The document discusses two approaches to managing quality of service (QoS) in distributed real-time database management systems (DRTDBMS) using different data replication policies. The first approach uses a semi-total data replication policy that replicates both real-time and non-real-time data between nodes based on access thresholds. The second approach uses a partial data replication policy that replicates the most accessed data objects between the most accessed nodes based on transaction access histories. Both approaches are applied within a distributed feedback control scheduling architecture and their results are compared to an existing approach using full data replication.
A quality of service management in distributed feedback control scheduling ar...csandit
In our days, there are many real-time applications that use data which are geographically
dispersed. The distributed real-time database management systems (DRTDBMS) have been used
to manage large amount of distributed data while meeting the stringent temporal requirements
in real-time applications. Providing Quality of Service (QoS) guarantees in DRTDBMSs is a
challenging task. To address this problem, different research works are often based on
distributed feedback control real-time scheduling architecture (DFCSA). Data replication is an
efficient method to help DRTDBMS meeting the stringent temporal requirements of real-time
applications. In literature, many works have designed algorithms that provide QoS guarantees
in distributed real-time databases using only full temporal data replication policy.
In this paper, we have applied two data replication policies in a distributed feedback control
scheduling architecture to manage a QoS performance for DRTDBMS. The first proposed data
replication policy is called semi-total replication, and the second is called partial replication
policy.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
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.
This document discusses synchronization and replication in occasionally connected mobile database systems. It begins by describing the architecture of such systems, where mobile clients maintain local copies of shared data and synchronize with a central server when reconnected. It then discusses using a data-centric approach where data is grouped and clients subscribe to relevant groups. The document proposes a grouping estimation algorithm to determine optimal data groupings. Finally, it describes how primary and secondary copies are used for replication among clients and servers, and the need for synchronization when clients operate offline.
This document discusses scheduling algorithms for batches of MapReduce jobs in heterogeneous cloud environments with budget and deadline constraints. It proposes two optimization problems: 1) Given a fixed budget B, how to efficiently schedule tasks to minimize workflow completion time without exceeding the budget. 2) Given a fixed deadline D, how to efficiently schedule tasks to minimize monetary cost without missing the deadline. It presents an optimal dynamic programming algorithm for the first problem that runs in O(κB2) time, and two faster greedy algorithms. It also briefly discusses reducing the second problem to a knapsack problem. The goal is to help cloud service providers deploy MapReduce cost-effectively given user constraints.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
IRJET- Improving Data Availability by using VPC Strategy in Cloud Environ...IRJET Journal
This document discusses improving data availability in cloud environments using virtual private cloud (VPC) strategies and data replication strategies (DRS). It proposes using VPC to define private networks in public clouds and deploying cloud resources into those private networks for improved security and control. It also proposes using DRS to store multiple copies of data across different nodes to increase data availability, reduce bandwidth usage, and provide fault tolerance. The proposed approach identifies popular data files for replication, selects the best storage sites based on factors like request frequency, failure probability, and storage usage, and decides when to replace replicas to optimize resource usage. A simulation showed this hybrid VPC and DRS approach improved performance metrics like response time, network usage, and load balancing compared to
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
A quality of service management in distributed feedback control scheduling ar...csandit
In our days, there are many real-time applications that use data which are geographically
dispersed. The distributed real-time database management systems (DRTDBMS) have been used
to manage large amount of distributed data while meeting the stringent temporal requirements
in real-time applications. Providing Quality of Service (QoS) guarantees in DRTDBMSs is a
challenging task. To address this problem, different research works are often based on
distributed feedback control real-time scheduling architecture (DFCSA). Data replication is an
efficient method to help DRTDBMS meeting the stringent temporal requirements of real-time
applications. In literature, many works have designed algorithms that provide QoS guarantees
in distributed real-time databases using only full temporal data replication policy.
In this paper, we have applied two data replication policies in a distributed feedback control
scheduling architecture to manage a QoS performance for DRTDBMS. The first proposed data
replication policy is called semi-total replication, and the second is called partial replication
policy.
Data Distribution Handling on Cloud for Deployment of Big Dataijccsa
Cloud computing is a new emerging model in the field of computer science. For varying workload Cloud computing presents a large scale on demand infrastructure. The primary usage of clouds in practice is to process massive amounts of data. Processing large datasets has become crucial in research and business environments. The big challenges associated with processing large datasets is the vast infrastructure required. Cloud computing provides vast infrastructure to store and process Big data. Vms can be provisioned on demand in cloud to process the data by forming cluster of Vms . Map Reduce paradigm can be used to process data wherein the mapper assign part of task to particular Vms in cluster and reducer combines individual output from each Vms to produce final result. we have proposed an algorithm to reduce the overall data distribution and processing time. We tested our solution in Cloud Analyst Simulation environment wherein, we found that our proposed algorithm significantly reduces the overall data processing time in cloud.
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.
This document discusses synchronization and replication in occasionally connected mobile database systems. It begins by describing the architecture of such systems, where mobile clients maintain local copies of shared data and synchronize with a central server when reconnected. It then discusses using a data-centric approach where data is grouped and clients subscribe to relevant groups. The document proposes a grouping estimation algorithm to determine optimal data groupings. Finally, it describes how primary and secondary copies are used for replication among clients and servers, and the need for synchronization when clients operate offline.
This document discusses scheduling algorithms for batches of MapReduce jobs in heterogeneous cloud environments with budget and deadline constraints. It proposes two optimization problems: 1) Given a fixed budget B, how to efficiently schedule tasks to minimize workflow completion time without exceeding the budget. 2) Given a fixed deadline D, how to efficiently schedule tasks to minimize monetary cost without missing the deadline. It presents an optimal dynamic programming algorithm for the first problem that runs in O(κB2) time, and two faster greedy algorithms. It also briefly discusses reducing the second problem to a knapsack problem. The goal is to help cloud service providers deploy MapReduce cost-effectively given user constraints.
