Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure

2021 ◽  
Vol 7 (3) ◽  
pp. 1-38
Author(s):  
Sunanda Bose ◽  
Sumit Kumar Paul ◽  
Nandini Mukherjee
Keyword(s):  
Probabilistic Models ◽  
Cloud Infrastructure ◽  
Distributed Sensors ◽  
Efficient Resource ◽  
Geographically Distributed ◽  
Spatio Temporal ◽  
Sensor Cloud ◽  
Cloud Infrastructures ◽  
Cloud Technologies ◽  
Fixed Region

Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are hosted inside sensor-cloud infrastructures. These requests need to be satisfied using geographically distributed sensors. However, if the sensing resources are mobile, then sensing territory is not limited to a fixed region, rather spatially diverse. In this work, we present a generic scheme for integrating spatio-temporal information of mobile sensors for Internet of Things– (IoT) based environment monitoring system. A set of algorithms are proposed in this work to model spatial and temporal features of mobile resources and exploit resource mobility. We also propose probabilistic models to measure feasibility of a resource to sense a specific spatio-temporal phenomenon. We rank the resources based on their feasibility of satisfying the sensing requests and later use the information for efficient resource allocation and scheduling.

scholarly journals A survey on Approaches Used for Efficient Workload Management in Geo-Distributed Data Centres

2018 ◽  
Vol 7 (3.34) ◽  
pp. 141
Author(s):  
D Ramya ◽  
J Deepa ◽  
P N.Karthikayan
Keyword(s):  
Operating Systems ◽  
Data Center ◽  
Data Centers ◽  
Distributed Data ◽  
Cloud Infrastructure ◽  
Workload Management ◽  
Software Applications ◽  
Geographically Distributed ◽  
Cloud Infrastructures ◽  
Data Centres

A geographically distributed Data center assures Globalization of data and also security for the organizations. The principles for Disaster recovery is also taken into consideration. The above aspects drive business opportunities to companies that own many sites and Cloud Infrastructures with multiple owners.  The data centers store very critical and confidential documents that multiple organizations share in the cloud infrastructure. Previously different servers with different Operating systems and software applications were used. As it was difficult to maintain, Servers are consolidated which allows sharing of resources at low of cost maintenance [7]. The availability of documents should be increased and down time should be reduced. Thus workload management becomes a challenging among the data centers distributed geographically. In this paper we focus on different approaches used for workload management in Geo-distributed data centers. The algorithms used and also the challenges involved in different approaches are discussed 

Cloud technologies for development of geographically distributed computational and analytical Geological environment

2021 ◽  
pp. 86-98
Author(s):  
Виталий Сергеевич Ерёменко ◽  
Вера Викторовна Наумова ◽  
Алексей Андреевич Загуменнов ◽  
Станислав Владимирович Булов
Keyword(s):  
Distributed Processing ◽  
Cloud Services ◽  
Geological Data ◽  
Cloud Processing ◽  
Geographically Distributed ◽  
Analytical System ◽  
Analytical Services ◽  
Basic Features ◽  
General Architecture ◽  
Cloud Technologies

Описываются подходы к организации единого рабочего пространства исследователя для обработки геологической информации. Предложены подходы по взаимодействию с внешними территориально распределенными сервисами обработки и анализа геологических данных на основе WPS-платформы и по организации интегральной платформы для доступа к интерактивным облачным сервисам обработки геологических данных. Приводятся примеры облачных сервисов и платформ и сравниваются их основные характеристики. Описывается программная реализация предложенных подходов в рамках разрабатываемой информационноаналитической системы GeologyScience.ru. Приводится общая архитектура системы, и кратко описывается каждый из ее модулей. Описываются внешние сервисы, интегрированные в разрабатываемую платформу. The purpose of the study is to create an approach for organization of unified workspace for geological data research and processing. The study proposes approaches for organization of interaction with external geographically distributed processing and analytical services of geological data based on WPS platforms. Аn approach for integral platform organization providing access to interactive cloud processing of geological data is proposed. It connects the users with modern methods of data processing and analysis. A brief description of existing cloud services and platforms and comparison of their basic features is presented. Various groups of cloud services are described depending on the type of services provided to the user. Software implementation of approaches proposed within developing informational and analytical system GeologyScience.ru is addressed. The general architecture of the system and author’s modules developed including technological solutions used are described. A brief review of the services integrated into the platform being developed now is given. The use of the developed platform for processing and analyzing of geological data allows further expanding of capabilities of information-analytical environment developers to support scientific research.

scholarly journals Resource-Aware Network Topology Management Framework

2020 ◽  
Author(s):  
Aaqif Afzaal Abbasi ◽  
Shahab Shamshirband ◽  
Mohammed A. A. Al-qaness ◽  
Almas Abbasi ◽  
Nashat T. AL-Jallad ◽  
...  
Keyword(s):  
Network Reliability ◽  
Service Level ◽  
Cloud Infrastructure ◽  
Topology Management ◽  
Management Framework ◽  
Provider Network ◽  
Computing Services ◽  
Cloud Infrastructures ◽  
Resource Aware ◽  
Path Computation Element

