Cloud based Resource Scheduling Methodology for Data-Intensive Smart Cities and Industrial Applications

For the data-intensive applications, resource planning and scheduling has become an important part for smart cities. The cloud computing techniques are being used for planning and scheduling of resources in data-intensive applications. The regular methodologies being used are adequately successful for giving the asset allotment yet they do not provide time effectiveness during task execution. This article presents an effective and time prioritization based smart resource management platform employing the Cuckoo Search based Optimized Resource Allocation (CSO-RA) methodology. The opensource JStorm platform is utilized for dynamic asset planning while using big data analytics and the outcomes of the experimentation are observed using various assessment parameters. The proposed (CSO-RA) system is compared with the current methodologies like particle swarm optimization (PSO), ant colony optimization (ACO) and genetic algorithm (GA) based optimization methodologies and the viability of the proposed framework is established. The percentage of optimality observed for CSO-RA algorithm is 97\% and overall resource deployment rate of 28\% is achieved using CSO-RA method which is comparatively much better than PSO, GA and ACO conventional algorithms. Feasible outcomes are obtained by using the CSO-RA methodology for cloud computing based large scale optimization-based data intensive industrial applications.

Download Full-text

GATECloud.net: a platform for large-scale, open-source text processing on the cloud

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2012.0071 ◽

2013 ◽

Vol 371 (1983) ◽

pp. 20120071 ◽

Cited By ~ 23

Author(s):

Valentin Tablan ◽

Ian Roberts ◽

Hamish Cunningham ◽

Kalina Bontcheva

Keyword(s):

Cloud Computing ◽

Language Processing ◽

Large Scale ◽

Virtual Machines ◽

Cost Benefit Analysis ◽

Text Processing ◽

Cost Benefit ◽

Data Intensive ◽

On Demand ◽

Usage Evaluation

Cloud computing is increasingly being regarded as a key enabler of the ‘democratization of science’, because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research—GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost–benefit analysis and usage evaluation.

Download Full-text

PrEstoCloud

Information Resources Management Journal ◽

10.4018/irmj.2021010104 ◽

2021 ◽

Vol 34 (1) ◽

pp. 66-85

Author(s):

Yiannis Verginadis ◽

Dimitris Apostolou ◽

Salman Taherizadeh ◽

Ioannis Ledakis ◽

Gregoris Mentzas ◽

...

Keyword(s):

Cloud Computing ◽

Software Engineering ◽

Response Time ◽

Fog Computing ◽

Data Sources ◽

Data Intensive ◽

Service Response Time ◽

Enabling Services ◽

Data Intensive Applications ◽

Multi Cloud

Fog computing extends multi-cloud computing by enabling services or application functions to be hosted close to their data sources. To take advantage of the capabilities of fog computing, serverless and the function-as-a-service (FaaS) software engineering paradigms allow for the flexible deployment of applications on multi-cloud, fog, and edge resources. This article reviews prominent fog computing frameworks and discusses some of the challenges and requirements of FaaS-enabled applications. Moreover, it proposes a novel framework able to dynamically manage multi-cloud, fog, and edge resources and to deploy data-intensive applications developed using the FaaS paradigm. The proposed framework leverages the FaaS paradigm in a way that improves the average service response time of data-intensive applications by a factor of three regardless of the underlying multi-cloud, fog, and edge resource infrastructure.

Download Full-text

NoSQL Databases

Advances in Data Mining and Database Management - Handbook of Research on Cloud Infrastructures for Big Data Analytics ◽

10.4018/978-1-4666-5864-6.ch008 ◽

2014 ◽

pp. 186-215 ◽

Cited By ~ 2

Author(s):

Ganesh Chandra Deka

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Processing ◽

Open Source ◽

Data Storage ◽

Big Data Processing ◽

Nosql Databases ◽

Data Intensive ◽

Huge Data ◽

Data Intensive Applications

NoSQL databases are designed to meet the huge data storage requirements of cloud computing and big data processing. NoSQL databases have lots of advanced features in addition to the conventional RDBMS features. Hence, the “NoSQL” databases are popularly known as “Not only SQL” databases. A variety of NoSQL databases having different features to deal with exponentially growing data-intensive applications are available with open source and proprietary option. This chapter discusses some of the popular NoSQL databases and their features on the light of CAP theorem.

