Dynamic scheduling of tasks in cloud computing applying dragonfly algorithm, biogeography-based optimization algorithm and Mexican hat wavelet

Shirani, Mohammad Reza; Safi-Esfahani, Faramarz

doi:10.1007/s11227-020-03317-8

Cited by 32 publications

(20 citation statements)

References 80 publications

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“…This algorithm explored and exploited the solution space by the inspiration of hunting and emigration behavior of dragonflies in nature. Masdari and Zangakani 21 presented a wide‐ranging survey and an outline of the intercloud scheduling schemes aimed to allocate user‐submitted tasks and workflows to the suitable VMs on multiple clouds concerning a number of objectives and factors. Lawanyashri et al 22 have developed hybrid fruitfly optimization algorithm for load balancing in cloud environments for healthcare organization.…”

Section: Related Workmentioning

confidence: 99%

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Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

Ramathilagam¹,

Vijayalakshmi²

2021

Int J Communication

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Summary Workflow scheduling is the most focused research issue in the on‐demand clouds where the user satisfaction like cost and bandwidth is more difficult. Several research works have been conducted earlier towards performing reliable workflow scheduling with the aim of reducing cost or execution time. However, those works lack to produce better result by compromising any attributes for attaining the goal. The existing work lacks from the security where the tasks might get corrupted during execution. To resolve this problem, an Enhanced Artificial Fish Swarm Algorithm (EAFSA)‐based IaaS Cloud Partial Critical Path (IC‐PCP) Replication and Hyper Elliptic Curve Cryptography (EAFSAIPR with HECC) is proposed. The main goal is to perform better workflow scheduling which can complete the task execution before deadline given by the users. This is done by predicting the early start time and latest finish time using EAFSA algorithm, so that task replication can be made to meet the soft deadline constraint. The task authentication is done efficiently using HEEC algorithm, so that corruption from malicious users can be avoided. The task replication is done securely using the cryptographic algorithm. The proposed EAFSAIPR with HECC algorithm uses idle time of provisioned resources to replicate workflow tasks optimally. The proposed EAFSAIPR algorithm scheduler focused to ensure the lowest cost while serving a deadline set by the user. The experimental results show that the scheduler can find good schedules of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

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Section: Related Workmentioning

confidence: 99%

“…The HECC methodology is assigned for ensuring the higher security in this research work. The hyper elliptic curves 20,21 accomplish the good security level with a little key length as differentiated to the cryptosystems using the elliptic curves. The novel class of public key methodologies is based on the hyper elliptic curves.…”

Section: Heccmentioning

confidence: 99%

Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

Ramathilagam¹,

Vijayalakshmi²

2021

Int J Communication

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“…HSI in BBO corresponds to fitness in GA, they both represent the objective function of a problem. SIV in BBO corresponds to GA, they all represent variable in a problem [28].…”

Section: Concepts Of Bbomentioning

confidence: 99%

Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

Wang¹,

Wu²,

Zhang³

et al. 2020

IJCIS

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Corona virus disease 2019 (COVID-19) is an acute infectious pneumonia and its pathogen is novel and was not previously found in humans. As a diagnostic method for COVID-19, chest computed tomography (CT) is more sensitive than reverse transcription polymerase chain reaction. However, the interpretation of COVID-19 based on chest CT is mainly done manually by radiologists and takes about 5 to 15 minutes for one patient. To shorten the time of interpreting the CT image and improve the reliability of identification of COVID-19. In this paper, a novel chest CT-based method for the automatic detection of COVID-19 was proposed. Our algorithm is a hybrid method composed of (i) wavelet Renyi entropy, (ii) feedforward neural network, and (iii) a proposed three-segment biogeography-based optimization (3SBBO) algorithm. The wavelet Renyi entropy is used to extract the image features. The novel optimization method of 3SBBO can optimize weights, biases of the network, and Renyi entropy order. Finally, we used 296 chest CT images to evaluate the detection performance of our proposed method. In order to reduce randomness and get unbiased result, the 10 runs of 10-fold cross validation are introduced. Experimental outcomes show that our proposed method is superior to state-of-the-art approaches in terms of sensitivity, specificity, precision, accuracy, and F1.

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“…It is not possible to perform benchmarking experiments in a repeatable, dispensable, and scalable environment using the real-world cloud, and simulation would be an alternative approach. According to paper [3], the CloudSim simulator is used to simulate a distributed environment with Java language and Planet-lab 2 data presented in CloudSim applied in [3,[23][24][25][26][27][28][29][30][31][32][33][34][35]. First, the described algorithms in the baseline research works are established, and the primary output is taken.…”

Section: Evaluationsmentioning

confidence: 99%

BFPF-Cloud: Applying SVM for Byzantine Failure Prediction to Increase Availability and Failure Tolerance in Cloud Computing

Beheshti

Safi-Esfahani

2020

SN COMPUT. SCI.

Self Cite

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One of the most important aspects of distributed systems is automatic failure recovery. In general, systems must be able to confront any type of failure. One issue is commonly overlooked in the subject of confronting the failures in services. Byzantine failures are the worst kind of arbitrary failures, either. The client should be ready for the worst possible conditions, especially if the server gives an answer that should never give. Sometimes, several servers, hand in hand together, make false answers deliberately. On the other hand, systems have no plan to protect themselves against byzantine failures that happen when the whole processes are not committed to a subject. It is possible that a server responds, but should not, and there is no way to detect that this is incorrect. The complexity of such failures is the main reason in cloud computing systems. However, several algorithms have been presented to detect failures by inspecting responses and address the problem. To detect Byzantine failures, the requests should be executed; first, responses should be created and gathered, and then, the responses are compared altogether. The failures occur when all the processes are unable to reach a consensus on an issue. In other words, Byzantine failures must occur first, and then, a solution should be considered to solve the problem. Accordingly, in cloud computing as a distributed infrastructure, the system should not be involved in severe failures. BFT-Cloud (Byzantine fault-tolerance cloud) as the previous research guarantees the robustness of systems when up to f of totally 3f + 1 resource providers are faulty. It uses replication techniques for overcoming failures, since a broad pool of nodes are available in the cloud. The challenge of the model is that a request should be executed several times to create a correct response that increases the number and duration of executions, either. While it is expected that all responses to requests to be correct without repeating the requests and needing to re-execute them. In this study, a framework is presented called BFPF-Cloud that introduces several features to be applied in algorithms that are based on support vector machine (SVM) to predict Byzantine failures. The reactive policy, along with the proactive one, is applied together to handle Byzantine failures. The main goal is to maintain reliability besides system availability. The experiments show that selecting the characteristics such as latency, PesNumber, MIPS, and failure probability of the replicas are the best features that SVM uses to predict Byzantine failures. In comparison to BFT-Cloud, the number of request re-execution and the execution time are decreased 69.91% on average; the number of repeated requests are decreased 69.78% on average; and throughput is improved 69.90% on average.

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Dynamic scheduling of tasks in cloud computing applying dragonfly algorithm, biogeography-based optimization algorithm and Mexican hat wavelet

Cited by 32 publications

References 80 publications

Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

Diagnosis of COVID-19 by Wavelet Renyi Entropy and Three-Segment Biogeography-Based Optimization

BFPF-Cloud: Applying SVM for Byzantine Failure Prediction to Increase Availability and Failure Tolerance in Cloud Computing

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