To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cl...Editor IJLRES
The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
This document summarizes a dissertation on an improved load balancing technique for secure data in cloud computing. The dissertation discusses research issues in load balancing and data security in cloud computing. It proposes a load balancing methodology that uses a load balancer, Kerberos authentication, and Nginx load balancing algorithms like round robin and least connections to securely store and balance load of encrypted data across multiple cloud nodes. The methodology is implemented using tools like HP LoadRunner, Amazon Web Services, and Jelastic cloud platform. Performance is analyzed in terms of transaction time. The proposed technique aims to improve resource utilization, access control, data security, and efficiency in cloud environments.
Rapid Miner is an open-source data mining software tool. It provides functionality for data loading, preprocessing, transformation, data mining, modeling, evaluation, and deployment. Rapid Miner uses learning schemes and attribute evaluators from Weka and statistical modeling schemes from R. It can be used for tasks like text mining, feature engineering, and distributed data mining. Rapid Miner includes a graphical user interface to design analytical workflows using operators. It can also be called as an API or from the command line.
Charm a cost efficient multi cloud data hosting scheme with high availabilityKamal Spring
More and more enterprises and organizations are hosting their data into the cloud, in order to reduce the IT maintenance cost and enhance the data reliability. However, facing the numerous cloud vendors as well as their heterogenous pricing policies, customers may well be perplexed with which cloud(s) are suitable for storing their data and what hosting strategy is cheaper. The general status quo is that customers usually put their data into a single cloud (which is subject to the vendor lock-in risk) and then simply trust to luck. Based on comprehensive analysis of various state-of-the-art cloud vendors, this paper proposes a novel data hosting scheme (named CHARM) which integrates two key functions desired. The first is selecting several suitable clouds and an appropriate redundancy strategy to store data with minimized monetary cost and guaranteed availability. The second is triggering a transition process to re-distribute data according to the variations of data access pattern and pricing of clouds. We evaluate the performance of CHARM using both trace-driven simulations and prototype experiments. The results show that compared with the major existing schemes, CHARM not only saves around 20% of monetary cost but also exhibits sound adaptability to data and price adjustments.
secure data transfer and deletion from counting bloom filter in cloud computing.Venkat Projects
The document discusses a proposed system for secure data transfer and deletion from one cloud to another. It aims to achieve verifiable data transfer and reliable data deletion without a trusted third party. The system uses a counting Bloom filter scheme to allow a data owner, original cloud, and target cloud to verify that data was completely and accurately transferred or deleted. The scheme ensures data confidentiality, integrity, and public verifiability during the transfer and deletion processes.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Cryptographic Cloud Storage with Hadoop ImplementationIOSR Journals
This document proposes a scheme for cryptographic cloud storage using Hadoop implementation. It introduces parallel homomorphic encryption schemes that allow computation over encrypted data through an evaluation algorithm that can run efficiently in parallel. This allows a client to outsource function evaluation on private inputs to a Hadoop cluster while maintaining data confidentiality. The scheme uses erasure coding to distribute encrypted data across servers and generate verification tokens to check integrity and locate errors. It analyzes how Hadoop security can be enhanced using Kerberos authentication and capabilities to control data access. The proposed approach aims to efficiently ensure cloud data storage security, correctness, and availability.
Performance and Cost Evaluation of an Adaptive Encryption Architecture for Cl...Editor IJLRES
The cloud database as a service is a novel paradigm that can support several Internet-based applications, but its adoption requires the solution of information confidentiality problems. We propose a novel architecture for adaptive encryption of public cloud databases that offers an interesting alternative to the tradeoff between the required data confidentiality level and the flexibility of the cloud database structures at design time. We demonstrate the feasibility and performance of the proposed solution through a software prototype. Moreover, we propose an original cost model that is oriented to the evaluation of cloud database services in plain and encrypted instances and that takes into account the variability of cloud prices and tenant workloads during a medium-term period.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
This document summarizes a dissertation on an improved load balancing technique for secure data in cloud computing. The dissertation discusses research issues in load balancing and data security in cloud computing. It proposes a load balancing methodology that uses a load balancer, Kerberos authentication, and Nginx load balancing algorithms like round robin and least connections to securely store and balance load of encrypted data across multiple cloud nodes. The methodology is implemented using tools like HP LoadRunner, Amazon Web Services, and Jelastic cloud platform. Performance is analyzed in terms of transaction time. The proposed technique aims to improve resource utilization, access control, data security, and efficiency in cloud environments.
Rapid Miner is an open-source data mining software tool. It provides functionality for data loading, preprocessing, transformation, data mining, modeling, evaluation, and deployment. Rapid Miner uses learning schemes and attribute evaluators from Weka and statistical modeling schemes from R. It can be used for tasks like text mining, feature engineering, and distributed data mining. Rapid Miner includes a graphical user interface to design analytical workflows using operators. It can also be called as an API or from the command line.