Differentiating Algorithms of Cloud Task Scheduling Based on various Parametersiosrjce
Cloud computing is a new design structure for large, distributed data centers. Cloud computing
system promises to offer end user “pay as go” model. To meet the expected quality requirements of users, cloud
computing need to offer differentiated services to users. QoS differentiation is very important to satisfy
different users with different QoS requirements. In this paper, various QoS based scheduling algorithms,
scheduling parameters and the future scope of discussed algorithms have been studied. This paper summarizes
various cloud scheduling algorithms, findings of algorithms, scheduling factors, type of scheduling and
parameters considered
IRJET- Improving Data Availability by using VPC Strategy in Cloud Environ...IRJET Journal
This document discusses improving data availability in cloud environments using virtual private cloud (VPC) strategies and data replication strategies (DRS). It proposes using VPC to define private networks in public clouds and deploying cloud resources into those private networks for improved security and control. It also proposes using DRS to store multiple copies of data across different nodes to increase data availability, reduce bandwidth usage, and provide fault tolerance. The proposed approach identifies popular data files for replication, selects the best storage sites based on factors like request frequency, failure probability, and storage usage, and decides when to replace replicas to optimize resource usage. A simulation showed this hybrid VPC and DRS approach improved performance metrics like response time, network usage, and load balancing compared to
THRESHOLD BASED VM PLACEMENT TECHNIQUE FOR LOAD BALANCED RESOURCE PROVISIONIN...IJCNCJournal
The unbalancing load issue is a multi-variation, multi-imperative issue that corrupts the execution and productivity of processing assets. Workload adjusting methods give solutions of load unbalancing circumstances for two bothersome aspects over-burdening and under-stacking. Cloud computing utilizes planning and workload balancing for a virtualized environment, resource partaking in cloud foundation. These two factors must be handled in an improved way in cloud computing to accomplish ideal resource sharing. Henceforth, there requires productive resource, asset reservation for guaranteeing load advancement in the cloud. This work aims to present an incorporated resource, asset reservation, and workload adjusting calculation for effective cloud provisioning. The strategy develops a Priority-based Resource Scheduling Model to acquire the resource, asset reservation with threshold-based load balancing for improving the proficiency in cloud framework. Extending utilization of Virtual Machines through the suitable and sensible outstanding task at hand modifying is then practiced by intensely picking a job from submitting jobs using Priority-based Resource Scheduling Model to acquire resource asset reservation. Experimental evaluations represent, the proposed scheme gives better results by reducing execution time, with minimum resource cost and improved resource utilization in dynamic resource provisioning conditions.
Role of Operational System Design in Data Warehouse Implementation: Identifyi...iosrjce
Data warehouse designing process takes input from operational system of the organization. Quality
of data warehousing solution depends on design of operational system. Often, operational system
implementations of organizations have some limitations. Thus, we cannot proceed for data warehouse
designing so easily. In this paper, we have tried to investigate operational system of the organization for
identifying such limitations and determine role of operational system design in the process of data warehouse
design and implementation. We have worked out to find possible methods to handle such limitations and have
proposed techniques to get a quality data warehousing solution under such limitations. To make the work based
on live example, National Rural Health Mission (NRHM) Project has been taken. It is a national project of
health sector, managed by Indian Government across the country. The complex structure and high volume of
data makes it an ideal case for data warehouse implementation.
RESOURCE ALLOCATION METHOD FOR CLOUD COMPUTING ENVIRONMENTS WITH DIFFERENT SE...IJCNCJournal
In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
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.
Overlapped clustering approach for maximizing the service reliability ofIAEME Publication
This document discusses an overlapped clustering approach for maximizing the reliability of heterogeneous distributed computing systems. It proposes assigning tasks to nodes based on their requirements in order to reduce network bandwidth usage and enable local communication. It calculates the reliability of each node and assigns more resource-intensive tasks to more reliable nodes. When nodes fail, it uses load balancing techniques like redistributing tasks from overloaded or failed nodes to idle nodes in the same cluster. The goal is to improve system reliability through approaches like minimizing network communication, assigning tasks based on node reliability, and handling failures through load balancing at the cluster level.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
In this paper we explore the issue of store determination in a portable shared specially appointed system. In our vision reserve determination ought to fulfill the accompanying prerequisites: (i) it ought to bring about low message overhead and (ii) the data ought to be recovered with least postponement. In this paper, we demonstrate that these objectives can be accomplished by part the one bounce neighbors into two sets in view of the transmission run. The proposed approach lessens the quantity of messages overflowed into the system to discover the asked for information. This plan is completely circulated and comes requiring little to no effort as far as store overhead. The test comes about gives a promising outcome in view of the measurements of studies.
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
PROVABLE MULTICOPY DYNAMIC DATA POSSESSION IN CLOUD COMPUTING SYSTEMSNexgen 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
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
T AXONOMY OF O PTIMIZATION A PPROACHES OF R ESOURCE B ROKERS IN D ATA G RIDSijcsit
A novel taxonomy of replica selection techniques is proposed. We studied some data grid approaches
where the selection strategies of data management is different. The aim of the study is to determine the
common concepts and observe their performance and to compare their performance with our strategy
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.
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.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An asynchronous replication model to improve data available into a heterogene...Alexander Decker
This document summarizes a research paper that proposes an asynchronous replication model to improve data availability in heterogeneous systems. The proposed model uses a loosely coupled architecture between main and replication servers to reduce dependencies. It also supports heterogeneous systems, allowing different parts of an application to run on different systems for better performance. This makes it a cost-effective solution for data replication across different system types.
This document discusses QoS aware replica control strategies for distributed real-time database management systems. It proposes a heuristic approach called Greedy-Cover Firefly algorithm that dynamically places replicas based on QoS requirements and replaces replicas using an adaptive algorithm. The algorithm calculates replication costs and selects optimal nodes for replica placement based on access history. It aims to improve system performance by reducing resources consumed over time while meeting QoS requirements. Simulation results show the proposed algorithms greatly improve the system performance.
A DDS-Based Scalable and Reconfigurable Framework for Cyber-Physical Systemsijseajournal
Cyber-Physical Systems (CPSs) involve the interconnection of heterogeneous computing devices which are
closely integrated with the physical processes under control. Often, these systems are resource-constrained
and require specific features such as the ability to adapt in a timeliness and efficient fashion to dynamic
environments. Also, they must support fault tolerance and avoid single points of failure. This paper
describes a scalable framework for CPSs based on the OMG DDS standard. The proposed solution allows
reconfiguring this kind of systems at run-time and managing efficiently their resources.
