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Some researchers have made a functional and experimental analysis of several distributed file systems including HDFS, Ceph, Gluster, Lustre and old (1.6.x) version of MooseFS, although this document is from 2013 and a lot of information are outdated (e.g. MooseFS had no HA for Metadata Server at that time).
Some examples include: MapR File System (MapR-FS), Ceph-FS, Fraunhofer File System (BeeGFS), Lustre File System, IBM General Parallel File System (GPFS), and Parallel Virtual File System. MapR-FS is a distributed file system that is the basis of the MapR Converged Platform, with capabilities for distributed file storage, a NoSQL database with ...
Transactional memory model [7] is the combination of cache coherency and memory consistency models as a communication model for shared memory systems supported by software or hardware; a transactional memory model provides both memory consistency and cache coherency. A transaction is a sequence of operations executed by a process that ...
Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers. [1] [2] The components of a distributed system communicate and coordinate their actions by passing messages to
A distributed hash table (DHT) is a distributed system that provides a lookup service similar to a hash table. Key–value pairs are stored in a DHT, and any participating node can efficiently retrieve the value associated with a given key. The main advantage of a DHT is that nodes can be added or removed with minimum work around re ...
The HPCC software architecture incorporates the Thor and Roxie clusters as well as common middleware components, an external communications layer, client interfaces which provide both end-user services and system management tools, and auxiliary components to support monitoring and to facilitate loading and storing of filesystem data from external sources.
The programming model utilized. Data-intensive computing systems utilize a machine-independent approach in which applications are expressed in terms of high-level operations on data, and the runtime system transparently controls the scheduling, execution, load balancing, communications, and movement of programs and data across the distributed ...
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the ...