Charm a cost efficient multi cloud data hosting scheme with high availabilityKamal Spring
More and more enterprises and organizations are hosting their data into the cloud, in order to reduce the IT maintenance cost and enhance the data reliability. However, facing the numerous cloud vendors as well as their heterogenous pricing policies, customers may well be perplexed with which cloud(s) are suitable for storing their data and what hosting strategy is cheaper. The general status quo is that customers usually put their data into a single cloud (which is subject to the vendor lock-in risk) and then simply trust to luck. Based on comprehensive analysis of various state-of-the-art cloud vendors, this paper proposes a novel data hosting scheme (named CHARM) which integrates two key functions desired. The first is selecting several suitable clouds and an appropriate redundancy strategy to store data with minimized monetary cost and guaranteed availability. The second is triggering a transition process to re-distribute data according to the variations of data access pattern and pricing of clouds. We evaluate the performance of CHARM using both trace-driven simulations and prototype experiments. The results show that compared with the major existing schemes, CHARM not only saves around 20% of monetary cost but also exhibits sound adaptability to data and price adjustments.
secure data transfer and deletion from counting bloom filter in cloud computing.Venkat Projects
The document discusses a proposed system for secure data transfer and deletion from one cloud to another. It aims to achieve verifiable data transfer and reliable data deletion without a trusted third party. The system uses a counting Bloom filter scheme to allow a data owner, original cloud, and target cloud to verify that data was completely and accurately transferred or deleted. The scheme ensures data confidentiality, integrity, and public verifiability during the transfer and deletion processes.
DYNAMIC TASK SCHEDULING BASED ON BURST TIME REQUIREMENT FOR CLOUD ENVIRONMENTIJCNCJournal
Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Cryptographic Cloud Storage with Hadoop ImplementationIOSR Journals
This document proposes a scheme for cryptographic cloud storage using Hadoop implementation. It introduces parallel homomorphic encryption schemes that allow computation over encrypted data through an evaluation algorithm that can run efficiently in parallel. This allows a client to outsource function evaluation on private inputs to a Hadoop cluster while maintaining data confidentiality. The scheme uses erasure coding to distribute encrypted data across servers and generate verification tokens to check integrity and locate errors. It analyzes how Hadoop security can be enhanced using Kerberos authentication and capabilities to control data access. The proposed approach aims to efficiently ensure cloud data storage security, correctness, and availability.
COST-MINIMIZING DYNAMIC MIGRATION OF CONTENT DISTRIBUTION SERVICES INTO HYBR...Nexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Neuro-Fuzzy System Based Dynamic Resource Allocation in Collaborative Cloud C...neirew J
This paper proposes a neuro-fuzzy system called Multi Attribute QoS scoring (MAQS) for dynamic resource allocation in collaborative cloud computing. MAQS uses a 3-layer neural network trained on 5 quality of service attributes - distance, reputation, task completion time, completion ratio, and load - to provide a QoS score for each resource. Resources are then allocated based on this score. The algorithm collects data periodically from nodes and calculates QoS scores for incoming tasks to select the highest scoring node for task allocation. The paper argues this approach considers multiple attributes and heterogeneity of resources better than previous single-attribute methods.
NEURO-FUZZY SYSTEM BASED DYNAMIC RESOURCE ALLOCATION IN COLLABORATIVE CLOUD C...ijccsa
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
A location based least-cost scheduling for data-intensive applicationsIAEME Publication
This document summarizes a research paper that proposes a location-based least-cost scheduling algorithm for transferring multiple data-intensive files simultaneously to multiple compute nodes in a grid environment. The proposed model includes an optimized meta-scheduler that receives multiple files, predicts the optimal number of parallel TCP streams to use for each file transfer based on sampling, and schedules the files to compute nodes using a greedy algorithm that considers location and cost. Experimental results showed the optimized model achieved better transfer times and throughput compared to non-optimized transfers.
Data-Intensive Technologies for CloudComputinghuda2018
This document provides an overview of data-intensive computing technologies for cloud computing. It discusses key concepts like data-parallelism and MapReduce architectures. It also summarizes several data-intensive computing systems including Google MapReduce, Hadoop, and LexisNexis HPCC. Hadoop is an open source implementation of MapReduce while HPCC provides distinct processing environments for batch and online query processing using its proprietary ECL programming language.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
The magnitude of data being stored and processed in the cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and computation offloading. Efficient management of limited computing and network resources is necessary to handle such an increase in cloud workload. Some of the critical issues in resource management for cloud computing are \emph{modeling resources / requirements} and \emph{allocating resources to users}. Potential benefits of tackling these issues include increases in utilization, scalability, Quality of Service (QoS) and throughput as well as decreases in latency and costs.