Data Distribution Handling on Cloud for Deployment of Big Dataneirew J
This document summarizes a research paper that proposes an algorithm to reduce data distribution and processing time in cloud computing for big data deployment. The paper discusses different data distribution techniques for virtual machines (VMs) in cloud computing, such as centralized, semi-centralized, hierarchical, and peer-to-peer approaches. It also reviews related work on MapReduce frameworks and load balancing algorithms. The authors implemented their proposed peer-to-peer distribution technique and Round Robin and Throttled load balancing algorithms in CloudSim. Experimental results showed the Throttled algorithm achieved significantly lower average response times than Round Robin.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Role of Operational System Design in Data Warehouse Implementation: Identifyi...iosrjce
Data warehouse designing process takes input from operational system of the organization. Quality
of data warehousing solution depends on design of operational system. Often, operational system
implementations of organizations have some limitations. Thus, we cannot proceed for data warehouse
designing so easily. In this paper, we have tried to investigate operational system of the organization for
identifying such limitations and determine role of operational system design in the process of data warehouse
design and implementation. We have worked out to find possible methods to handle such limitations and have
proposed techniques to get a quality data warehousing solution under such limitations. To make the work based
on live example, National Rural Health Mission (NRHM) Project has been taken. It is a national project of
health sector, managed by Indian Government across the country. The complex structure and high volume of
data makes it an ideal case for data warehouse implementation.
RESOURCE ALLOCATION METHOD FOR CLOUD COMPUTING ENVIRONMENTS WITH DIFFERENT SE...IJCNCJournal
In a cloud computing environment with multiple data centers over a wide area, it is highly likely that each data center would provide the different service quality to users at different locations. It is also required to consider the nodes at the edge of the network (local cloud) which support applications such as IoTs that require low latency and location awareness. The authors proposed the joint multiple resource allocation method in a cloud computing environment that consists of multiple data centers and each data center provides the different network delay. However, the existing method does not take account of cases where requests that require a short network delay occur more than expected. Moreover, the existing method does not take account of service processing time in data centers and therefore cannot provide the optimal resource allocation when it is necessary to take the total processing time (both network delay and service processing time in a data center) into consideration in resource allocation.
Data Warehouses store integrated and consistent data in a subject-oriented data repository dedicated
especially to support business intelligence processes. However, keeping these repositories updated usually
involves complex and time-consuming processes, commonly denominated as Extract-Transform-Load tasks.
These data intensive tasks normally execute in a limited time window and their computational requirements
tend to grow in time as more data is dealt with. Therefore, we believe that a grid environment could suit
rather well as support for the backbone of the technical infrastructure with the clear financial advantage of
using already acquired desktop computers normally present in the organization. This article proposes a
different approach to deal with the distribution of ETL processes in a grid environment, taking into account
not only the processing performance of its nodes but also the existing bandwidth to estimate the grid
availability in a near future and therefore optimize workflow distribution.
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.
Overlapped clustering approach for maximizing the service reliability ofIAEME Publication
This document discusses an overlapped clustering approach for maximizing the reliability of heterogeneous distributed computing systems. It proposes assigning tasks to nodes based on their requirements in order to reduce network bandwidth usage and enable local communication. It calculates the reliability of each node and assigns more resource-intensive tasks to more reliable nodes. When nodes fail, it uses load balancing techniques like redistributing tasks from overloaded or failed nodes to idle nodes in the same cluster. The goal is to improve system reliability through approaches like minimizing network communication, assigning tasks based on node reliability, and handling failures through load balancing at the cluster level.
Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environmentrahulmonikasharma
This document summarizes a research paper that proposes a new algorithm called KD-Tree approach for efficient virtual machine (VM) allocation in cloud computing environments. The algorithm aims to minimize the response time for allocating VMs to user requests. It does this by adopting a KD-Tree data structure to index physical host machines, allowing the scheduler to quickly find the host that can accommodate a new VM request with the minimum latency in O(Log n) time. The proposed approach is evaluated through simulations using the CloudSim toolkit and is shown to outperform an existing linear scheduling strategy (LSTR) algorithm in terms of reducing VM allocation times.
In this paper we explore the issue of store determination in a portable shared specially appointed system. In our vision reserve determination ought to fulfill the accompanying prerequisites: (i) it ought to bring about low message overhead and (ii) the data ought to be recovered with least postponement. In this paper, we demonstrate that these objectives can be accomplished by part the one bounce neighbors into two sets in view of the transmission run. The proposed approach lessens the quantity of messages overflowed into the system to discover the asked for information. This plan is completely circulated and comes requiring little to no effort as far as store overhead. The test comes about gives a promising outcome in view of the measurements of studies.
This document evaluates the performance of a hybrid differential evolution-genetic algorithm (DE-GA) approach for load balancing in cloud computing. It first provides background on cloud computing and load balancing. It then describes the DE-GA approach, which uses differential evolution initially and switches to genetic algorithm if needed. The results show that the hybrid DE-GA approach improves performance over differential evolution and genetic algorithm alone, reducing makespan, average response time, and improving resource utilization. The study demonstrates the benefits of the hybrid evolutionary algorithm for an important problem in cloud computing.
Scheduling Algorithm Based Simulator for Resource Allocation Task in Cloud Co...IRJET Journal
This document proposes a scheduling algorithm for allocating resources in cloud computing based on the Project Evaluation and Review Technique (PERT). It aims to address issues like starvation of lower priority tasks. The algorithm models task allocation as a directed acyclic graph and uses PERT to schedule critical and non-critical tasks, prioritizing higher priority tasks. The algorithm is evaluated against other scheduling methods and shows improvements in reducing completion time and optimizing resource allocation for all tasks.
PROVABLE MULTICOPY DYNAMIC DATA POSSESSION IN CLOUD COMPUTING SYSTEMSNexgen 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
Challenges in Dynamic Resource Allocation and Task Scheduling in Heterogeneou...rahulmonikasharma
This document discusses the challenges of dynamic resource allocation and task scheduling in heterogeneous cloud environments. It outlines that resource allocation involves deciding how to allocate resources to tasks to maximize utilization, while task scheduling assigns tasks to processors to minimize execution time. The major challenges are optimizing allocated resources to minimize costs while meeting customer demands and application requirements. Allocating resources dynamically in heterogeneous cloud environments is difficult due to issues like resource contention, scarcity, and fragmentation. The document also discusses approaches to resource modeling, allocation, offering, discovery and monitoring that algorithms must address to effectively allocate resources on demand.
