scholarly journals Programming Abstractions for Managing Workflows on Tiered Storage Systems

2021 ◽  
Vol 17 (4) ◽  
pp. 1-21
Author(s):  
Devarshi Ghoshal ◽  
Lavanya Ramakrishnan
Keyword(s):  
Data Management ◽  
High Performance ◽  
New Technologies ◽  
Storage Capacity ◽  
Storage Systems ◽  
Storage System ◽  
Management Strategies ◽  
Scientific Workflows ◽  
Programming Abstractions ◽  
Storage Hierarchy

Scientific workflows in High Performance Computing ( HPC ) environments are processing large amounts of data. The storage hierarchy on HPC systems is getting deeper, driven by new technologies (NVRAMs, SSDs, etc.) There is a need for new programming abstractions that allow users to seamlessly manage data at the workflow level on multi-tiered storage systems, and provide optimal workflow performance and use of storage resources. In previous work, we introduced a software architecture Managing Data on Tiered Storage for Scientific Workflows (MaDaTS ) that used a Virtual Data Space ( VDS ) abstraction to hide the complexities of the underlying storage system while allowing users to control data management strategies. In this article, we detail the data-centric programming abstractions that allow users to manage a workflow around its data on the storage layer. The programming abstractions simplify data management for scientific workflows on multi-tiered storage systems, without affecting workflow performance or storage capacity. We measure the overheads and effectiveness introduced by the programming abstractions of MaDaTS. Our results show that these abstractions can optimally use the storage capacity in lesser capacity storage tiers, and simplify data management without adding any performance overheads.

scholarly journals It’s Time to Talk About HPC Storage: Perspectives on the Past and Future

2021 ◽  
Author(s):  
Bradley Settlemyer ◽  
George Amvrosiadis ◽  
Philip Carns ◽  
Robert Ross
Keyword(s):  
Artificial Intelligence ◽  
High Performance ◽  
New Technologies ◽  
Storage Systems ◽  
Storage System ◽  
Status Quo ◽  
Future Research ◽  
The Past ◽  
Experimental Data Analysis ◽  
Performance Computing

High-performance computing (HPC) storage systems are a key component of the success of HPC to date. Recently, we have seen major developments in storage-related technologies, as well as changes to how HPC platforms are used, especially in relation to artificial intelligence and experimental data analysis workloads. These developments merit a revisit of HPC storage system architectural designs. In this paper we discuss the drivers, identify key challenges to status quo posed by these developments, and discuss directions future research might take to unlock the potential of new technologies for the breadth of HPC applications.

scholarly journals Key Performance Indicators for an Energy Community Based on Sustainable Technologies

2021 ◽  
Vol 13 (16) ◽  
pp. 8789
Author(s):  
Giovanni Bianco ◽  
Barbara Bonvini ◽  
Stefano Bracco ◽  
Federico Delfino ◽  
Paola Laiolo ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Urban Areas ◽  
High Performance ◽  
Power Plants ◽  
New Technologies ◽  
Storage Systems ◽  
Clean Energy ◽  
Key Performance Indicators ◽  
Energy Performance ◽  
Green Energy

As reported in the “Clean energy for all Europeans package” set by the EU, a sustainable transition from fossil fuels towards cleaner energy is necessary to improve the quality of life of citizens and the livability in cities. The exploitation of renewable sources, the improvement of energy performance in buildings and the need for cutting-edge national energy and climate plans represent important and urgent topics to be faced in order to implement the sustainability concept in urban areas. In addition, the spread of polygeneration microgrids and the recent development of energy communities enable a massive installation of renewable power plants, high-performance small-size cogeneration units, and electrical storage systems; moreover, properly designed local energy production systems make it possible to optimize the exploitation of green energy sources and reduce both energy supply costs and emissions. In the present paper, a set of key performance indicators is introduced in order to evaluate and compare different energy communities both from a technical and environmental point of view. The proposed methodology was used in order to assess and compare two sites characterized by the presence of sustainable energy infrastructures: the Savona Campus of the University of Genoa in Italy, where a polygeneration microgrid has been in operation since 2014 and new technologies will be installed in the near future, and the SPEED2030 District, an urban area near the Campus where renewable energy power plants (solar and wind), cogeneration units fed by hydrogen and storage systems are planned to be installed.