ORCHESTRATING BULK DATA TRANSFERS ACROSS GEO-DISTRIBUTED DATACENTERSNexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
The magnitude of data being stored and processed in the Cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and mobile code offloading. Concurrently, cloud services are getting more global and geographically distributed. To handle such changes in its usage scenario, the Cloud needs to transform into a completely decentralized, federated and ubiquitous environment similar to the historical transformation of the Internet. Indeed, research ideas for the transformation has already started to emerge including but not limited to Cloud Federations, Multi-Clouds, Fog Computing, Edge Computing, Cloudlets, Nano data centers, etc.
Standardization and resource management come up as the most significant issues for the realization of the distributed cloud paradigm. The focus in this thesis is the latter: efficient management of limited computing and network resources to adapt to the decentralization. Specifically, cloud services that consist of several virtual machines, dedicated network connections and databases are mapped to a multi-provider, geographically distributed and dynamic cloud infrastructure. The objective of the mapping is to improve quality of service in a cost-effective way. To that end; network latency and bandwidth as well as the cost of storage and computation are subjected to a multi-objective optimization.
The first phase of the resource mapping optimization is the topology mapping. In this phase, the virtual machines and network connections (i.e. the virtual cluster) of the cloud service are mapped to the physical cloud infrastructure. The hypothesis is that mapping the virtual cluster to a group of data centers with a similar topology would be the optimal solution.
Replication management is the second phase where the focus is on the data storage. Data objects that constitute the database are replicated and mapped to the storage as a service providers and end devices. The hypothesis for this phase is that an objective function adapted from the facility location problem optimizes the replica placement.
Detailed experiments under real-world as well as synthetic workloads prove that the hypotheses of the both phases are true.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Towards secure and dependable storage service in cloudsibidlegend
The document proposes a distributed storage integrity auditing mechanism for cloud data storage that allows for lightweight communication and computation during audits. The proposed design ensures strong correctness guarantees for stored data and enables fast error localization to identify misbehaving servers. It also supports secure and efficient dynamic operations like modifying, deleting, and appending blocks of outsourced data. Analysis shows the scheme is efficient and resilient against various attacks.
International Journal of Engineering Research and DevelopmentIJERD Editor
This document summarizes a research paper on developing an efficient dynamic resource scheduling model called CRAM for cloud computing. The proposed model uses Stochastic Reward Nets to model cloud resources and client requests in an analytical way. It captures key concepts like virtualization, federation between clouds, and defines performance metrics from the perspective of both cloud providers and users. The model is scalable and can represent systems with thousands of resources to analyze the impact of different resource management strategies.
Introduction to Big Data and Science Clouds (Chapter 1, SC 11 Tutorial)Robert Grossman
This document provides an introduction to data intensive computing. It discusses how advances in instruments are producing massive amounts of data, creating new paradigms of "data intensive science" and computing. It also discusses how utility clouds like Amazon and data clouds are addressing this challenge by providing on-demand access to vast computing resources and data storage at large scale. The document outlines different models for responsibility between cloud service providers and customers.
This document discusses several cloud computing projects from IEEE in 2014. It provides descriptions of 8 projects, including their titles, programming languages, links, and abstract summaries. The projects focus on topics like network coding-based cloud storage systems, privacy-preserving search over encrypted cloud data, cloud service composition, cloud resource procurement, and competition/cooperation among cloud providers.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Psdot 1 optimization of resource provisioning cost in cloud computingZTech Proje
The document discusses optimizing resource provisioning costs in cloud computing. It proposes an optimal cloud resource provisioning (OCRP) algorithm that formulates a stochastic programming model to minimize the total cost of reserving resources from cloud providers over multiple stages. The OCRP algorithm considers demand and price uncertainty and can be solved using different approaches like deterministic equivalent formulation or sample-average approximation. It allows cloud consumers to reduce resource provisioning costs compared to static pricing schemes.
NEW SECURE CONCURRECY MANEGMENT APPROACH FOR DISTRIBUTED AND CONCURRENT ACCES...ijiert bestjournal
Handover the critical data to the cloud provider sh ould have the guarantee of security and availabilit y for data at rest,in motion,and in use. Many alternatives sys tems exist for storage services,but the data confi dentiality in the database as a service paradigm are still immature. We propose a novel architecture that integrates clo ud database services paradigm with data confidentiality and exe cuting concurrent operations on encrypted data. Thi s is the method supporting geographically distributed client s to connect directly and access to an encrypted cl oud database,and to execute concurrent and independent operation s by using modifying the database structure. The proposed architecture has also the more advanta ge of removing intermediate proxies that limit the flexibility,availability,and expandability properties that are inbuilt in cloud-based systems. The efficacy of th e proposed architecture is evaluated by theoretical analyses a nd extensive experimental results with the help of prototype implementation related to the TPC-C standard benchm ark for various categories of clients and network l atencies. We propose a multi-keyword ranked search method for the encrypted cloud data databases,which simultan eously fulfill the needs of privacy requirements. The prop osed scheme could return not only the exact matchin g files,but also the files including the terms latent semantica lly associated to the query keyword.