T AXONOMY OF O PTIMIZATION A PPROACHES OF R ESOURCE B ROKERS IN D ATA G RIDSijcsit
A novel taxonomy of replica selection techniques is proposed. We studied some data grid approaches
where the selection strategies of data management is different. The aim of the study is to determine the
common concepts and observe their performance and to compare their performance with our strategy
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.
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.
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
An asynchronous replication model to improve data available into a heterogene...Alexander Decker
This document summarizes a research paper that proposes an asynchronous replication model to improve data availability in heterogeneous systems. The proposed model uses a loosely coupled architecture between main and replication servers to reduce dependencies. It also supports heterogeneous systems, allowing different parts of an application to run on different systems for better performance. This makes it a cost-effective solution for data replication across different system types.
This document discusses QoS aware replica control strategies for distributed real-time database management systems. It proposes a heuristic approach called Greedy-Cover Firefly algorithm that dynamically places replicas based on QoS requirements and replaces replicas using an adaptive algorithm. The algorithm calculates replication costs and selects optimal nodes for replica placement based on access history. It aims to improve system performance by reducing resources consumed over time while meeting QoS requirements. Simulation results show the proposed algorithms greatly improve the system performance.
A DDS-Based Scalable and Reconfigurable Framework for Cyber-Physical Systemsijseajournal
Cyber-Physical Systems (CPSs) involve the interconnection of heterogeneous computing devices which are
closely integrated with the physical processes under control. Often, these systems are resource-constrained
and require specific features such as the ability to adapt in a timeliness and efficient fashion to dynamic
environments. Also, they must support fault tolerance and avoid single points of failure. This paper
describes a scalable framework for CPSs based on the OMG DDS standard. The proposed solution allows
reconfiguring this kind of systems at run-time and managing efficiently their resources.
Data Distribution Handling on Cloud for Deployment of Big Dataneirew J
This document summarizes a research paper that proposes an algorithm to reduce data distribution and processing time in cloud computing for big data deployment. The paper discusses different data distribution techniques for virtual machines (VMs) in cloud computing, such as centralized, semi-centralized, hierarchical, and peer-to-peer approaches. It also reviews related work on MapReduce frameworks and load balancing algorithms. The authors implemented their proposed peer-to-peer distribution technique and Round Robin and Throttled load balancing algorithms in CloudSim. Experimental results showed the Throttled algorithm achieved significantly lower average response times than Round Robin.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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.
A load balancing strategy for reducing data loss risk on cloud using remodif...IJECEIAES
This document summarizes a research paper that proposes a load balancing strategy called the re-modified throttled algorithm (RMTA) to reduce the risk of data loss on cloud computing. The RMTA aims to address limitations in previous algorithms by considering both the availability and capacity of virtual machines (VMs) during load distribution and migration processes. It maintains two index tables to track available and unavailable VMs. When a new request arrives, the RMTA load balancer selects a VM that has sufficient available storage and bandwidth to handle the request size without risk of data overflow. This is intended to minimize data loss or hampering during migration. The performance of the RMTA is evaluated through simulation and analysis on the CloudAnalyst tool.
A Reconfigurable Component-Based Problem Solving EnvironmentSheila Sinclair
This technical report describes a reconfigurable component-based problem solving environment called DISCWorld. The key features discussed are:
1) DISCWorld uses a data flow model represented as directed acyclic graphs (DAGs) of operators to integrate distributed computing components across networks.
2) It supports both long running simulations and parameter search applications by allowing complex processing requests to be composed graphically or through scripting and executed on heterogeneous platforms.
3) Operators can be simple "pure Java" implementations or wrappers to fast platform-specific implementations, and some operators may represent sub-graphs that can be reconfigured to run across multiple servers for faster execution.
This document proposes a novel distributed architecture for a NoSQL datastore that supports strong consistency while maintaining high scalability. It is based on the Scalable Distributed Two-Layer Data Store (SD2DS) model, which has proven efficient. The architecture considers concurrent and unfinished operations to ensure consistency. Algorithms for scheduling operations are presented and proven theoretically correct. An implementation is evaluated experimentally against MongoDB and MemCached, showing high performance compared to existing NoSQL systems. The architecture aims to augment SD2DS with consistency mechanisms without impacting scalability.
An efficient resource sharing technique for multi-tenant databases IJECEIAES
Multi-tenancy is a key component of Software as a Service (SaaS) paradigm. Multi-tenant software has gained a lot of attention in academics, research and business arena. They provide scalability and economic benefits for both cloud service providers and tenants by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model referred as MultiTenant Dynamic Resource Scheduling Model (MTDRSM) embracing a query classification and worker sorting technique enabling efficient and dynamic resource sharing among tenants. The experiments show significant performance improvement over existing model.
Automatic Management of Wireless Sensor Networks through Cloud Computingyousef emami
This document presents a framework for integrating wireless sensor networks (WSNs) with cloud computing. The framework uses policy-based network management to automate WSN management tasks. It proposes adding a policy analyzer to a publish/subscribe broker to match sensor data with stored policies and mandate appropriate actions. The framework includes software as a service, virtualization management, a publish/subscribe broker, management services like SLA and change management, fault tolerance, and security measures. The goal is to facilitate and automate WSN management through the use of cloud computing and policy-based network rules.
This document summarizes a paper that presents a novel method for passive resource discovery in cluster grid environments. The method monitors network packet frequency from nodes' network interface cards to identify nodes with available CPU cycles (<70% utilization) by detecting latency signatures from frequent context switching. Experiments on a 50-node testbed showed the method can consistently and accurately discover available resources by analyzing existing network traffic, including traffic passed through a switch. The paper also proposes algorithms for distributed two-level resource discovery, replication and utilization to optimize resource allocation and access costs in distributed computing environments.
IRJET- A Survey on Remote Data Possession Verification Protocol in Cloud StorageIRJET Journal
This document summarizes a survey on remote data possession verification protocols for cloud storage. It begins with an abstract describing the problem of verifying integrity of outsourced data files on remote cloud servers. It then provides background on remote data possession verification (RDPV) protocols and discusses related work on ensuring data integrity and supporting dynamic operations. The document describes the system framework, RDPV protocol, use of homomorphic hash functions, and an optimized implementation using an operation record table to efficiently support dynamic operations like modifications. It concludes that the presented efficient and secure RDPV protocol is suitable for cloud storage applications.
