Grzegorz Łukawski

Preserving Data Consistency in Scalable Distributed Two Layer Data Structures

The scalable NoSQL systems are often the best solutions to store huge amount of data. Despite that they in vast majority do not provide some features known from database systems (like transactions) they are suitable for many applications. However, in systems that require data consistency, e.g. payments or Internet booking, the lack of transactions is still noticeable. Recently the need of transactions in such data store systems can be observed more and more often. The Scalable Distributed Two Layer Data Structures (SD2DS) are very efficient implementation of NoSQL system. This paper exposes its basic inconsistency problems. We propose simple mechanisms which will be used to introduce consistency in SD2DS.

Fault tolerant record placement for decentralized SDDS LH

Scalable Distributed Data Structures (SDDS) is a file of records distributed to servers of a multicomputer. Faults of record placement in SDDS file may lead the file to crash. In this paper a fault model for decentralized SDDS LH* (DSDDS LH*) is given and, for such a model, a fault tolerant record placement architecture is presented. Client message forwarding is supplemented with client message backwarding. Split token (used in DSDDS LH* for controlling the file scalability) is supplemented with token tracing protocol and so called token level. These additional mechanisms allow for regeneration of DSDDS LH* file damaged by transient control faults.

Scalable Distributed Two-Layer Block Based Datastore

Modern distributed systems require fast and scalable datastores for efficient processing of huge amounts of information. Traditional DBMSs are insufficient for such a purpose. Hence, new datastore models were developed. In the paper we present the design and implementation of a scalable distributed two-layer block based datastore. We also discuss experimental results of comparing the performance of our datastore with MongoDB, a widely used NoSQL datastore.

SD2DS-Based Datastore for Large Files

We are introducing Scalable Distributed Two-Layer Datastore. The system that is an efficient solution while storing relatively big multimedia files. In the article we are focusing on storing high-resolution photos. We are introducing some of the key implementation concepts as well as the careful evaluation. We are comparing our solution with two of the most recognizable data storing systems: MongoDB and Memcached.

SDDSfL vs. local disk-a comparative study for Linux

Efficient data management and distribution in a multicomputer is a subject of much research. Distributed file systems are the most common solution of this problem, however, recent works are focused on more general data distribution protocols. Scalable, Distributed Data Structures (SDDS) are another promising approach to this issue. In this paper we discuss the efficiency of an implementation of SDDS in various applications. The results of experiments are presented.

Distributed memory virtualization with the use of SDDSfL

Scalable Distributed Data Structures (sdds) are a user---level software component that makes it possible to create a single coherent memory pool out of distributed rams of multicomputer nodes. In other words they are a tool for distributed memory virtualization. Applications that use sdds benefit from a fast data access and a scalability offered by such data structures. On the other hand, adapting an application to work with sdds may require significant changes in its source code. We have proposed an architecture of sdds called sddsfl that overcomes this difficulty by providing sdds functionality for applications in a form of an operating system service. In this paper we investigate usefulness of sddsfl for different types of applications.