ESTIMATING CLOUD COMPUTING ROUND-TRIP TIME (RTT) USING FUZZY LOGIC FOR INTERR...IJCI JOURNAL
Cloud computing is widely considered a transformative force in the computing world and is poised to
replace the traditional office setup as an industry standard. However, given the relative novelty of these
services and challenges such as the impact of physical distance on round-trip time (rtt), questions have
arisen regarding system performance and associated billing structures. The primary objective of this study
is to address these concerns. We aim to alleviate doubts by leveraging a fuzzy logic system to classify
distances between regions that support computing services and compare them with the conventional web
hosting format. To achieve this, we analyse the responses of one of these services, like amazon web
services, across different distance categories (near, medium, and far) between regions and strive to
conclude overall system performance. Our tests reveal that significant data is consistently lost during
customer transmission despite exhibiting superior round-trip times. We delve into this issue and present
our findings, which may illuminate the observed anomalous behaviour.
Hire some ii towards privacy-aware cross-cloud service composition for big da...ieeepondy
Hire some ii towards privacy-aware cross-cloud service composition for big data applications
+91-9994232214,8144199666, ieeeprojectchennai@gmail.com,
www.projectsieee.com, www.ieee-projects-chennai.com
IEEE PROJECTS 2015-2016
-----------------------------------
Contact:+91-9994232214,+91-8144199666
Email:ieeeprojectchennai@gmail.com
ieee projects chennai, ieee projects bangalore
COST-MINIMIZING DYNAMIC MIGRATION OF CONTENT DISTRIBUTION SERVICES INTO HYBR...Nexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Neuro-Fuzzy System Based Dynamic Resource Allocation in Collaborative Cloud C...neirew J
This paper proposes a neuro-fuzzy system called Multi Attribute QoS scoring (MAQS) for dynamic resource allocation in collaborative cloud computing. MAQS uses a 3-layer neural network trained on 5 quality of service attributes - distance, reputation, task completion time, completion ratio, and load - to provide a QoS score for each resource. Resources are then allocated based on this score. The algorithm collects data periodically from nodes and calculates QoS scores for incoming tasks to select the highest scoring node for task allocation. The paper argues this approach considers multiple attributes and heterogeneity of resources better than previous single-attribute methods.
NEURO-FUZZY SYSTEM BASED DYNAMIC RESOURCE ALLOCATION IN COLLABORATIVE CLOUD C...ijccsa
Cloud collaboration is an emerging technology which enables sharing of computer files using cloud
computing. Here the cloud resources are assembled and cloud services are provided using these resources.
Cloud collaboration technologies are allowing users to share documents. Resource allocation in the cloud
is challenging because resources offer different Quality of Service (QoS) and services running on these
resources are risky for user demands. We propose a solution for resource allocation based on multi
attribute QoS Scoring considering parameters such as distance to the resource from user site, reputation of
the resource, task completion time, task completion ratio, and load at the resource. The proposed algorithm
referred to as Multi Attribute QoS scoring (MAQS) uses Neuro Fuzzy system. We have also included a
speculative manager to handle fault tolerance. In this paper it is shown that the proposed algorithm
perform better than others including power trust reputation based algorithms and harmony method which
use single attribute to compute the reputation score of each resource allocated.
A location based least-cost scheduling for data-intensive applicationsIAEME Publication
This document summarizes a research paper that proposes a location-based least-cost scheduling algorithm for transferring multiple data-intensive files simultaneously to multiple compute nodes in a grid environment. The proposed model includes an optimized meta-scheduler that receives multiple files, predicts the optimal number of parallel TCP streams to use for each file transfer based on sampling, and schedules the files to compute nodes using a greedy algorithm that considers location and cost. Experimental results showed the optimized model achieved better transfer times and throughput compared to non-optimized transfers.
Data-Intensive Technologies for CloudComputinghuda2018
This document provides an overview of data-intensive computing technologies for cloud computing. It discusses key concepts like data-parallelism and MapReduce architectures. It also summarizes several data-intensive computing systems including Google MapReduce, Hadoop, and LexisNexis HPCC. Hadoop is an open source implementation of MapReduce while HPCC provides distinct processing environments for batch and online query processing using its proprietary ECL programming language.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...DataWorks Summit
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Modeling and Optimization of Resource Allocation in Cloud [PhD Thesis Progres...AtakanAral
The magnitude of data being stored and processed in the cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and computation offloading. Efficient management of limited computing and network resources is necessary to handle such an increase in cloud workload. Some of the critical issues in resource management for cloud computing are \emph{modeling resources / requirements} and \emph{allocating resources to users}. Potential benefits of tackling these issues include increases in utilization, scalability, Quality of Service (QoS) and throughput as well as decreases in latency and costs.