A latency-aware max-min algorithm for resource allocation in cloud IJECEIAES
Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.
BI-TEMPORAL IMPLEMENTATION IN RELATIONAL DATABASE MANAGEMENT SYSTEMS: MS SQ...lyn kurian
Traditional database management systems (DBMS) are the computation
storage and reservoir of large amounts of information. The data accumulated by these
database systems is the information valid at present time, valid now. It is the data that
is true at the present moment. Past data is the information that was kept in the
database at an earlier time, data that is hold to be existed in the past, were valid at
some point before now. Future data is the information supposed to be valid at a future
time instance, data that will be true in the near future, valid at some point after now.
The commercial DBMS of today used by organizations and individuals, such as MS
SQL Server, Oracle, DB2, Sybase, Postgres etc., do not provide models to support and
process (retrieving, modifying, inserting and removing) past and future data.
The implementation of bi-temporal modelling in Microsoft SQL Server is important
to know how relational database management system handles data the bi-temporal
property. In bi-temporal database, data saved is never deleted and additional values
are always appended. Therefore, the paper explores one of the way we can build bitemporal handling of data. The paper aims to build the core concepts of bi-temporal
data storage and querying techniques used in bi-temporal relational DBMS i.e., from
data structures to normalized storage, and to extraction or slicing of data.
The unlimited growth of data results relational data to become complicated in terms
of management and storage of data. Thus, the developers working in various
commercial and industrial applications should know how bi-temporal concepts apply to relational databases, especially due to their increased flexibility in the bi-temporal
storage as well as in analyzing data. Thereby, the paper demonstrates how bi-temporal
data structures and their operations are applied in Relational Database Management
System
Cloud Computing: A Perspective on Next Basic Utility in IT World IRJET Journal
This document discusses cloud computing and its architecture. It begins with an introduction to cloud computing, defining it as a model that provides infrastructure, platforms, and software as services. The key characteristics and service models of cloud computing are described.
The document then discusses the architecture of cloud computing, including the layers of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It also describes the deployment models of private cloud, public cloud, community cloud, and hybrid cloud.
The document outlines several challenges of cloud computing, such as resource allocation and scheduling, cost optimization, processing time and speed, memory management, load balancing, security issues, fault
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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
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Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
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Many real-time systems are naturally distributed and these distributed systems require not only highavailability
but also timely execution of transactions. Consequently, eventual consistency, a weaker type of
strong consistency is an attractive choice for a consistency level. Unfortunately, standard eventual
consistency, does not contain any real-time considerations. In this paper we have extended eventual
consistency with real-time constraints and this we call real-time eventual consistency. Followed by this new
definition we have proposed a method that follows this new definition. We present a new algorithm using
revision diagrams and fork-join data in a real-time distributed environment and we show that the proposed
method solves the problem.
Similar to QUALITY OF SERVICE MANAGEMENT IN DISTRIBUTED FEEDBACK CONTROL SCHEDULING ARCHITECTURE USING DIFFERENT REPLICATION POLICIES (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
The document proposes a new validation method for fuzzy association rules based on three steps: (1) applying the EFAR-PN algorithm to extract a generic base of non-redundant fuzzy association rules using fuzzy formal concept analysis, (2) categorizing the extracted rules into groups, and (3) evaluating the relevance of the rules using structural equation modeling, specifically partial least squares. The method aims to address issues with existing fuzzy association rule extraction algorithms such as large numbers of extracted rules, redundancy, and difficulties with manual validation.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
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2. 76 Computer Science & Information Technology (CS & IT)
deadlines. To address this problem, more studies focus on feedback control techniques have been
proposed [1,5] to provide better QoS guarantees in replicated environment.
Data replication consists on replicating a data on participating nodes. Given that this technique
increases the data availability, it can help DRTDBMS to meet the stringent temporal
requirements. In literature, two data replication policies are presented: the full data replication
policy and the partial data replication policy [8].
Wei et al [8] have proposed a QoS management algorithm in distributed real-time databases with
full temporal data replication. They proposed a replication model in which only real-time data are
replicated and the updating replicas data is propagate at the same time to all replicas in each other
nodes. In replicated environment, user operations on data replicas are often read operations [8]. In
this case, to guarantee the replicas data freshness, many efficient replica management algorithms
have been added in literature. All proposed algorithms aim to meet deadlines of transactions and
data freshness guarantees.
This proposed solution in [8] is shown to be appropriate for a fixed number of participating
nodes, e.g., 8 nodes. However, in the presence of a high number of nodes (more than 8 nodes),
using a full data replication policy is inefficient in these systems, and it may have many
limitations. Those issues are related to communication costs between nodes, highly message loss
and periodically collected data performance.
To address this problem, we proposed in this work a new data replication policy called a semi-
total data replication policy and, we have applied it in feedback control scheduling architecture in
replicated environment. Furthermore, we have applied the partial replication policy in this
architecture, and we have presented a comparison between obtained results with these data
replication policies and the existing results with full data replication policy proposed in previous
work. Compared to previous work [8], we increment the number of nodes. Also, in our replication
model, we proposed to replicate both types of data; the classical data and the temporal data. In
this model, temporal replicas data are updating transparently, and the classical replicas data are
updating according to the RT-RCP policy presented in [3].
The main objective of our work is to limit the miss ratio of the arrived transactions to the system.
At the same time, our work aims to tolerate a certain imprecise value of replicas data and to
control timely transactions, which must guarantee access only to fresh replicas data, even in the
presence of unpredictable workloads.
In this article, we begin by presenting a distributed real-time database model. In the section 3, we
present the related works on which, we base our approach. In section 4, we present the proposed
QoS management approach based on DFCS architecture to provide QoS guarantees in
DRTDBMS. Section 5 shows the details of the simulation and the evaluation results. We
conclude this article by discussing this work and by focusing on its major perspectives.
2. RELATED WORK
In this section, we present the QoS management architecture proposed in [3], on which we based
our work. We describe, also, an overview of the data replication policies presented in literature,
which is used for the QoS enhancement.
3. Computer Science & Information Technology (CS & IT) 77
2.1. Architecture for QoS management in DRTDBMS
The QoS can be considered as a metric which measures the overall system performance. In fact,
QoS is a collective measure of the service level provided to the customer. It can be evaluated by
different performance criteria that include basic availability, error rate, response time, the rate of
successful transactions before their deadline, etc.