Cloud infrastructure provides computing services where computing resources can be adjusted on-demand. However, the adoption of cloud infrastructures brings concerns like reliance on the service provider network, reliability, compliance for service level agreements (SLAs), etc. Software-defined networking (SDN) is a networking concept that suggests the segregation of a network’s data plane from the control plane. This concept improves networking behavior. In this paper, we present an SDN-enabled resource-aware topology framework. The proposed framework employs SLA compliance, Path Computation Element (PCE) and shares fair loading to achieve better topology features. We also present an evaluation, showcasing the potential of our framework.

scholarly journals Enhancing Fault Tolerance of Cloud Nodes using Replication Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 2040-2044
Keyword(s):  
Fault Tolerance ◽  
High Availability ◽  
The Other ◽  
Cloud Infrastructure ◽  
Virtual Node ◽  
Adaptive Scheme ◽  
Fine Grained ◽  
Cloud Technologies ◽  
Tolerance Method ◽  
Selection Of

The cloud technologies are gaining boom in the field of information technology. But on the same side cloud computing sometimes results in failures. These failures demand more reliable frameworks with high availability of computers acting as nodes. The request made by the user is replicated and sent to various VMs. If one of the VMs fail, the other can respond to increase the reliability. A lot of research has been done and being carried out to suggest various schemes for fault tolerance thus increasing the reliability. Earlier schemes focus on only one way of dealing with faults but the scheme proposed by the the author in this paper presents an adaptive scheme that deals with the issues related to fault tolerance in various cloud infrastructure. The projected scheme uses adaptive behavior during the selection of replication and fine-grained checkpointing methods for attaining a reliable cloud infrastructure that can handle different client requirements. In addition to it the algorithm also determines the best suited fault tolerance method for every designated virtual node. Zheng, Zhou,. Lyu and I. King (2012).

Cloud infrastructure as a growth driver of the modern digital economy

2021 ◽  
Vol 14 (4) ◽  
pp. 433-455
Author(s):  
Husan S. UMAROV
Keyword(s):  
Cloud Storage ◽  
Digital Economy ◽  
Cloud Infrastructure ◽  
High Quality ◽  
Cloud Technologies ◽  
Modern Russian

Subject. This article discusses the issues of development of cloud infrastructure in Russia and abroad, the advantages of cloud technologies, and forecasting their potential prospects. Objectives. The article aims to identify the features and potential problematic aspects of the development of cloud technologies in the modern Russian and foreign digital economies and predict the prospects for their advancement taking into account the post-pandemic development of world economies in 2020–2021. Methods. For the study, I used the methods of observation, generalization, and analysis. Results. The article describes the benefits of cloud infrastructure for the growth of global digital economies, as well as potential threats that impede the effectiveness of cloud technologies in Russia and abroad. Conclusions. The article concludes that it is necessary to further implement legislative initiatives in Russia that contribute to the high-quality storage of information and ensure its confidentiality in cloud storage.

Cloud Computing for Scientific Simulation and High Performance Computing

2013 ◽  
pp. 51-70
Author(s):  
Adrian Jackson ◽  
Michèle Weiland
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Parallel Programs ◽  
Small Scale ◽  
Cloud Infrastructure ◽  
Scientific Simulations ◽  
Cloud Infrastructures ◽  
Performance Computing

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.

An Outline of Threats and Sensor Cloud Infrastructure in Wireless Sensor Network

2015 ◽  
pp. 412-432
Author(s):  
Bhavana Butani ◽  
Piyush Kumar Shukla ◽  
Sanjay Silakari
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Cloud Infrastructure ◽  
Node Capture Attack ◽  
Node Capture ◽  
Sensor Cloud ◽  
A New Technique ◽  
Commercial Applications

Wireless sensor networks are utilized in vital situations like military and commercial applications, traffic surveillance, habitat monitoring, and many other applications. WSNs have to face various issues and challenges in terms of memory, communication, energy, computation, and storage, which require efficient management of huge amount of sensor data. Therefore, storage is an important issue in the WSN. Emergence of Sensor-Cloud infrastructure overcomes several shortcomings of WSN such as storage capacity and offers high processing capabilities for huge sensor data. Security is also the major challenge that is faced by the sensor network. This chapter includes a brief overview of the importance of cloud computing in sensor networks and the goal of DDoS and Node Capture Attack in WSN. This chapter includes descriptions of different modeling techniques of Node Capture attack and various detection and key pre-distribution schemes to invent a new technique to improve network resilience against node capture attacks.

A Study on Virtualized Sensor Cloud Infrastructure

2021 ◽  
pp. 1321-1329
Author(s):  
S. J. Akhila ◽  
N. J. Anasuya
Keyword(s):  
Cloud Infrastructure ◽  
Sensor Cloud
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