ORCHESTRATING BULK DATA TRANSFERS ACROSS GEO-DISTRIBUTED DATACENTERSNexgen Technology
bulk ieee projects in pondicherry,ieee projects in pondicherry,final year ieee projects in pondicherry
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
The magnitude of data being stored and processed in the Cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and mobile code offloading. Concurrently, cloud services are getting more global and geographically distributed. To handle such changes in its usage scenario, the Cloud needs to transform into a completely decentralized, federated and ubiquitous environment similar to the historical transformation of the Internet. Indeed, research ideas for the transformation has already started to emerge including but not limited to Cloud Federations, Multi-Clouds, Fog Computing, Edge Computing, Cloudlets, Nano data centers, etc.
Standardization and resource management come up as the most significant issues for the realization of the distributed cloud paradigm. The focus in this thesis is the latter: efficient management of limited computing and network resources to adapt to the decentralization. Specifically, cloud services that consist of several virtual machines, dedicated network connections and databases are mapped to a multi-provider, geographically distributed and dynamic cloud infrastructure. The objective of the mapping is to improve quality of service in a cost-effective way. To that end; network latency and bandwidth as well as the cost of storage and computation are subjected to a multi-objective optimization.
The first phase of the resource mapping optimization is the topology mapping. In this phase, the virtual machines and network connections (i.e. the virtual cluster) of the cloud service are mapped to the physical cloud infrastructure. The hypothesis is that mapping the virtual cluster to a group of data centers with a similar topology would be the optimal solution.
Replication management is the second phase where the focus is on the data storage. Data objects that constitute the database are replicated and mapped to the storage as a service providers and end devices. The hypothesis for this phase is that an objective function adapted from the facility location problem optimizes the replica placement.
Detailed experiments under real-world as well as synthetic workloads prove that the hypotheses of the both phases are true.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Towards secure and dependable storage service in cloudsibidlegend
The document proposes a distributed storage integrity auditing mechanism for cloud data storage that allows for lightweight communication and computation during audits. The proposed design ensures strong correctness guarantees for stored data and enables fast error localization to identify misbehaving servers. It also supports secure and efficient dynamic operations like modifying, deleting, and appending blocks of outsourced data. Analysis shows the scheme is efficient and resilient against various attacks.
International Journal of Engineering Research and DevelopmentIJERD Editor
This document summarizes a research paper on developing an efficient dynamic resource scheduling model called CRAM for cloud computing. The proposed model uses Stochastic Reward Nets to model cloud resources and client requests in an analytical way. It captures key concepts like virtualization, federation between clouds, and defines performance metrics from the perspective of both cloud providers and users. The model is scalable and can represent systems with thousands of resources to analyze the impact of different resource management strategies.
Introduction to Big Data and Science Clouds (Chapter 1, SC 11 Tutorial)Robert Grossman
This document provides an introduction to data intensive computing. It discusses how advances in instruments are producing massive amounts of data, creating new paradigms of "data intensive science" and computing. It also discusses how utility clouds like Amazon and data clouds are addressing this challenge by providing on-demand access to vast computing resources and data storage at large scale. The document outlines different models for responsibility between cloud service providers and customers.
This document discusses several cloud computing projects from IEEE in 2014. It provides descriptions of 8 projects, including their titles, programming languages, links, and abstract summaries. The projects focus on topics like network coding-based cloud storage systems, privacy-preserving search over encrypted cloud data, cloud service composition, cloud resource procurement, and competition/cooperation among cloud providers.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Psdot 1 optimization of resource provisioning cost in cloud computingZTech Proje
The document discusses optimizing resource provisioning costs in cloud computing. It proposes an optimal cloud resource provisioning (OCRP) algorithm that formulates a stochastic programming model to minimize the total cost of reserving resources from cloud providers over multiple stages. The OCRP algorithm considers demand and price uncertainty and can be solved using different approaches like deterministic equivalent formulation or sample-average approximation. It allows cloud consumers to reduce resource provisioning costs compared to static pricing schemes.
NEW SECURE CONCURRECY MANEGMENT APPROACH FOR DISTRIBUTED AND CONCURRENT ACCES...ijiert bestjournal
Handover the critical data to the cloud provider sh ould have the guarantee of security and availabilit y for data at rest,in motion,and in use. Many alternatives sys tems exist for storage services,but the data confi dentiality in the database as a service paradigm are still immature. We propose a novel architecture that integrates clo ud database services paradigm with data confidentiality and exe cuting concurrent operations on encrypted data. Thi s is the method supporting geographically distributed client s to connect directly and access to an encrypted cl oud database,and to execute concurrent and independent operation s by using modifying the database structure. The proposed architecture has also the more advanta ge of removing intermediate proxies that limit the flexibility,availability,and expandability properties that are inbuilt in cloud-based systems. The efficacy of th e proposed architecture is evaluated by theoretical analyses a nd extensive experimental results with the help of prototype implementation related to the TPC-C standard benchm ark for various categories of clients and network l atencies. We propose a multi-keyword ranked search method for the encrypted cloud data databases,which simultan eously fulfill the needs of privacy requirements. The prop osed scheme could return not only the exact matchin g files,but also the files including the terms latent semantica lly associated to the query keyword.