The DRTDBMS as RTDBMS have to maintain both the logical consistency of the database and
its temporal consistency. Since it seems to be difficult to reach these two goals simultaneously
because of the lack of predictability of such systems, some researchers have designed new
techniques to manage real-time transactions. These techniques use feedback control scheduling
theory in order to provide an acceptable DRTDBMS behaviour. They also attempt to provide a
fixed QoS by considering the data and the transactions behaviour.
In RTDBMS, many works are presented to provide a QoS guarantees. Those works consist of the
applicability of most of the management techniques of the real-time transactions and/or real-time
data [1,2,9,11].
A significant contribution on QoS guarantees for real-time data services in mid-size scale of
DRTDBMS is presented by Wei et al. [8] (cf. Figure 1). The authors have designed an
architecture, which we base our work, that provides QoS guarantees in DRTDBMS with full
replication of only temporal data with small number of nodes (8 nodes). This architecture, called
Distributed FCSA (DFCSA), consists of heuristic Feedback-based local controllers and global
load balancers (GLB) working at each site.
The general outline of the DFCSA is shown in Figure 1. In what follows, we give a brief
description of its basic components.
The admission controller is used to regulate the system workload in order to prevent its
overloading. Its functioning is based on the estimated CPU utilization and the target utilization set
point. For each admitted transaction, its estimated execution time is added to the estimated CPU
utilization. Therefore, transactions will be discarded from the system if the estimated CPU
utilization is higher than the target utilization set by the local controller.
The transaction manager handles the execution of transactions. It is composed of a concurrency
controller (CC), a freshness manager (FM), a data manager (DM) and a replica manager (RM).
Figure 1 FCS architecture for QoS guarantee in DRTDBMS [8].
4. 78 Computer Science & Information Technology (CS & IT)
2.2. Distributed real-time database model
In this section, we present the distributed real-time database model on which we base our work.
This model is issued from many works about QoS management in DRTDBMS [6]. The main
difference is the applicability of different data replication policies. In our works, DRTDBMS
model is defined by the interconnection between many centralized RTDBMS. We consider a
main memory database model on each site, in which the CPU is the main system resource taken
into account.
2.2.1. Data model
In this model, data objects are classified into either real-time or non real-time data. In our model,
we consider both types of data objects. Non real-time data are classical data found in
conventional databases, whereas real-time data have a validity interval beyond which they
become stale. These data may change continuously to reflect the real world state (e.g., the current
temperature value). Each real-time data has a timestamps which define its last observation in the
real world state [9], a validity interval and a value.
2.2.2. Data replication model
In DRTDBMS, data replication is very attractive in order to increase the chance of distributed
real-time transaction to meet its deadline, system throughput and provide fault-tolerance.
However, it is a challenge to keep data copies consistent. For that purpose, two types of data
replication policies have been developed in literature.
The first data replication policy is the full data replication; the entire database at each node is
replicated to all other sites which admit transactions that can use their replicas data. Therefore, all
the data of this database will be available in different sites which facilitates access by the various
local and remote transactions.
The second policy data replication is the partial data replication policy, which is based on access
history of all transactions in each node. In fact, for each node, if the number of access on one data
object by current transactions reach a threshold then current node request to replicate locally this
data object. Therefore, this policy consist of replicate the most accessed data object of the most
accessed nodes to satisfy various user requests.
In this paper, we present the third policy. We called it the "Semi-total data replication". Wei et al.
[8] have proposed a replication model that only temporal data had replicated and the updating
replicas data is propagate at the same time to all replicas in each other nodes. In the replicated
environment, the frequently user operations on data replicas are read operations. In this case, to
guarantee the replicas data freshness, many efficient replica management algorithms was added in
literature to manage the data replicas at every node that supported data replication. All proposed
algorithms aims to meet transactions deadlines and data freshness guarantees.
Here, we discuss the difference between the two data replication policies. The full replication is
characterized by a maximum number of replicated data. Then, full database replication means that
all sites store copies of all data items. An analytical study in [12] has shown the scalability limits
of full replication. Therefore, the time needed for updating replicated data is quite important. In
some protocols update transactions are executed to preserve all the databases consistency. By
taking into consideration the time of transmission of messages between sites, the chance to
respect distributed real-time transactions decreases. However, partial data replication policy only
assigns copies of a frequently accessed data item. When there is a high update workload, full
5. Computer Science & Information Technology (CS & IT) 79
replication has too much overhead to keep all copies consistent and the individual replicas have
little resources left to execute read operations. In contrast, with partial replication, updating
protocols for replica data only has to execute the updates for mostly accessed replica data items,
and thus, it has more potential to execute read operations.
In the next section, we present our QoS management algorithms based on a different data
replication policy.
2.2.3. Transaction model
In this model, we use the firm transaction model, in which tardy transactions are aborted because
they can't meet their deadlines. In fact, transactions are classified into two classes: update
transactions and user transactions. Update transactions are used to update the values of real-time
data in order to reflect the real world state. These transactions are executed periodically and have
only to write real-time data. User transactions, representing user requests, arrive aperiodically.
They may read real-time data, and read or write non real-time data.
Furthermore, each update transaction is composed only by one write operation on real-time data.
User transactions are composed of a set of sub-transactions which are executed at local node or at
remote nodes that participate in the execution of the global system.
We consider that a user transaction may arrive at any node of the global system and define its
data needs. If all data needed by the transaction exist at the current site, then the transaction is
executed locally. Otherwise, the transaction, called real-time distributed user transaction, is split
into sub-transactions according to the location of their data. Those sub-transactions are transferred
and executed at corresponding nodes.
There are two sub-types of real-time distributed user transaction: remote and local [3]. Remote
transactions are executed at more than one node, whereas the local transactions are executed only
at one node. There is one process called coordinator which is executed at the site where the
transaction is submitted (master node). There is also a set of other processes called cohorts that
execute on behalf of the transaction at other sites accessed by the transaction(cohort node). The
transaction is an atomic unit of work, which is either entirely complete or not at all. Hence, a
distributed commit protocol is needed to guarantee uniform commitment of distributed
transaction execution. The commit operation implies that the transaction is successful, and hence
all of its updates should be incorporated into the database permanently. An abort operation
indicates that the transaction has failed, and hence requires the database management system to
cancel or abolish all of its effects in the database system. In short, a transaction is an all or
nothing unit of execution.
