Load balancing in the presence of random node failure and recovery

Apolipoprotein E 4 Allele (APOE 4) is an important factors in Mild cognitive impairment (MCI) and Alzheimer’s disease(AD). It plays a primary role in abnormal modification of aggregated Tau protein-paired helical filaments Tau (PHF-Tau). In this study, 143 subjects with PHF-Tau PET were divided into 2 groups (APOE 4 carriers and noncarriers). The measurements of the PHF-Tau network properties and resilient were calculated for 2 group networks respectively. APOE 4 carriers group showed significant differences in all the network properties in the results. We also found significant differences of betweenness centrality in some brain regions for APOE 4 carriers. Moreover, the APOE 4 carriers showed less resilient to targeted or random node failure. Our results indicated that the effects of APOE 4 may lead to abnormalities of PHF-Tau protein network. These findings may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI patients.

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Robustness of multipartite networks in face of random node failure

Chaos Solitons & Fractals ◽

10.1016/j.chaos.2019.01.036 ◽

2019 ◽

Vol 121 ◽

pp. 149-159 ◽

Cited By ~ 1

Author(s):

Zhaoxing Li ◽

Li Chen

Keyword(s):

Node Failure ◽

Random Node

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Reliable Task Allocation in Heterogeneous Distributed System with Random Node Failure: Load Sharing Approach

2012 International Conference on Computing Sciences ◽

10.1109/iccs.2012.62 ◽

2012 ◽

Cited By ~ 2

Author(s):

Vinod Kumar Yadav ◽

Mahendra Pratap Yadav ◽

Dharmendra Kumar Yadav

Keyword(s):

Distributed System ◽

Task Allocation ◽

Failure Load ◽

Load Sharing ◽

Node Failure ◽

Random Node

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Load-Balancing Performance of Consistent Hashing: Asymptotic Analysis of Random Node Join

IEEE/ACM Transactions on Networking ◽

10.1109/tnet.2007.893881 ◽

2007 ◽

Vol 15 (4) ◽

pp. 892-905 ◽

Cited By ~ 19

Author(s):

Xiaoming Wang ◽

Dmitri Loguinov

Keyword(s):

Load Balancing ◽

Asymptotic Analysis ◽

Consistent Hashing ◽

Random Node

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DISTRIBUTED LOAD BALANCING IN THE PRESENCE OF NODE FAILURE AND NETWORK DELAYS

IFAC Proceedings Volumes ◽

10.3182/20050703-6-cz-1902.01561 ◽

2005 ◽

Vol 38 (1) ◽

pp. 124-129 ◽

Cited By ~ 1

Author(s):

J. Ghanem ◽

C.T. Abdallah ◽

M.M. Hayat ◽

J. Chiasson ◽

J.D. Birdwell

Keyword(s):

Load Balancing ◽

Node Failure ◽

Distributed Load ◽

Network Delays

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Comparing Spark with MapReduce

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2018070101 ◽

2018 ◽

Vol 9 (3) ◽

pp. 1-22 ◽

Cited By ~ 2

Author(s):

Goutham Miryala ◽

Simone A. Ludwig

Keyword(s):

Load Balancing ◽

Optimization Techniques ◽

Failure Recovery ◽

High Dimensional ◽

Swarm Optimization ◽

Node Failure ◽

Evaluation Functions ◽

Glowworm Swarm Optimization ◽

Efficient Communication ◽

Spark Framework

Glowworm swarm optimization (GSO) is one of the optimization techniques, which need to be parallelized in order to evaluate large problems with high-dimensional function spaces. There are various issues involved in the parallelization of any algorithm such as efficient communication among nodes in a cluster, load balancing, automatic node failure recovery, and scalability of nodes at runtime. In this article, the authors have implemented the GSO algorithm with the Apache Spark framework. Even though we need to address how to distribute the data in the cluster to improve the efficiency of algorithm, the Spark framework is designed in such a way that one does not need to deal with any actual underlying parallelization details. For the experimentation, two multimodal benchmark functions were used to evaluate the Spark-GSO algorithm with various sizes of dimensionality. The authors evaluate the optimization results of the two evaluation functions as well as they will compare the Spark results with the ones obtained using a previously implemented MapReduce-based GSO algorithm.

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Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841125 ◽

2018 ◽

pp. 96-101

Author(s):

Shailendra Raghuvanshi ◽

Priyanka Dubey

Keyword(s):

Load Balancing ◽

Task Scheduling ◽

Waiting Time ◽

Virtual Machines ◽

Scheduling Algorithm ◽

Cloud Environment ◽

Task Scheduling Algorithm ◽

Machine Utilization ◽

Independent Tasks ◽

Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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