ESTIMATING CLOUD COMPUTING ROUND-TRIP TIME (RTT) USING FUZZY LOGIC FOR INTERR...IJCI JOURNAL
Cloud computing is widely considered a transformative force in the computing world and is poised to
replace the traditional office setup as an industry standard. However, given the relative novelty of these
services and challenges such as the impact of physical distance on round-trip time (rtt), questions have
arisen regarding system performance and associated billing structures. The primary objective of this study
is to address these concerns. We aim to alleviate doubts by leveraging a fuzzy logic system to classify
distances between regions that support computing services and compare them with the conventional web
hosting format. To achieve this, we analyse the responses of one of these services, like amazon web
services, across different distance categories (near, medium, and far) between regions and strive to
conclude overall system performance. Our tests reveal that significant data is consistently lost during
customer transmission despite exhibiting superior round-trip times. We delve into this issue and present
our findings, which may illuminate the observed anomalous behaviour.
Hire some ii towards privacy-aware cross-cloud service composition for big da...ieeepondy
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Cost-Minimizing Dynamic Migration of Content Distribution Services into Hybri...nexgentechnology
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1) The document discusses quality of service (QoS)-aware data replication for data-intensive applications in cloud computing systems. It aims to minimize data replication cost and number of QoS violated replicas.
2) It presents a mathematical model and algorithm to optimally place QoS-satisfied and QoS-violated data replicas. The algorithm uses minimum-cost maximum flow to obtain the optimal placement.
3) The algorithm takes as input a set of requested nodes and outputs the optimal placement for QoS-satisfied and QoS-violated replicas by modeling the problem as a network flow graph and applying existing polynomial-time algorithms.
Jayant Ghorpade - Cloud Computing White PaperJayant Ghorpade
This document summarizes a paper that proposes a mechanism for securely outsourcing linear programming computations to cloud computing services. It involves encrypting sensitive data before outsourcing using RSA encryption. A service selector service directs requests to encryption or decryption services. Encrypted data is divided and distributed across multiple cloud providers using a data distribution service and tag definitions. Decryption reconverts the encrypted results back to plaintext. The goal is to enable secure outsourcing of computations while protecting sensitive data and validating accurate results through encryption, distribution of encrypted data pieces, and decryption verification.
IRJET- Cloud Cost Analyzer and OptimizerIRJET Journal
This document proposes a system to monitor virtual machines (VMs or EC2 instances) on private clouds like Amazon or Google and provide solutions to reduce infrastructure costs from the customer's perspective. The system would monitor EC2 VM usage, performance metrics, and the customer's current cloud cost plan. It aims to optimize resource usage and save costs by proposing reductions to resources or cost plans. The system is designed to build a test bed using an Amazon account to connect to a user's resources and fetch performance data like RAM, CPU usage. It would then calculate pricing for storage, CPU usage, requests and other metrics to estimate overall setup costs and find opportunities for cost optimization.
dynamic resource allocation using virtual machines for cloud computing enviro...Kumar Goud
Abstract—Cloud computing allows business customers to scale up and down their resource usage based on needs., we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of “skewness” to measure the unevenness in the multidimensional resource utilization of a server. By minimizing imbalance, we will mix completely different of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.
Index Terms—Cloud computing, resource management, virtualization, green computing.
Psdot 15 performance analysis of cloud computingZTech Proje
The document discusses performance analysis of cloud computing centers using queuing systems. It aims to evaluate key performance indicators like response time distribution and mean number of tasks using a queuing model. The proposed system models cloud server farms as COCOMO II systems to obtain more accurate estimations of performance metrics while addressing issues with existing models like high traffic intensity and service time variation. It analyzes how changing server numbers and buffer sizes impacts the performance indicators.
Flaw less coding and authentication of user data using multiple cloudsIRJET Journal
This document discusses secure data storage in multiple cloud storage providers. It proposes a method for users to store encrypted data across multiple cloud storage providers using splitting and merging concepts. Private keys are generated during file access using a pseudo key generator and encrypted using 3DES for transmission. The method aims to increase data availability, confidentiality and reduce costs by distributing data across multiple cloud providers. It also discusses using image compression with reversible data hiding techniques to provide data confidentiality when storing images in the cloud.
