2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2015
Regarding the increasingly expanded utility of Cloud storage, the improvement of resources manage... more Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users' requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems' capability for responding to unexpected fault through software or hardware. This article represents an algorithm based on Learning Automata-oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The results indicate that the suggested Learning Automata Fault-Tolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availability in comparison to other similar algorithms.
Regarding the increasingly expanded utility of Cloud storage, the improvement of
resources manage... more Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users’ requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems’ capability for responding to unexpected fault through software or hardware. This paper represents an algorithm based on Learning Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The experimental results indicate that the proposed Learning Automata Fault-Tolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availibility in comparison with other similar approaches.
Regarding the increasingly expanded utility of
Cloud storage, the improvement of resources manage... more Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users’ requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems’ capability for responding to unexpected fault through software or hardware. This article represents an algorithm based on Learning Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The results indicate that the suggested Learning Automata FaultTolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availability in comparison to other similar algorithms.
In recent years, cloud computing is highly embraced and more organizations consider
at least some... more In recent years, cloud computing is highly embraced and more organizations consider at least some type of cloud strategy and apply theming their business process. Since failure is probable in cloud data centers and access to cloud resources available is fundamental, evaluation and application of different fault-tolerance methods is inevitable. On the other hand, the increasing growth of cloud storage users motivated us to study fault-tolerance techniques, and their strengths and weaknesses. In this paper, after introducing the concept off ault-tolerance in the context of cloud computing, the faulttolerant techniques are presented, and after introduction of some measures, a comparative analysis is provided.
2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2015
Regarding the increasingly expanded utility of Cloud storage, the improvement of resources manage... more Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users' requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems' capability for responding to unexpected fault through software or hardware. This article represents an algorithm based on Learning Automata-oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The results indicate that the suggested Learning Automata Fault-Tolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availability in comparison to other similar algorithms.
Regarding the increasingly expanded utility of Cloud storage, the improvement of
resources manage... more Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users’ requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems’ capability for responding to unexpected fault through software or hardware. This paper represents an algorithm based on Learning Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The experimental results indicate that the proposed Learning Automata Fault-Tolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availibility in comparison with other similar approaches.
Regarding the increasingly expanded utility of
Cloud storage, the improvement of resources manage... more Regarding the increasingly expanded utility of Cloud storage, the improvement of resources management in the shortest time to respond upon the users’ requests and the geographical constraints is of prime importance to both the Cloud service providers and the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is appraised by Cloud storage systems’ capability for responding to unexpected fault through software or hardware. This article represents an algorithm based on Learning Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic and query loads dispatched on data centers and learning automata that provides the best possible status for scaling up or down of data nodes. Based on appraisal of traffic on nodes, the node with the highest traffic is chosen for coping among physical nodes. The results indicate that the suggested Learning Automata FaultTolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availability in comparison to other similar algorithms.
In recent years, cloud computing is highly embraced and more organizations consider
at least some... more In recent years, cloud computing is highly embraced and more organizations consider at least some type of cloud strategy and apply theming their business process. Since failure is probable in cloud data centers and access to cloud resources available is fundamental, evaluation and application of different fault-tolerance methods is inevitable. On the other hand, the increasing growth of cloud storage users motivated us to study fault-tolerance techniques, and their strengths and weaknesses. In this paper, after introducing the concept off ault-tolerance in the context of cloud computing, the faulttolerant techniques are presented, and after introduction of some measures, a comparative analysis is provided.
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Papers by Seyed Mansour hosseini
resources management in the shortest time to respond upon the users’ requests and the
geographical constraints is of prime importance to both the Cloud service providers and
the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is
appraised by Cloud storage systems’ capability for responding to unexpected fault
through software or hardware. This paper represents an algorithm based on Learning
Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic
and query loads dispatched on data centers and learning automata that provides the best
possible status for scaling up or down of data nodes. Based on appraisal of traffic on
nodes, the node with the highest traffic is chosen for coping among physical nodes. The
experimental results indicate that the proposed Learning Automata Fault-Tolerant and
High-efficient Replication algorithm (LARFH) has utilization high replication, high query
efficiency, low cost and high availibility in comparison with other similar approaches.
Cloud storage, the improvement of resources management in the
shortest time to respond upon the users’ requests and the
geographical constraints is of prime importance to both the
Cloud service providers and the users. Since the Cloud storage
systems are exposed to failure, fault-tolerance is appraised by
Cloud storage systems’ capability for responding to unexpected
fault through software or hardware. This article represents an
algorithm based on Learning Automata–oriented approach to
fault tolerance data in Cloud storage regarding traffic and query
loads dispatched on data centers and learning automata that
provides the best possible status for scaling up or down of data
nodes. Based on appraisal of traffic on nodes, the node with the
highest traffic is chosen for coping among physical nodes. The
results indicate that the suggested Learning Automata FaultTolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availability in comparison to other similar algorithms.
at least some type of cloud strategy and apply theming their business process. Since
failure is probable in cloud data centers and access to cloud resources available is
fundamental, evaluation and application of different fault-tolerance methods is inevitable.
On the other hand, the increasing growth of cloud storage users motivated us to study
fault-tolerance techniques, and their strengths and weaknesses. In this paper, after
introducing the concept off ault-tolerance in the context of cloud computing, the faulttolerant techniques are presented, and after introduction of some measures, a
comparative analysis is provided.
resources management in the shortest time to respond upon the users’ requests and the
geographical constraints is of prime importance to both the Cloud service providers and
the users. Since the Cloud storage systems are exposed to failure, fault-tolerance is
appraised by Cloud storage systems’ capability for responding to unexpected fault
through software or hardware. This paper represents an algorithm based on Learning
Automata–oriented approach to fault tolerance data in Cloud storage regarding traffic
and query loads dispatched on data centers and learning automata that provides the best
possible status for scaling up or down of data nodes. Based on appraisal of traffic on
nodes, the node with the highest traffic is chosen for coping among physical nodes. The
experimental results indicate that the proposed Learning Automata Fault-Tolerant and
High-efficient Replication algorithm (LARFH) has utilization high replication, high query
efficiency, low cost and high availibility in comparison with other similar approaches.
Cloud storage, the improvement of resources management in the
shortest time to respond upon the users’ requests and the
geographical constraints is of prime importance to both the
Cloud service providers and the users. Since the Cloud storage
systems are exposed to failure, fault-tolerance is appraised by
Cloud storage systems’ capability for responding to unexpected
fault through software or hardware. This article represents an
algorithm based on Learning Automata–oriented approach to
fault tolerance data in Cloud storage regarding traffic and query
loads dispatched on data centers and learning automata that
provides the best possible status for scaling up or down of data
nodes. Based on appraisal of traffic on nodes, the node with the
highest traffic is chosen for coping among physical nodes. The
results indicate that the suggested Learning Automata FaultTolerant and High-efficient Replication algorithm (LARFH) has utilization high replication, high query efficiency, low cost and high availability in comparison to other similar algorithms.
at least some type of cloud strategy and apply theming their business process. Since
failure is probable in cloud data centers and access to cloud resources available is
fundamental, evaluation and application of different fault-tolerance methods is inevitable.
On the other hand, the increasing growth of cloud storage users motivated us to study
fault-tolerance techniques, and their strengths and weaknesses. In this paper, after
introducing the concept off ault-tolerance in the context of cloud computing, the faulttolerant techniques are presented, and after introduction of some measures, a
comparative analysis is provided.