86,143 research outputs found
Microservices: Granularity vs. Performance
Microservice Architectures (MA) have the potential to increase the agility of
software development. In an era where businesses require software applications
to evolve to support software emerging requirements, particularly for Internet
of Things (IoT) applications, we examine the issue of microservice granularity
and explore its effect upon application latency. Two approaches to microservice
deployment are simulated; the first with microservices in a single container,
and the second with microservices partitioned across separate containers. We
observed a neglibible increase in service latency for the multiple container
deployment over a single container.Comment: 6 pages, conferenc
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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Learning from AI : new trends in database technology
Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database
Flowfield-dependent variant method for moving-boundary problems
A novel numerical scheme using the combination of flowfield-dependent variation method and arbitrary Lagrangian–Eulerian method is developed. This method is a mixed explicit–implicit numerical scheme, and its implicitness is dependent on the physical properties of the flowfield. The scheme is discretized using the finite-volume method to give flexibility in dealing with complicated geometries. The formulation itself yields a sparse matrix, which can be solved by using any iterative algorithm. Several benchmark problems in two-dimensional inviscid and viscous flow have been selected to validate the method. Good agreement with available experimental and numerical data in the literature has been obtained, thus showing its promising application in complex fluid–structure interaction problems
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