A novel leader election algorithm based on resources for ring networks

Biswas, Tarun; Bhardwaj, Rajat; Ray, Anjan Kumar; Kuila, Pratyay

doi:10.1002/dac.3583

Cited by 13 publications

(10 citation statements)

References 31 publications

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“…Crash Failures. In the context of crash failures, leader election in fully-identified rings have already been investigated in [27]: after a crash, the topology is re-organized as a ring where the node with the highest calculated priority is elected. Notice that this paper also considers the join of new nodes.…”

Section: Long-term Perspectivesmentioning

confidence: 99%

Election in unidirectional rings with homonyms

Altisen¹,

Datta

Devismes³

et al. 2020

Journal of Parallel and Distributed Computing

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Section: Long-term Perspectivesmentioning

confidence: 99%

Election in unidirectional rings with homonyms

Altisen¹,

Datta

Devismes³

et al. 2020

Journal of Parallel and Distributed Computing

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“…To deal with the problem of single leader election, two approaches have been presented in [9], which worked with nominating multiple leaders to avoid the risk of communication delays and minimized the latency rate. ree empirical techniques were employed in a cohesive way for calculating the proper status of leaders in a polynomial period.…”

Section: Related Workmentioning

confidence: 99%

“…(2) while i � 1 to total population (3) population list � randomly assign each job to a host (4) end while (5) calculate the cost (); ( 6) if (time � � scheduling interval) ( 7) for (! Stopping criteria) (8) Perform Crossover Function(); (9) Perform Mutation Function(); (10) Calculate Fitness Function (); (11) end for (12) leader and candidate node � host with the maximal value from equation (1) in the first index of the population list is head node and the second is candidate (13) if (Head Node Failed) (14) candidate � host at the second index of the population list became a head node (15) end if (16) different scenarios and gave different weightages to parameters according to the user requirement. In scenario 1, the range of hosts is fixed as 50 and the number of tasks/jobs is increasing by 1000.…”

Section: Evaluation Through Different Weightages (θ)mentioning

confidence: 99%

“…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%

“…A bully algorithm is proposed in [17][18][19][20] for leader election which selects the leader nodes dynamically based on node ID criteria. In [9,21,22], the authors proposed a ring algorithm, in which each node shares its ID with all other nodes and keeps track of them in a database. Based on priority, the algorithm selects one node as a leader from this list.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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