2.3. Performance metrics
The main performance metric, we consider in our model, is the Success Ratio (SR). It is a QoS
parameter which measures the percentage of transactions that meet their deadlines. It is defined as
follows:
( )
#
1 0 0 %
# #
T im e ly
S R
T im e ly T a rd y
= ×
+
Where #timely and #tardy represent, respectively, the number of transactions that have met and
missed their deadlines.
6. 80 Computer Science & Information Technology (CS & IT)
3. QOS MANAGEMENT APPROACHES USING DIFFERENT DATA
REPLICATION POLICIES
In this paper, the number of nodes is larger than the number used in experiments in [8] for full
data replication policy. We have also proposed to apply other data replication policies that
dedicated to mid-size scale system.
Our work consists of a new approach to enhance the QoS in DRTDBMS. We apply two data
replication policies in conventional DFCS architecture; the semi-total replication and partial
replication, on the DFCS architecture using a greater number of nodes (16 nodes) of overall
system than classical DFCS architecture. Then, we have compared obtained results with those
both data replication policies and the result obtained with full data replication policy used in [8].
Moreover, in our replication model, we proposed to replicate both types of data; the classical data
and the temporal data. In this model, temporal replicas data are updating transparently, and the
classical replicas data are updating as RT-RCP protocol presented by Haj Said et al. [3].
3.1 Approach using semi-total replication policy
In the first part, we propose a new algorithm replication called semi-total replication of real-time
and non real-time data (cf. Algorithm 1). In this proposed data replication policy, the system
execution start running without any database replication. Distributed real-time transactions
require data located in local or in remote nodes. Each node define an access counter for
transactions which require data located on remote sites which is define by an access counter of
mostly accessed sites. In case where the number of accessed remote data at each node ni reaches a
maximum threshold, then, the current node request the full replication of the database from node
ni. In other case, if the number of accessed remote data at each node ni reaches a minimum
threshold, then the current node decide to remove the full replicated database from node ni. The
maximum and the minimum threshold parameters are defined as input parameter to the system.
Algorithm 1: The semi-total data replication policy
nbAccNode: number of accessed remote node by all transactions from current node
nbAccDataNi: number of accessed remote data by all transactions from current node Ni
MaxAccNodeThres: maximum number of accessed remote node by all transactions.
MinAccNodeThres: minimum number of accessed remote node by all transactions.
Ni : Node i.
7. Computer Science & Information Technology (CS & IT) 81
3.2 Approach using partial replication policy
In the second part of our work, we propose to apply a partial data replication policy of real-time
and non real-time data (cf. Algorithm 2) in DFCS architecture. This technique may provide good
management of database storage by reducing the updating replica data of the overfull system.
The principle of this algorithm is to start running system execution without any real-time and non
real-time data replication. User’s transactions require data located in local or in remote nodes. We
use an accumulator of accessed remote data on other nodes by all transactions of the current node.
In this policy, the access counter is calculated for each data, this means that it uses two
accumulators; the first consists of the number of the frequently accessed remote nodes, and the
second consists of the number of the accessed remote data on frequently accessed nodes. in the
first case, if the number of accessed remote data at each node ni reaches a maximum threshold,
then the current node requests the data replication from node ni. In another case, if the number of
accessed remote data at each node ni reaches a minimum threshold, then the current node decide
to remove the current replicated data from node ni. The maximum and the minimum threshold
parameters are defined as input parameter to the system.
The both of those algorithms do not overload the system any more by updating replica data
workload compared with full data replication policy. Each one of them has advantages to
enhancing the QoS performance in DRTDBMS. This assertion for both algorithms is validated
through a set of simulations.
Algorithm 2: The partial data replication policy
nbAccNode: number of accessed remote node by all transactions from current node
nbAccDataNi: number of accessed remote data by all transactions from current node Ni
nbAccDataOcc: number of occurrences of accessed remote data from current node Ni
MaxAccNodeThres: maximum number of accessed remote node by all transactions.
MinAccNodeThres: minimum number of accessed remote node by all transactions.
MaxAccDataThres: maximum number of accessed remote data from current node by all
transactions.
MinAccDataThres: minimum number of accessed remote data from current node by all
transactions.
Ni : Node i.
8. 82 Computer Science & Information Technology (CS & IT)
4. SIMULATIONS AND RESULTS
To validate our QoS management approach, we have developed a simulator. In this section, we
describe the overall architecture of the simulator, and we present and comments the obtained
results.
4.1. Simulation model
Our simulator is composed of:
• Database: it consists of a data generator that generates data randomly avoiding duplicate
information. The consistency in the database is provided by update transactions. In our
simulator, the database contains real-time data and classic data.
• Generator of transactions: it is composed of two parts.
o User transactions generator: it generates user transactions using a random
distribution, taking into account their unpredictable arrival.
9. Computer Science & Information Technology (CS & IT) 83
o Update transactions generator: it generates update transactions according to an
arrival process that respects the periodicity of transactions.
• Precision controller: it rejects update transactions when the data to be updated are
sufficiently representative of the real world, based on the value of MDE.
• Scheduler: it is used to schedule transactions according to their priorities.
• Freshness manager: it checks the freshness of data that will be accessed by transactions.
If the accessed data object is fresh, then the transaction can be executed, and it will be
sent to the transactions handler. Otherwise, it will be sent to the block queue. Then, it will
be reinserted in the ready queue when the update of the accessed data object is
successfully executed.
• Concurrency controller: it is responsible for the resolution of data access conflicts.
Based on priorities (deadlines) of transactions, it determines which one can continue its
execution and which one should be blocked, aborted or restarted.
• Distributed commit protocol: it manages the global distributed real-time transaction to
ensure that all participating nodes agree with the final transaction result (validation or
abortion) [9,10]. In our simulator, we use the commit protocol called “Permits Reading
Of Modified Prepared-data for Timeliness” (PROMPT) defined in [4] which allows
transactions accessing data not committed (optimistic protocol).
• Admission controller: the main role of this component is to filter the arrival of user
transactions, depending on the system workload and respecting deadlines of transactions.
Then, accepted transactions will be sent to the ready queue.
