Vol 43, No 3 (2017)

The development and investigation of efficient methods of parallel processing of very large databases using the columnar data representation designed for computer cluster is discussed. An approach that combines the advantages of relational and column-oriented DBMSs is proposed. A new type of distributed column indexes fragmented based on the domain-interval principle is introduced. The column indexes are auxiliary structures that are constantly stored in the distributed main memory of a computer cluster. To match the elements of a column index to the tuples of the original relation, surrogate keys are used. Resource hungry relational operations are performed on the corresponding column indexes rather than on the original relations of the database. As a result, a precomputation table is obtained. Using this table, the DBMS reconstructs the resulting relation. For basic relational operations on column indexes, methods for their parallel decomposition that do not require massive data exchanges between the processor nodes are proposed. This approach improves the class OLAP query performance by hundreds of times.

Nowadays, with the rapid evolution of information technologies, new teaching and learning methods and tools develop in the educational system. Virtual laboratories are one of those new tools that are now being increasingly used for teaching various disciplines in institutes and universities. In this paper, we outline the advantages of virtual laboratories over traditional ones and present a survey of existing solutions in the field of virtual laboratory development. Moreover, a virtual physical laboratory is described that was developed by authors of this paper taking into account both requirements imposed by the educational system of Kazakhstan and shortcomings of presently-available solutions.

This paper describes an algorithm for constructing a procedural sea bottom model, which can be used for testing and debugging machine vision systems of autonomous underwater vehicles (AUVs). The algorithm consists of three main stages: generating a low-frequency heightmap (used by the designer to define the basic form of a water area), constructing a three-dimensional model (based on the heightmap and fractal noise), and visualizing the three-dimensional model (refined by means of hardware or manual tessellation). The sea bottom model has the following features: it is detailed accurate to a screen pixel, each of its sections is absolutely unique, and its size is adequate for any tests.

In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking into account types of applications and heterogeneous workloads that could include CPU-intensive, diskintensive, I/O-intensive, memory-intensive, network-intensive, and other applications. When jobs of one type are allocated to the same resource, they may create a bottleneck and resource contention either in CPU, memory, disk or network. It may result in degradation of the system performance and increasing energy consumption. We focus on energy characteristics of applications, and show that an intelligent allocation strategy can further improve energy consumption compared with traditional approaches. We propose heterogeneous job consolidation algorithms and validate them by conducting a performance evaluation study using the Cloud Sim toolkit under different scenarios and real data. We analyze several scheduling algorithms depending on the type and amount of information they require.