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Decreasing Impact of SLA Violations: A Proactive Resource Allocation Approach for Cloud Computing Environments
"... Abstract—User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these char ..."
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Abstract—User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user’s hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users ’ satisfaction level that cause to gain in profitability. Index Terms—Users satisfaction level, cloud service, resource allocation, willingness to pay, learning automaton Ç 1
A mobility-based cluster formation algorithm for wireless mobile ad hoc networks
- Journal of Cluster Computing
"... In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad‐hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, wh ..."
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In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad‐hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, while they are stochastic and vary with time indeed. Therefore, the proposed clustering algorithms do not scale well in realistic MANETs, where the mobility parameters of the hosts freely and randomly change at any time. Finding the optimal solution to the cluster formation problem is incredibly difficult, if we assume that the movement direction and mobility speed of the hosts are random variables. This becomes harder when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a learning automata‐based weighted cluster formation algorithm called MCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster‐head. This is done based solely on the local information each host receives from its neighbors and the hosts need not to be synchronized. The experimental results show the superiority of MCFA over the best existing mobility‐ based clustering algorithms in terms of the number of clusters, cluster lifetime, reaffiliation rate, and control message overhead.
Distributed Learning Automata-Based Clustering Algorithm in Wireless Ad Hoc Networks
"... In ad hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering is a method by which the nodes are hierarchically organized on the basis of the proximity and thus the scalability problem is alleviated. Finding the weakly connected dominating set ..."
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In ad hoc networks, the performance is significantly degraded as the size of the network grows. The network clustering is a method by which the nodes are hierarchically organized on the basis of the proximity and thus the scalability problem is alleviated. Finding the weakly connected dominating set (WCDS) is a well-known approach, proposed for clustering the wireless ad hoc networks. Finding the minimum WCDS in the unit disk graph is an NP-Hard problem, and a host of approximation algorithms have been proposed. In this paper, an approximation algorithm based on distributed learning automata is first proposed for finding a near optimal solution to the minimum WCDS problem in a unit disk graph. Then, a distributed learning automata-based algorithm is proposed for clustering the wireless ad hoc networks. This clustering method is a generalization of the algorithm proposed for solving the WCDS problem, in which the dominator nodes and their closed neighbors assume the role of the cluster-heads and cluster members, respectively. The proposed clustering algorithm, in an iterative process tries to find a policy that determines a cluster-head set with the minimum cardinality for the network. For both algorithms, the simulation results show that they outperform the best existing algorithms in terms of the number of hosts (nodes) in the cluster-head set (dominating set).
Computers and Mathematics with Applications
"... journal homepage: www.elsevier.com/locate/camwa Sleep-based topology control in the Ad Hoc networks by using fitness aware learning automata ..."
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journal homepage: www.elsevier.com/locate/camwa Sleep-based topology control in the Ad Hoc networks by using fitness aware learning automata
A Coverage Monitoring Algorithm based on Learning Automata for Wireless Sensor Networks
, 2015
"... Abstract: To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-pl ..."
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Abstract: To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant de-ployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node is in active mode only if it is required to. In this paper, we propose an efficient scheduling method based on learning automata and we called it LAML, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing method.
Wireless Pers Commun (2014) 77:1923–1933 DOI 10.1007/s11277-014-1616-3 Learning Automata Based Face-Aware Mobicast
, 2014
"... © The Author(s) 2014. This article is published with open access at Springerlink.com Abstract Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protoc ..."
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© The Author(s) 2014. This article is published with open access at Springerlink.com Abstract Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protocol, called “mobicast”. In this protocol, it is assumed that the area around the moving target, called the delivery zone, is known at any given time during the operation of the network. The aim of the protocol is to awake sensor nodes, which will be within the delivery zone in the near future, to be prepared for tracking the approaching moving target. In this paper, we propose a novel mobicast algorithm, aiming at reducing the number of awakened sensor nodes. To this end, we equipped every sensor node with a learning automaton, which helps the node in determining the sensor nodes it must awaken. To evaluate the performance of the proposed algorithm, several experiments have been conducted. The results have shown that the proposed algorithm can significantly outperform other existing algorithms such as forward-zone constrained and FAR in terms of energy consumption, number of active nodes, number of exchanged packets and slack time.
Learn ing Automata Based Face- Aware Mobicast
, 2014
"... Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protocol, called mobicast. In this protocol, it is assumed that the area around the moving target, c ..."
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Target tracking is one of the most popular applications of the wireless sensor networks. It can be accomplished using different approaches and algorithms, one of which is the spatiotemporal multicast protocol, called mobicast. In this protocol, it is assumed that the area around the moving target, called the delivery zone, is known at any given time during the operation of the network. The aim of the protocol is to awake sensor nodes, which will be within the delivery zone in the near future, to be prepared for tracking the approaching moving target. In this paper, we propose a novel mobicast algorithm, aiming at reducing the number of awakened sensor nodes. To this end, we equipped every sensor node with a learning automaton, which helps the node in determining the sensor nodes it must awaken. To evaluate the performance of the proposed algorithm, several experiments have been conducted. The results have shown that the proposed algorithm can significantly
IEEE TRANSACTIONS ON CLOUD COMPUTING, SPECIAL ISSUE 1 Decreasing Impact of SLA Violations: A Proactive Resource Allocation Approach for
"... Abstract — User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these ch ..."
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Abstract — User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users ’ satisfaction level. The amount of this decrease depends on user’s characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user’s hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users ’ satisfaction level that cause to gain in profitability.