• Handler: it is used to execute transactions. If a conflict between transactions appears, it
calls the concurrency controller.
• Global load balancers (GLB): it ensures that the load on each node does not affect the
functioning of other nodes. So the GLB is used to balance the system load by transferring
transactions from overloaded nodes to less loaded nodes in order to maintain QoS.
The input interface of our simulator allows choosing parameter values by which each simulation
runs. It includes general parameters of the system, parameters for generating database, generating
update transactions and generating user transactions. It also enables to choose the scheduling
algorithm, the concurrency control protocol and the real-time distributed validation protocol.
Once the simulation is well finished, results are saved in an output file. We can also show results
in the form of curves, pie charts or histograms.
4.2. Simulation settings
The performance evaluation of the DRTDBMS is achieved by a set of simulation experiments, in
which we varied some of the parameter values. Table 1 summarizes the general system
parameters settings used in our simulations. The DRTDBMS is composed of 16 nodes. In each
node we have 200 real-time data objects and 10000 classic data objects. Validity values of real-
time data are distributed between 500 milliseconds and 1000 milliseconds. For real-time data, the
value of MDE varies by 1 between 1 and 10. We choose the universal method to update real-time
data. Within each queue in our system, transactions are scheduled according to the EDF
algorithm. We use the 2PL-HP protocol for concurrency control. For the validation of
10. 84 Computer Science & Information Technology (CS & IT)
transactions, the distributed algorithm PROMPT is chosen. Update transactions are generated
according to the number of real-time data in the database.
Table 1. System parameter settings.
Parameter Value
Simulation time 3000 ms
Number of nodes 16
Number of real-time data 200/node
Number of classic data 10000/node
Validity of real-time data [500,1000]
MDE value [1,10]
Method to update real-time data Universal
Scheduling algorithm EDF
Concurrency control algorithm 2PL-HP
Distributed validation algorithm PROMPT
Parameter settings for user transactions are defined in Table 2. User transactions are a set of read
and write operations (from 1 to 4). Read operations (from 0 to 2) can access real-time data objects
and classic data objects, however, write operations (from 1 to 2) access only classic data objects.
The time of one read operations is used to be 1 millisecond, and for one write operation is set to 2
milliseconds. We set the slack factor of transactions to 10. The remote data ratio, which
represents the ratio of the number of remote data operations to that of all data operations, is set to
20%. We note that user transactions are generated at arrival times calculated according to the
"Poisson" process, which uses the value of the lambda parameter to vary the number of
transactions.
Table 2. User transactions parameter settings.
Parameter Value
Number of write operations [1,2]
Number of read operations [0,2]
Write operation time (ms) 2
Read operation time (ms) 1
Slack factor 10
Remote data ratio 20 %
Parameter settings for the real-time and non real-time data replication parameter settings are
given in Table 3. To simulate using the partial data replication policy, we have to fix a minimum
and a maximum threshold for nodes supporting replication which are set respectively to 0.2 and
0.5. We have to define, also, the value of the minimum and the maximum threshold of the number
of accessed remote data at each node, which are set to 0.1 and 0.2. In case of a simulation with
semi-total replication, only maximum values of the threshold of accessed remote nodes to
replicate their database have to be fixed.
11. Computer Science & Information Technology (CS & IT) 85
Table 3. Data replication parameter settings.
Parameter Value
Maximum threshold of nodes
supporting replication
0.5
Maximum threshold of data to be
replicated
0.2
Minimum threshold of nodes
supporting replication
0.2
Minimum threshold of data to be
replicated
0.1
4.3. Simulation principle
To evaluate the performance of the proposed QoS management approach to enhance the
performance of the overall system, we conducted a series of simulations by varying values of
some parameters. Each transaction, whatever its type (user or update), undergoes a series of tests
since its creation to its execution. In experiments, the workload distribution is initially balanced
between all participating nodes.
4.3.1. Simulation using semi-total data replication policy
The first set of experiments evaluates the QoS management using semi-total data replication
policy. For each node, an accumulator counter is used to calculate the mostly accessed remote
nodes which the current node requests to replicate fully their databases.
4.3.2. Simulation using partial data replication policy
In this set of simulations, we evaluate the QoS management using partial data replication policy.
For each node, as with semi-total replication, an accumulator counter is used to calculate the
mostly accessed remote nodes. For each frequently accessed node, an accumulator counter is used
to calculate the mostly accessed data which are requested to be replicated in the current node.
4.4. Results and discussions
As shown in Figure 2, the transactions success ratio is not affected by the increase of incoming
user transactions. In fact, the system workload remains balanced and the system behaviour is
maintained in stable state. Also, it is shown that the number of successful transactions with partial
and semi-total data replication policies is greater than with full data replication policy. Indeed, the
QoS guarantees is defined by increasing the number of transactions that meet their deadlines
using fresh data.
We can say that the use of semi-total and partial data replication policy is suitable when
increasing the number of participating nodes in DRTDBMS. By this way, the applicability of
these data replication policies provides an optimistic management of the database storage while
using fresh data by reducing the time of updating replicas.
12. 86 Computer Science & Information Technology (CS & IT)
0
10
20
30
40
50
60
70
80
90
100
0 200 400 600 800 1000 1200 1400 1600
Successratio
Number of transactions
full replication
semi-total
replication
Figure 2. Simulation results for user transactions
The proposed QoS management, using semi-total and partial data replication policies, provides a
better QoS guarantees than full data replication policy, ensuring stability and robustness of
DRTDBMS.
5. CONCLUSION AND FUTURE WORK
In this article, we presented our QoS management approach to provide QoS guarantees in
DRTDBMS. This approach is an extension work of the DFCS architecture proposed by Wei et al.
[8]. It consists on applying two data replication policies, semi-total replication and partial
replication of both classical data and real-time data on the conventional DFCS architecture, in
order to make DRTDBMS more robust and stable. Indeed, the proposed approach is defined by a
set of modules for data and transaction management in distributed real-time environment. The
proposed approach helps to establish a compromise between real-time and data storage
requirements by applying different data replication policies.
In future work, we will propose an approach for QoS enhancement in DRTDBMS using multi-
versions data with both semi-total and partial data replication policies.
REFERENCES
[1] Amirijoo, M. & Hansson J. & Son, S.H., (2003) « Specification and Management of QoS in Imprecise
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