scholarly journals Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Daniele D’Agostino ◽  
Ivan Merelli ◽  
Marco Aldinucci ◽  
Daniele Cesini
Keyword(s):  
Energy Efficient ◽  
Huge Amount ◽  
Careful Evaluation ◽  
Energy Aware ◽  
Scientific Software ◽  
Computing Power ◽  
Energy Efficient Computing ◽  
Tools And Techniques ◽  
Decentralized Model ◽  
The Internet Of Things

Energy consumption is one of the major issues in today’s computer science, and an increasing number of scientific communities are interested in evaluating the tradeoff between time-to-solution and energy-to-solution. Despite, in the last two decades, computing which revolved around centralized computing infrastructures, such as supercomputing and data centers, the wide adoption of the Internet of Things (IoT) paradigm is currently inverting this trend due to the huge amount of data it generates, pushing computing power back to places where the data are generated—the so-called fog/edge computing. This shift towards a decentralized model requires an equivalent change in the software engineering paradigms, development environments, hardware tools, languages, and computation models for scientific programming because the local computational capabilities are typically limited and require a careful evaluation of power consumption. This paper aims to present how these concepts can be actually implemented in scientific software by presenting the state of the art of powerful, less power-hungry processors from one side and energy-aware tools and techniques from the other one.

scholarly journals An Improved Energy-Aware Clustering Method for The Regional Queries in The Internet of Things

2020 ◽  
Author(s):  
Arezoo Khatibi ◽  
Omid Khatibi
Keyword(s):  
Internet Of Things ◽  
Energy Efficient ◽  
Large Data ◽  
Base Station ◽  
The Internet ◽  
Heterogeneous Cluster ◽  
Energy Aware ◽  
Save Energy ◽  
Index Tree ◽  
The Internet Of Things

Abstract We will offer a method to improve energy efficient consumption for processing queries on the Internet of Things. We focused on an energy efficient hierarchical clustering index tree such that we can facilitate time-correlated region queries in the I.o.T (Internet of Things). We try to improve clustering and make a change on its proposed index tree. We try to do this by optimizing the query processing. We improve clustering to increase the accuracy of the Internet of Things and prevent the network from disconnecting. In the article that we have chosen, there is a heterogeneous cluster which means there exists a large data difference in the two ends of a cluster. Also, it often happens that the same information is sent to the base station by two overlapping clusters; therefore, we save energy by eliminating duplicated data.

scholarly journals Energy-Efficient IoT Service Brokering with Quality of Service Support

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 693
Author(s):  
Giacomo Tanganelli ◽  
Enzo Mingozzi
Keyword(s):  
Quality Of Service ◽  
Energy Efficient ◽  
Energy Aware ◽  
Allocation Policy ◽  
Service Support ◽  
Service Allocation ◽  
Iot Platforms ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) is becoming real, and recent studies highlight that the number of IoT devices will significantly grow in the next decade. Such massive IoT deployments are typically made available to applications as a service by means of IoT platforms, which are aware of the characteristics of the connected IoT devices–usually constrained in terms of computation, storage and energy capabilities–and dispatch application’s service requests to appropriate devices based on their capabilities. In this work, we develop an energy-aware allocation policy that aims at maximizing the lifetime of all the connected IoT devices, whilst guaranteeing that applications’ Quality of Service (QoS) requirements are met. To this aim, we formally define an IoT service allocation problem as a non-linear Generalized Assignment Problem (GAP). We then develop a time-efficient heuristic algorithm to solve the problem, which is shown to find near-optimal solutions by exploiting the availability of equivalent IoT services provided by multiple IoT devices, as expected especially in the case of massive IoT deployments.

Energy Aware Migration Model for Efficient Resource Management in Cloud

2014 ◽  
Vol 573 ◽  
pp. 537-542
Author(s):  
Iniya E. Nehru ◽  
Saswati Mukherjee ◽  
Jocelyn T. Noel
Keyword(s):  
Energy Efficient ◽  
Virtual Machines ◽  
Service Providers ◽  
Huge Amount ◽  
Energy Aware ◽  
Migration Model ◽  
Efficient Management ◽  
Internet Users ◽  
The World ◽  
Efficient Resource

Cloud computing is a platform that provides different services for the Internet users and companies on a pay-as-you-use basis. Services are provided through the datacenters which are available all over the world. One of the major problems faced by the service providers is the huge amount of energy consumed at the Datacenter. In todays world, where the environmental factors are the most talked about, energy efficient management of Datacenters has to be given due importance. In this paper a migration model is proposed for migration of job between virtual machines by considering the energy consumed and deadline as crucial factors.

A Review on Big Data Analytics in Business

2018 ◽  
pp. 210-214
Author(s):  
Manbir Sandhu ◽  
Purnima, Anuradha Saini
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Smart Devices ◽  
Data Streaming ◽  
Huge Amount ◽  
Business Units ◽  
Enormous Amount ◽  
Tools And Techniques ◽  
Heath Care ◽  
Data Analytic

Big data is a fast-growing technology that has the scope to mine huge amount of data to be used in various analytic applications. With large amount of data streaming in from a myriad of sources: social media, online transactions and ubiquity of smart devices, Big Data is practically garnering attention across all stakeholders from academics, banking, government, heath care, manufacturing and retail. Big Data refers to an enormous amount of data generated from disparate sources along with data analytic techniques to examine this voluminous data for predictive trends and patterns, to exploit new growth opportunities, to gain insight, to make informed decisions and optimize processes. Data-driven decision making is the essence of business establishments. The explosive growth of data is steering the business units to tap the potential of Big Data to achieve fueling growth and to achieve a cutting edge over their competitors. The overwhelming generation of data brings with it, its share of concerns. This paper discusses the concept of Big Data, its characteristics, the tools and techniques deployed by organizations to harness the power of Big Data and the daunting issues that hinder the adoption of Business Intelligence in Big Data strategies in organizations.

scholarly journals Energy Efficiency in Short and Wide-Area IoT Technologies—A Survey

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 22
Author(s):  
Eljona Zanaj ◽  
Giuseppe Caso ◽  
Luca De Nardis ◽  
Alireza Mohammadpour ◽  
Özgü Alay ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Internet Of Things ◽  
Low Power ◽  
Energy Efficient ◽  
Wide Area ◽  
Wide Area Networks ◽  
The Internet ◽  
Extensive Review ◽  
Research Directions ◽  
The Internet Of Things

In the last years, the Internet of Things (IoT) has emerged as a key application context in the design and evolution of technologies in the transition toward a 5G ecosystem. More and more IoT technologies have entered the market and represent important enablers in the deployment of networks of interconnected devices. As network and spatial device densities grow, energy efficiency and consumption are becoming an important aspect in analyzing the performance and suitability of different technologies. In this framework, this survey presents an extensive review of IoT technologies, including both Low-Power Short-Area Networks (LPSANs) and Low-Power Wide-Area Networks (LPWANs), from the perspective of energy efficiency and power consumption. Existing consumption models and energy efficiency mechanisms are categorized, analyzed and discussed, in order to highlight the main trends proposed in literature and standards toward achieving energy-efficient IoT networks. Current limitations and open challenges are also discussed, aiming at highlighting new possible research directions.

Energy-efficient computing for a group of clusters

2013 ◽  
Vol 39 (6) ◽  
pp. 295-300
Author(s):  
D. A. Grushin ◽  
N. N. Kuzyurin
Keyword(s):  
Energy Efficient ◽  
Energy Efficient Computing
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