Download Full-text

Time and Cost Efficient Cloud Resource Allocation for Real-Time Data-Intensive Smart Systems

Energies ◽

10.3390/en13215706 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5706

Author(s):

Muhammad Shuaib Qureshi ◽

Muhammad Bilal Qureshi ◽

Muhammad Fayaz ◽

Muhammad Zakarya ◽

Sheraz Aslam ◽

...

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Real Time ◽

Large Scale ◽

Smart Devices ◽

Budget Constraints ◽

Smart Systems ◽

Data Intensive ◽

Cost Efficient ◽

And Storage

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

Download Full-text

Performance mining of large-scale data-intensive applications

Proceedings 16th International Parallel and Distributed Processing Symposium ◽

10.1109/ipdps.2002.1016582 ◽

2002 ◽

Author(s):

C. Carothers ◽

B.K. Szymanski ◽

M. Zaki

Keyword(s):

Large Scale ◽

Data Intensive ◽

Large Scale Data ◽

Data Intensive Applications ◽

Scale Data

Download Full-text

A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d1050.1284s519 ◽

2020 ◽

Vol 8 (4S5) ◽

pp. 227-235

Keyword(s):

Information Technology ◽

Cloud Computing ◽

Radio Frequency Identification ◽

Cloud Storage ◽

Large Scale ◽

Rapid Development ◽

Storage System ◽

Cuckoo Search ◽

Data Volume ◽

High Degree

The rapid development in information technology has rendered an increase in the data volume at a speed which is surprising. In recent times, cloud computing and the Internet of Things (IoT) have become the hottest among the topics in the industry of information technology. There are many advantages to Cloud computing such as scalability, low price, and large scale and the primary technique of the IoTs like the Radio-Frequency Identification (RFID) have been applied to a large scale. In the recent times, the users of cloud storage have been increasing to a great extent and the reason behind this was the cloud storage system bringing down the issues in maintenance and also has a low amount of storage when compared to other methods. This system provides a high degree of reliability and availability where redundancy is introduced to the systems. In the replicated systems, objects get to be copied many times and every copy resides in a different location found in distributed computing. So, replication of data has been posing some threat to the cloud storage for users and also for the providers since it has been a major challenge providing efficient storage of data. So, the work has been analysing different strategies of replication of data and have pointed out several issues that are affected by this. For the purpose of this work, replication of data has been presented by employing the Cuckoo Search (CS) and the Greedy Search. The research is proceeding in a direction to reduce the replications without any adverse effect on the reliability and the availability of data.

Download Full-text

NoSQL Databases

Handbook of Research on Securing Cloud-Based Databases with Biometric Applications - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-4666-6559-0.ch006 ◽

2015 ◽

pp. 109-152 ◽

Cited By ~ 2

Author(s):

Mainak Adhikari ◽

Sukhendu Kar

Keyword(s):

Cloud Computing ◽

Social Networking ◽

Data Storage ◽

Social Networking Sites ◽

Nosql Databases ◽

Data Intensive ◽

Huge Data ◽

Nosql Database ◽

Real Time Applications ◽

Data Intensive Applications

NoSQL database provides a mechanism for storage and access of data across multiple storage clusters. NoSQL dabases are finding significant and growing industry to meet the huge data storage requirements of Big data, real time applications, and Cloud Computing. NoSQL databases have lots of advantages over the conventional RDBMS features. NoSQL systems are also referred to as “Not only SQL” to emphasize that they may in fact allow Structured language like SQL, and additionally, they allow Semi Structured as well as Unstructured language. A variety of NoSQL databases having different features to deal with exponentially growing data intensive applications are available with open source and proprietary option mostly prompted and used by social networking sites. This chapter discusses some features and challenges of NoSQL databases and some of the popular NoSQL databases with their features on the light of CAP theorem.

Download Full-text