A leader election algorithm for dynamic networks with causal clocks

Ingram, Rebecca; Radeva, Tsvetomira; Shields, Patrick; Viqar, Saira; Walter, Jennifer E.; Welch, Jennifer L.

doi:10.1007/s00446-013-0184-1

Cited by 21 publications

(11 citation statements)

References 27 publications

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“…We have compared the performance of our Topology Aware algorithm with a variant of the leader election algorithm of Vasudevan et al [12], because their work is a good example of a typical flooding algorithm and is strongly referenced in the literature [13], [14], [18], [22]. Results of experiments conducted on the PeerSim simulator [1] confirms the effectiveness of our algorithm and that it outperforms the latter.…”

Section: Introductionmentioning

confidence: 55%

“…The election criterion used by the algorithm is the lowest value of a movement-based counter. The algorithm was extended by [22] and [14] where the election of a new leader requires three diffusion waves over the network: two waves to search for a potential leader and one confirmation wave to spread the election result over the network. These waves induce a high number of message exchanges which slow down the election process.…”

Section: Related Workmentioning

confidence: 99%

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Topology Aware Leader Election Algorithm for Dynamic Networks

Favier

Guittonneau

Arantes

et al. 2020

2020 IEEE 25th Pacific Rim International Symposium on Dependable Computing (PRDC)

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This paper proposes an algorithm that eventually elects a leader for each connected component of a dynamic network where nodes can move or fail by crash. A node only communicates with nodes in its transmission range and locally keeps a global view, denoted topological knowledge, of the communication graph of the network and its dynamic evolution. Every change in the topology or in nodes membership is detected by one or more nodes and propagated over the network, updating thus the topological knowledge of the nodes. As the choice of the leader has an impact on the performance of applications that use an eventual leader election service, our algorithm, thanks to nodes topological knowledge, exploits the closeness centrality as the criterion for electing a leader. Experiments were conducted on top of PeerSim simulator [1], comparing our algorithm to a representative flooding algorithm. Performance results show that our algorithm outperforms the flooding one when considering leader choice stability, number of messages, and average distance to the leader.

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Section: Introductionmentioning

confidence: 55%

Section: Related Workmentioning

confidence: 99%

Topology Aware Leader Election Algorithm for Dynamic Networks

Favier

Guittonneau

Arantes

et al. 2020

2020 IEEE 25th Pacific Rim International Symposium on Dependable Computing (PRDC)

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“…It has been used in solutions to resource allocation [Chandy and Misra 1984;Barbosa and Gafni 1989;Malka et al 1993], distributed queuing [Tirthapura and Herlihy 2006;Attiya et al 2010], and various problems in mobile ad hoc networks, such as routing [Park and Corson 1997;Ko and Vaidya 2000], mutual exclusion [Raymond 1989;Naimi et al 1996;Walter et al 2001], and leader election [Malpani et al 2000;Derhab and Badache 2008;Ingram et al 2013].…”

Section: Introductionmentioning

confidence: 99%

Time Complexity of Link Reversal Routing

Charron-Bost

Függer

Welch

et al. 2015

ACM Trans. Algorithms

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Link reversal is a versatile algorithm design paradigm, originally proposed by Gafni and Bertsekas in 1981 for routing and subsequently applied to other problems including mutual exclusion, leader election, and resource allocation. Although these algorithms are well known, until now there have been only preliminary results on time complexity, even for the simplest link reversal algorithm for routing, called Full Reversal. In Full Reversal, a sink reverses all its incident links, whereas in other link reversal algorithms (e.g., Partial Reversal), a sink reverses only some of its incident links. Charron-Bost et al. introduced a generalization, called LR, that includes Full and Partial Reversal as special cases. In this article, we present an exact expression for the time complexity of LR. The expression is stated in terms of simple properties of the initial graph. The result specializes to exact formulas for the time complexity of any node in any initial acyclic directed graph for both Full and Partial Reversal. Having the exact formulas provides insight into the behavior of Full and Partial Reversal on specific graph families. Our first technical insight is to describe the behavior of Full Reversal as a dynamical system and to observe that this system is linear in min-plus algebra. Our second technical insight is to overcome the difficulty posed by the fact that LR is not linear by transforming every execution of LR from an initial graph into an execution of Full Reversal from a different initial graph while maintaining the execution's work and time complexity.

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“…For an effective leader election process in distributed networks and cloud computing environments, the research community has proposed several protocols [1][2][3][4][5][6][7][8] and algorithms (Biswas et al) [9][10][11][12][13][14][15][16]. A bully algorithm is proposed in [17][18][19][20] for leader election which selects the leader nodes dynamically based on node ID criteria.…”

Section: Introductionmentioning

confidence: 99%

“…Based on priority, the algorithm selects one node as a leader from this list. In [11,[23][24][25], node IDs are generated at random, and each node is assigned a priority number. After that, the node with the highest priority number is selected as the leader node.…”

Section: Introductionmentioning

confidence: 99%

Head Node Selection Algorithm in Cloud Computing Data Center

Kanwal

Iqbal

Irtaza

et al. 2021

Mathematical Problems in Engineering

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Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption.

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A leader election algorithm for dynamic networks with causal clocks

Cited by 21 publications

References 27 publications

Topology Aware Leader Election Algorithm for Dynamic Networks

Topology Aware Leader Election Algorithm for Dynamic Networks

Time Complexity of Link Reversal Routing

Head Node Selection Algorithm in Cloud Computing Data Center

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