A Cloud-Aware Distributed Object Storage System to Retrieve Large Data via HTML5-Enabled Web Browsers

2016 ◽  
pp. 25-45 ◽  
Author(s):  
Ahmet Artu Yıldırım ◽  
Dan Watson
Keyword(s):  
High Performance ◽  
Storage Systems ◽  
Storage System ◽  
Large Data ◽  
Web Browsers ◽  
Cloud Infrastructure ◽  
Scalable Storage ◽  
Distributed Object ◽  
Object Storage ◽  
Restful Web Service

Major Internet services are required to process a tremendous amount of data at real time. As we put these services under the magnifying glass, It's seen that distributed object storage systems play an important role at back-end in achieving this success. In this chapter, overall information of the current state-of –the-art storage systems are given which are used for reliable, high performance and scalable storage needs in data centers and cloud. Then, an experimental distributed object storage system (CADOS) is introduced for retrieving large data, such as hundreds of megabytes, efficiently through HTML5-enabled web browsers over big data – terabytes of data – in cloud infrastructure. The objective of the system is to minimize latency and propose a scalable storage system on the cloud using a thin RESTful web service and modern HTML5 capabilities.

Recent Trends in Cold Storage Systems

2020 ◽  
pp. 26-43
Author(s):  
Kapilan N.
Keyword(s):  
Phase Change ◽  
Cold Storage ◽  
Phase Change Materials ◽  
New Technologies ◽  
Storage Systems ◽  
Storage System ◽  
Operating Cost ◽  
Nano Particles ◽  
Commercial Building ◽  
Perishable Items

The cold storage is a big room or commercial building used to store perishable items like vegetables, fruits, fish, milk products, meat, etc. under controlled conditions where temperature, humidity, etc. are controlled for a longer duration. Different types of cold storages are available and selection of cold storage is based on the application. In recent years, solar hybrid cold storage systems are getting popular due to lower operating cost and reduced greenhouse gas emissions. Also, thermal energy storage systems with phase change materials are being used in cold storage systems. The new technologies are developed to reduce the energy consumption of cold storage systems. The quality of the perishable items stored in the cold storage may be retained by applying nano coating on the fruits. This chapter reviews cold storage systems, elements of cold storage, design of cold storage, application of phase change and nano particles in cold storage system.

scholarly journals Tin/vanadium redox electrolyte for battery-like energy storage capacity combined with supercapacitor-like power handling

2016 ◽  
Vol 9 (11) ◽  
pp. 3392-3398 ◽  
Author(s):  
Juhan Lee ◽  
Benjamin Krüner ◽  
Aura Tolosa ◽  
Sethuraman Sathyamoorthi ◽  
Daekyu Kim ◽  
...  
Keyword(s):  
Energy Storage ◽  
High Performance ◽  
Storage Capacity ◽  
Aqueous Electrolyte ◽  
Storage System ◽  
Energy Storage System ◽  
Power Handling ◽  
Redox Electrolyte ◽  
Energy Storage Capacity ◽  
Vanadium Redox

We introduce a high performance hybrid electrochemical energy storage system based on an aqueous electrolyte containing tin sulfate (SnSO4) and vanadyl sulfate (VOSO4) with nanoporous activated carbon.

scholarly journals Distributed Streaming Storage Performance Benchmarking: Kafka and Pravega

2019 ◽  
Vol 9 (2S) ◽  
pp. 1-8
Keyword(s):  
High Performance ◽  
Storage Systems ◽  
Storage System ◽  
Performance Benchmarking ◽  
Design And Implementation ◽  
Storage Performance ◽  
Performance Benchmark

The performance benchmarking tool for a distributed streaming storage system should be targeted toachieve maximum possible throughput from the streaming storage system by thrusting data massively. This paper details the design and implementation of high-performance benchmark tool for Kafka and Pravega streaming storage systems. The benchmark tool presented in this paper supports multiple writers and readers. The Pravega streaming storage is evaluated against Kafka with respect to performance.