JPJ1403 A Stochastic Model To Investigate Data Center Performance And QoS I...chennaijp
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This document discusses various cloud computing architectures including workload distribution, cloud bursting, elastic disk provisioning, resource pooling, dynamic failure detection and recovery, and capacity planning architectures. It also covers cloud mechanisms like automated scaling listeners, load balancers, pay-per-use monitors, audit monitors, service level agreements (SLAs), and fail-over systems that are important components of cloud architectures. The key cloud architectures aim to optimize resource utilization, enable horizontal and vertical scaling, provide high availability, and implement billing and monitoring functions.
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This document discusses a proposed system for improving social-based routing in delay tolerant networks. The proposed system takes into account both the frequency and duration of contacts to generate a higher quality social graph. It also studies community evolution to dynamically detect overlapping communities and bridge nodes in social networks. Simulation results show the proposed routing algorithm outperforms existing strategies significantly.
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To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
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1. The document proposes a privacy-preserving public auditing mechanism called Oruta for shared data stored in the cloud.
2. Oruta allows a third party auditor (TPA) to efficiently verify the integrity of shared data for a group of users while preserving their identity privacy.
3. It exploits ring signatures to generate verification information for shared data blocks while keeping the identity of the signer private from the TPA.
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This document discusses dynamic cloud pricing for revenue maximization. It first discusses how static pricing is currently dominant but dynamic pricing could improve revenue. It then outlines three contributions: 1) an empirical study finding Amazon spot prices are not set by market demand, motivating developing market-driven dynamic mechanisms, 2) formulating revenue maximization as a stochastic dynamic program to characterize optimal conditions, and 3) extending the model to consider non-homogeneous demand.
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The document proposes a cloud-based mobile multimedia recommendation system that can reduce network overhead and speed up the recommendation process. It analyzes limitations of existing systems, including difficulty reusing video tags, lack of scalability, and inability to identify spammers. The proposed system classifies users to recommend desired multimedia content with high precision and recall, while collecting user clusters instead of detailed profiles to avoid exploding network overhead. It utilizes computing resources in large data centers and detects video spammers through a machine learning approach.
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Embedded machine learning-based road conditions and driving behavior monitoring
IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Performance and cost evaluation of an adaptive encryption architecture for cloud databases
1. GLOBALSOFT TECHNOLOGIES
Performance and cost evaluation of an adaptive
encryption architecture for cloud databases
Abstract:
The cloud database as a service is a novel paradigm that can support several
Internet-based applications, but its adoption requires the solution of information
confidentiality problems. We propose a novel architecture for adaptive encryption
of public cloud databases that offers an interesting alternative to the trade-off
between the required data confidentiality level and the flexibility of the cloud
database structures at design time. We demonstrate the feasibility and performance
of the proposed solution through a software prototype. Moreover, we propose an
original cost model that is oriented to the evaluation of cloud database services in
plain and encrypted instances and that takes into account the variability of cloud
prices and tenant workload during a medium-term period.
Existing System:
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2. The cloud computing paradigm is successfully converging as the fifth utility , but
this positive trend is partially limited by concerns about information confidentiality
and unclear costs over a medium-long term .We are interested in the Database as a
Service paradigm (DBaaS) that poses several research challenges in terms of
security and cost evaluation from a tenant’s point of view. Most results concerning
encryption for cloud-based services are in applicable to the database paradigm.
Other encryption schemes, which allow the execution of SQL operations over
encrypted data, either suffer from performance limits or they require the choice of
which encryption scheme must be adopted for each database column and
SQL operations .
Proposed System:
The proposed architecture guarantees in an adaptive way the best level of data
confidentiality for any database workload, even when the set of SQL queries
dynamically changes. The adaptive encryption scheme, which was initially
proposed for applications not referring to the cloud, encrypts each plain column
into multiple encrypted columns, and each value is encapsulated into different
layers of encryption, so that the outer layers guarantee higher confidentiality but
support fewer computation capabilities with respect to the inner layers. we propose
the first analytical cost estimation model for evaluating cloud database costs in
plain and encrypted instances from a tenant’s point of view in a medium-term
period. It takes also into account the variability of cloud prices and the possibility
that the database workload may change during the evaluation period. This model is
instanced with respect to several cloud provider offers and related real prices. As
expected, adaptive encryption influences the costs related to storage size and
3. network usage of a database service. However, it is important that a tenant can
anticipate the final costs in its period of interest, and can choose the best
compromise between data confidentiality and expenses.
Architecture :
Implementation Modules:
1. Adaptive encryption
2. Metadata structure
3. Encrypted database management
4. Cost Estimation of cloud database services
5. Cost model
4. 6. Cloud pricing models
7. Usage Estimation
Adaptive encryption:
The proposed system supports adaptive encryption methods for public cloud
database service, where distributed and concurrent clients can issue direct SQL
operations. By avoiding an architecture based on one [or] multiple intermediate
servers between the clients and the cloud database, the proposed solution
guarantees the same level of scalability and availability of the cloud service. Figure
1 shows a scheme of the proposed architecture where each client executes an
encryption engine that manages encryption operations. This software module is
accessed by external user applications through the encrypted database interface.
