in the general case, a set of versions of scheduling, or a strategy, is required instead of a single version. We propose a callable model of scheduling based on multicriteria strategies. The choice of the specific schedule depends on the load level of the resource dynamics and is formed as a resource query which is sent to a local batch-job management system.
In this paper, we present results of a computational evaluation of goMapReduce parallel programming model approach for solving distributed data processing problems. In some applications, particularly data center problems, including text processing the programming models can aggregate significant number of parallel processes. We first discuss the implementation of these approaches using both Linux and Plan9 operating system and conduct a comparative scalability study of the both. From these results, we empirically show that, in practical implementation and evaluation of a goMapReduce model, the degree of OS's support for distributed processing encountered in solving the resulting word counting problem is crucial. We conclude that the goMapReduce approach under Plan9 may be useful in developing a heuristic approach for the data center problems.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
The geographic information system (GIS) is based on the first and only Russian Imperial Census of 1897 and the First All-Union Census of the Soviet Union of 1926. The GIS features vector data (shapefiles) of allprovinces of the two states. For the 1897 census, there is information about linguistic, religious, and social estate groups. The part based on the 1926 census features nationality. Both shapefiles include information on gender, rural and urban population. The GIS allows for producing any necessary maps for individual studies of the period which require the administrative boundaries and demographic information.
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.