Overview of Big-Data-Intensive Storage and Its Technologies

2018 ◽  
pp. 33-74
Author(s):  
Richard S. Segall ◽  
Jeffrey S. Cook
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Storage Systems ◽  
Storage System ◽  
Management Strategies ◽  
Sensor Data ◽  
Data Intensive Computing ◽  
Data Intensive ◽  
Future Challenges ◽  
Data Storage System

This chapter deals with a detailed discussion on the storage systems for data-intensive computing using Big Data. The chapter begins with a brief introduction about data-intensive computing and types of parallel processing approaches. It also highlights the points that display how data-intensive computing systems differ from other forms of computing. A discussion on the importance of Big Data computing is put forth. The current and future challenges of storage in genomics are discussed in detail. Also, storage and data management strategies are given. The chapter's focus is then on the software challenges for storage. Storage use cases are provided like DataDirect Networks, SDSC, etc. The list of storage tools and their details are provided. A small section discusses the sensor data storage system. Then a table is provided that shows the top 10 cloud storage systems for data-intensive computing using Big Data in the world. Top 500 Big Data storage servers statistics are also displayed effectively by the images from Top500 website.

scholarly journals Optimal Siting and Sizing of Wayside Energy Storage Systems in a D.C. Railway Line

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6271
Author(s):  
Regina Lamedica ◽  
Alessandro Ruvio ◽  
Laura Palagi ◽  
Nicola Mortelliti
Keyword(s):  
Energy Storage ◽  
High Performance ◽  
Net Present Value ◽  
Storage Systems ◽  
Storage System ◽  
Economic Benefits ◽  
Particle Swarm Algorithm ◽  
Railway Network ◽  
Economic Point ◽  
Railway Line

The paper proposes an optimal siting and sizing methodology to design an energy storage system (ESS) for railway lines. The scope is to maximize the economic benefits. The problem of the optimal siting and sizing of an ESS is addressed and solved by a software developed by the authors using the particle swarm algorithm, whose objective function is based on the net present value (NPV). The railway line, using a standard working day timetable, has been simulated in order to estimate the power flow between the trains finding the siting and sizing of electrical substations and storage systems suitable for the railway network. Numerical simulations have been performed to test the methodology by assuming a new-generation of high-performance trains on a 3 kV direct current (d.c.) railway line. The solution found represents the best choice from an economic point of view and which allows less energy to be taken from the primary network.

Performance Analysis of Structured, Un-Structured, and Cloud Storage Systems

2019 ◽  
Vol 10 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Anindita Sarkar Mondal ◽  
Madhupa Sanyal ◽  
Samiran Chattapadhyay ◽  
Kartick Chandra Mondal
Keyword(s):  
Big Data ◽  
Performance Analysis ◽  
Data Management ◽  
Cloud Storage ◽  
Storage Systems ◽  
Storage System ◽  
Storage Requirement ◽  
Comparative Performance ◽  
Research Challenge ◽  
Unstructured Databases

Big Data management is an interesting research challenge for all storage vendors. Since data can be structured or unstructured, hence variety of storage systems has been designed to meet storage requirement as per organization's demands. The article focuses on different kinds of storage systems, their architecture and implementations. The first portion of the article describes different examples of structured (PostgreSQL) and unstructured databases (MongoDB, OrientDB and Neo4j) along with data models and comparative performance analysis between them. The second portion of the paper focuses on cloud storage systems. As an example of cloud storage, Google Cloud Storage and mainly its implementation details have been discussed. The aim of the article is not to eulogize any particular storage system, but to clearly point out that every storage has a role to play in the industry. It depends on the enterprise to identify the requirements and deploy the storage systems.

scholarly journals Improved Efficiency Management Strategy for Battery-Based Energy Storage Systems

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1459
Author(s):  
Emilio Arnieri ◽  
Luigi Boccia ◽  
Francesco Amoroso ◽  
Giandomenico Amendola ◽  
Gregorio Cappuccino
Keyword(s):  
Energy Storage ◽  
Management Strategy ◽  
Storage Systems ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Management Strategies ◽  
Distribution Grid ◽  
Energy Storage Systems ◽  
Efficient Management ◽  
Efficiency Increase

Battery-based energy storage systems are forecasted to have a rapid diffusion in the next future, because they can support the diffusion of renewable energy sources and can offer interesting ancillary services for the distribution grid. Consequently, energy management strategies for batteries and inverters present in storage systems will play a fundamental role in order to guarantee effective energy transfer processes between storage systems and the grid. This paper proposes an efficient management strategy which allows maximizing the overall energy efficiency of grid-connected storage systems taking into account the actual relationship between the efficiency and the charging/discharging power of the storage system. The effectiveness of the strategy is as shown by analysis results, the proposed strategy can allow a remarkable efficiency increase compared with strategies which are not aimed at the efficiency optimization.

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