The proposed architecture manages five types of information.
• plain data is the tenant information;
• encrypted data is stored in the cloud database;
• plain metadata represent the additional information that is necessary to execute
SQL operations on encrypted data;
• encrypted metadata is the encrypted version of the metadata that are stored in the
cloud database;
• master key is the encryption key of the encrypted metadata that is distributed to
legitimate clients.
Metadata structure:
5. Metadata include all information that allows a legitimate client knowing the master
key to execute SQL operations over an encrypted database. They are organized and
stored at a table-level granularity to reduce communication overhead for retrieval,
and to improve management of concurrent SQL operations. We define all metadata
information associated to a table as table metadata. Let us describe the structure of
a table metadata .Table metadata includes the correspondence between the plain
table name and the encrypted table name because each encrypted table name is
randomly generated. Moreover, for each column of the original plain table
it also includes a column metadata parameter containing the name and the data
type of the corresponding plain column (e.g., integer, string, timestamp). Each
column metadata is associated to one or more onion metadata, as many as the
number of onions related to the column.
Encrypted database management:
The database administrator generates a master key, and uses it to initialize the
architecture metadata. The master key is then distributed to legitimate clients. Each
table creation requires the insertion of a new row in the metadata table. For each
table creation, the administrator adds a column by specifying the column name,
data type and confidentiality parameters. These last are the most important for this
paper because they include the set of onions to be associated with the column, the
starting layer (denoting the actual layer at creation time) and the field
confidentiality of each onion. If the administrator does not specify the
confidentiality parameters of a column, then they are automatically chosen by the
client with respect to a tenant’s policy. Typically, the default policy assumes that
the starting layer of each onion is set to its strongest encryption algorithm.
6. Cost Estimation of cloud database services:
A tenant that is interested in estimating the cost of porting its database to a cloud
platform. This porting is a strategic decision that must evaluate confidentiality
issues and the related costs over a medium-long term. For these reasons, we
propose a model that includes the overhead of encryption schemes and variability
of database workload and cloud prices. The proposed model is general enough to
be applied to the most popular cloud database services, such as Amazon Relational
Database Service.
Cost model:
The cost of a cloud database service can be estimated as a function of three main
parameters:
Cost = f(T ime, Pricing,Usage) where:
• Time: identifies the time interval T for which the tenant requires the service.
• Pricing: refers to the prices of the cloud provider for subscription and resource
usage; they typically tend to diminish during T .
• Usage: denotes the total amount of resources used by the tenant; it typically
increases during T .In order to detail the pricing attribute, it is important
to specify that cloud providers adopt two subscription
policies: the on-demand policy allows a tenant to payper-use and to withdraw its
subscription anytime; the reservation policy requires the tenant to commit in
advance for a reservation period. Hence, we distinguish between billing costs
depending on resource usage and reservation costs denoting additional fees for
commitment in exchange for lower pay-per-use prices. Billing costs are billed
periodically to the tenant every billing period.
7. Cloud pricing models:
Popular cloud database providers adopt two different billing functions, that we call
linear L and tiered T . Let us consider a generic resource x, we define as xb its
usage at the b-th billing period and px b its price. If the billing function is tiered,
the cloud provider uses different prices for different ranges of resource usage. Let
us define Z as the number of tiers, and [ˆx1, . . . , ˆxZ−1] as the set of thresholds
that define all the tiers. The uptime and the storage billing functions of Amazon
RDS are linear, while the network usage is a tiered billing function. On the other
hand, the uptime billing functions of Azure SQL is linear, while the
storage and network billing functions are tiered.
Usage Estimation:
The uptime is easily measurable, it is more difficult to estimate accurately the
usage of storage and network , since they depend on the database structure, the
workload and the use of encryption. We now propose a methodology for the
estimation of storage and network usage due to encryption. For clarity, we define
sp, se, sa as the storage usage in the plaintext, encrypted, and adaptively encrypted
databases for one billing period. Similarly, np, ne, na represent network usage of
the three configurations. We assume that the tenant knows the database structure
and the query workload and we assume that each column a A stores ra values. By
denoting as vp a the average storage size of each plaintext value stored in column
a, we estimate the storage of the plaintext database.
8. System Configuration:
HARDWARE REQUIREMENTS:
Hardware - Pentium
Speed - 1.1 GHz
RAM - 1GB
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
SOFTWARE REQUIREMENTS:
Operating System : Windows
Technology : Java and J2EE
Web Technologies : Html, JavaScript, CSS
IDE : My Eclipse
Web Server : Tomcat
Tool kit : Android Phone
Database : My SQL
Java Version : J2SDK1.5