Related papers: Management of Data Replication for PC Cluster-base…
Container technologies such as Docker have become a crucial component of many software industry practices especially those pertaining to reproducibility and portability. The containerization philosophy has influenced the scientific…
The Globus Data Grid architecture provides a scalable infrastructure for the management of storage resources and data that are distributed across Grid environments. These services are designed to support a variety of scientific…
This paper addresses the clustering of data in the hyperdimensional computing (HDC) domain. In prior work, an HDC-based clustering framework, referred to as HDCluster, has been proposed. However, the performance of the existing HDCluster is…
Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Existing primary deduplication techniques either use inline…
Distributed storage systems (DSSs) provide a scalable solution for reliably storing massive amounts of data coming from various sources. Heterogeneity of these data sources often means different data classes (types) exist in a DSS, each…
A new class of Second generation high-performance computing applications with heterogeneous, dynamic and data-intensive properties have an extended set of requirements, which cover application deployment, resource allocation, -control, and…
Dynamic replication is a wide-spread multi-copy routing approach for efficiently coping with the intermittent connectivity in mobile opportunistic networks. According to it, a node forwards a message replica to an encountered node based on…
Network codes designed specifically for distributed storage systems have the potential to provide dramatically higher storage efficiency for the same availability. One main challenge in the design of such codes is the exact repair problem:…
Distributed dataflow systems like Apache Spark and Apache Hadoop enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources for dataflow jobs -- that neither lead to bottlenecks nor to…
We consider how underused computing resources within an enterprise may be harnessed to improve utilization and create an elastic computing infrastructure. Most current cloud provision involves a data center model, in which clusters of…
Data processing systems impose multiple views on data as it is processed by the system. These views include spreadsheets, databases, matrices, and graphs. There are a wide variety of technologies that can be used to store and process data…
Training and deploying deep learning models in real-world applications require processing large amounts of data. This is a challenging task when the amount of data grows to a hundred terabytes, or even, petabyte-scale. We introduce a hybrid…
Recent reproducibility case studies have raised concerns showing that much of the deposited research has not been reproducible. One of their conclusions was that the way data repositories store research data and code cannot fully facilitate…
Distributed systems store data objects redundantly to balance the data access load over multiple nodes. Load balancing performance depends mainly on 1) the level of storage redundancy and 2) the assignment of data objects to storage nodes.…
A key strategy to build disaster-resilient clouds is to employ backups of virtual machines in a geo-distributed infrastructure. Today, the continuous and acknowledged replication of virtual machines in different servers is a service…
A conventional data center that consists of monolithic-servers is confronted with limitations including lack of operational flexibility, low resource utilization, low maintainability, etc. Resource disaggregation is a promising solution to…
Any information is valuable as long as it has related data. If related data are not put together, the information is meaningless as unrelated data has no value. The mapped information is required only by authenticated users. So there is no…
High-performance computing (HPC) storage systems become increasingly critical to scientific applications given the data-driven discovery paradigm shift. As a storage solution for large-scale HPC systems, dozens of applications share the…
A common task in scientific computing is the derivation of data. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for…
The parallel algorithm for loading large sparse matrices from files into distributed memories of high performance computing (HPC) systems is presented. This algorithm was designed specially for matrices stored in files in the space-effcient…