Scalable Energy-Aware Dynamic Task Allocation

Bokar, A.; Bozyiğit, Müslim; Sener, Cevat

doi:10.1109/waina.2009.46

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…The dynamic task allocation scheme in [22] aims to prolong network lifetime by allocating tasks such that minimum residual energy is maximized in a cluster. The self-adaptive method of task allocation scheme [23] prolongs the lifecycle of a cluster by alleviating the load of the cluster head and balancing the load of nodes.…”

Section: Related Workmentioning

confidence: 99%

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Shi

Yang

et al. 2016

Pervasive and Mobile Computing

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An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds, Pervasive and Mobile Computing (2015), http://dx. ABSTRACTMobile Cloud Computing (MCC) enables mobile devices to use resource providers other than mobile devices themselves to host the execution of mobile applications. Various mobile cloud architectures and scheduling algorithms have been studied recently. However, how to utilize MCC to enable mobile devices to run complex real-time applications while keeping high energy efficiency remains a challenge. In this paper, firstly, we introduce the local mobile clouds formed by nearby mobile devices and give the mathematical models of the mobile devices and their applications. Secondly, we formulate the scheduling problem in local mobile clouds. After describing the resource discovery scheme and the adaptive, probabilistic scheduling algorithm, we finally validate the performance of the proposed algorithm by simulation experiments.

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Section: Related Workmentioning

confidence: 99%

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

Shi

Yang

et al. 2016

Pervasive and Mobile Computing

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“…The Weighted Clustering Algorithm (WCA) [3] to form a one hop clusters with one cluster head based on the weight metric. Nodes weights are calculated by the following factors, that are number of adjacent nodes, the total distance to all its adjacent nodes, mobility [4] [5] and remaining battery power [6]. This algorithm takes more time and reduces the scalability.…”

Section: Related Workmentioning

confidence: 99%

“…The Table 1 shows values for nodes within the single cluster. The above table shows the value of nodes according to (2) (5) (6). After finding the value of nodes evaluate the Node Weight Value (NWV) of each node.…”

Section: Resources(r)mentioning

confidence: 99%

An Efficient Cluster Based Service Discovery Model for Mobile Ad hoc Network

2015

KSII TIIS

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The use of web service has been increased rapidly, with an increase in the number of available services, finding the exact service is the challenging task. Service discovery is the most significant job to complete the service discoverers needs. In order to achieve the efficient service discovery, we focus on designing a cluster based service discovery model for service registering and service provisioning among all mobile nodes in a mobile ad hoc network (MANETs). A dynamic backbone of nodes (i.e. cluster heads) that forms a service repository to which MANET nodes can publish their services and/or send their service queries. The designed model is based on storing services with their service description on cluster head nodes that are found in accordance with the proposed cluster head election model. In addition to identifying and analyzing the system parameters for finding the effectiveness of our model, this paper studies the stability analysis of the network, overhead of the cluster, and bandwidth utilization and network traffic is evaluated using analytic derivations and experimental evaluation has been done.

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“…In order to make this classification, the node's energy is compared to that of the nodes that stand a distance of at most three-hops from it. This restriction simplifies the selection scheme, and reduces its energy consumption and required time, thereby reducing the overall selection cost [12] [13]. We will use the simple topology illustrated in Fig.…”

Section: Selection Schemementioning

confidence: 99%

Energy Aware Dynamic Server Selection and Task Allocation

Bokar

Bozyiğit

Sener

2008

2008 23rd International Symposium on Computer and Information Sciences

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___ Battery energy limitation is one of the main challenges in the mobile ad hoc networks. Several hardware and software based techniques have been proposed in this field. Energy aware task scheduling is one of the software methods where the scheduling policy aims at optimizing the energy. Most of the previous work have achieved significant energy savings for individual mobile nodes but did not consider overall network lifetime and scalability. In this paper, we propose an Energy Aware Dynamic Server Selection and Task Allocation technique (EADSSTA) for prolonging the network lifetime. In our model, the network lifetime is divided into many rounds. At the beginning of each round new servers are located. Based on the proposed technique, selected servers are used in the remote allocation such that minimum residual energy is maximized at the end of each round. The results of our simulation showed that our proposed scheme makes a significant improvement in the network lifetime while simultaneously minimizing the energy consumption and time delay for each task. INTRODUTIONMobile ad hoc networks (MANETs) are those which can be formed dynamically without the support of any existing infrastructure or any centralized administration. They consist of various mobile nodes which move arbitrarily and are connected by wireless links. MANETs have diversity of applications in many fields. For example, two of its common application areas are disaster recovery management and military applications, where infrastructure networks are almost impossible to form or maintain. Other important MANET applications are peer-to-peer dominant applications in business, industrial, crops monitoring, social event monitoring, and others [1].One of the most crucial issues in mobile systems is the lifetime prolongation of the battery life. In this concern, several hardware and software methods have been introduced. In general, two hardware techniques can be used for this sake; the first is to have an automatic switch off for the idle device in order to save energy, whereas the second method works in the Dynamic Voltage Scalable (DVS) processors. DVS processors have different speed levels which can be configured by changing the supply voltage. Based on the application requirements, the processor's speed is manipulated accordingly [2].As for software techniques, many efficient protocols have been designed to work in different layers in order to reduce the energy consumption. One of the recent software methods is the energyaware task scheduling, in which the tasks are scheduled by taking their energy (execution and communication) into account.In the recent years, a significant amount of work has been done in the field of energy-aware task scheduling. In [2][3], the authors proposed a two-phase real time dynamic task scheduling algorithm for the battery operated DVS system in order to maximize both, the residual energy and the battery voltage. This proposal is valid for single processor systems. Aperiodic and periodic static task scheduling for a single a...

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Scalable Energy-Aware Dynamic Task Allocation

Cited by 13 publications

References 5 publications

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

An energy-efficient scheduling scheme for time-constrained tasks in local mobile clouds

An Efficient Cluster Based Service Discovery Model for Mobile Ad hoc Network

Energy Aware Dynamic Server Selection and Task Allocation

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