Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987760
Jaspal Singh, Major Singh Goraya
Nowadays, the field of Cloud Computing has particularly been becoming an emerging paradigm to deliver services over the internet. The cloud data center needs a set of virtualized resources for providing cloud infrastructure services based on user's demand. The need for optimal resource management turns into a vital issue in cloud data center to reduce the level of resource wastage and Service Level Agreement (SLA) violation. Meta heuristics algorithms are well suitable to resolve this issue which comes under NP-hard problem. The paper, proposes a Multi-Objective Hybrid fruit fly Optimization (MOHFO) based scheme for SLA aware dynamic resource management in cloud data center. The Bald Eagle Search (BES) optimization behaviour is adopted to enhance the searching ability for fruit fly optimization algorithm. The proposed scheme follows a dynamic virtual machine (VM) deployment and consolidation scheme to obtain a trade-off among SLA violation and resource wastage. The proposed scheme is simulated on Cloudsim with different data centre configurations and experimental results are evaluated based on QoS metrics. Moreover, the proposed MOHFO scheme is evaluated and compared with other optimization schemes with respect to resource wastage, energy consumption, communication cost and number of migrations performed in order to provide QoS aware optimal resource provisioning in cloud computing.
{"title":"Multi-Objective Hybrid Optimization based Dynamic Resource Management Scheme for Cloud Computing Environments","authors":"Jaspal Singh, Major Singh Goraya","doi":"10.1109/ICSSIT46314.2019.8987760","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987760","url":null,"abstract":"Nowadays, the field of Cloud Computing has particularly been becoming an emerging paradigm to deliver services over the internet. The cloud data center needs a set of virtualized resources for providing cloud infrastructure services based on user's demand. The need for optimal resource management turns into a vital issue in cloud data center to reduce the level of resource wastage and Service Level Agreement (SLA) violation. Meta heuristics algorithms are well suitable to resolve this issue which comes under NP-hard problem. The paper, proposes a Multi-Objective Hybrid fruit fly Optimization (MOHFO) based scheme for SLA aware dynamic resource management in cloud data center. The Bald Eagle Search (BES) optimization behaviour is adopted to enhance the searching ability for fruit fly optimization algorithm. The proposed scheme follows a dynamic virtual machine (VM) deployment and consolidation scheme to obtain a trade-off among SLA violation and resource wastage. The proposed scheme is simulated on Cloudsim with different data centre configurations and experimental results are evaluated based on QoS metrics. Moreover, the proposed MOHFO scheme is evaluated and compared with other optimization schemes with respect to resource wastage, energy consumption, communication cost and number of migrations performed in order to provide QoS aware optimal resource provisioning in cloud computing.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115667497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987838
B. Vinothini, N. Gnanambigai, P. Dinadayalan
Cloud computing has emerged as real world technology over the Internet. Due to the development of big data with high dimensionality, data storage possibility over cloud has created large scope in recent times. Document clustering is the fundamental topic that turned into an indispensable component in many areas like cloud computing. Document clustering partitions the document into significant classes or groups for retrieving the relevant document. Many researchers used the factorization methods and ontologies for internal and external knowledge based document clustering. However, existing methods failed to provide the semantic feature construction and leads to the information loss while covering all the ideas in documents. In order to address these problems, different document clustering techniques in cloud has been reviewed in this paper. In addition to that Document clustering by Entropy-based Boosting with Projection Neural Feature (EB-PNF) method is presented. The proposed method involves two stages. They are, similar document identification based on semantic similarity score, feature extraction which includes the extraction of both single and multi-label features based on the precision, recall and computational complexity to prove that EB-PNF method produces high-quality clusters comparable to the state-of-the-art methods.
{"title":"Boosting Projection Neural Features for Semantic Similar Clustered Documents in Cloud","authors":"B. Vinothini, N. Gnanambigai, P. Dinadayalan","doi":"10.1109/ICSSIT46314.2019.8987838","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987838","url":null,"abstract":"Cloud computing has emerged as real world technology over the Internet. Due to the development of big data with high dimensionality, data storage possibility over cloud has created large scope in recent times. Document clustering is the fundamental topic that turned into an indispensable component in many areas like cloud computing. Document clustering partitions the document into significant classes or groups for retrieving the relevant document. Many researchers used the factorization methods and ontologies for internal and external knowledge based document clustering. However, existing methods failed to provide the semantic feature construction and leads to the information loss while covering all the ideas in documents. In order to address these problems, different document clustering techniques in cloud has been reviewed in this paper. In addition to that Document clustering by Entropy-based Boosting with Projection Neural Feature (EB-PNF) method is presented. The proposed method involves two stages. They are, similar document identification based on semantic similarity score, feature extraction which includes the extraction of both single and multi-label features based on the precision, recall and computational complexity to prove that EB-PNF method produces high-quality clusters comparable to the state-of-the-art methods.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124368970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987887
Sravankumar Jogunuri, V. Mehra, D. Vyas
Fluctuating solar radiation greatly diminishes the output power from the solar photovoltaic modules. The most capable approach for improving the power output is considered as reconfiguration of solar photovoltaic modules through dynamically switching electrical connections. Efforts are made in this paper to discuss the current state of photovoltaic PV systems, sizing, optimizing and reconfiguration methodologies and recommended the efficient reconfiguration technique.
{"title":"Reconfiguration of Solar Photovoltaic Panels for Water Pumping Applications","authors":"Sravankumar Jogunuri, V. Mehra, D. Vyas","doi":"10.1109/ICSSIT46314.2019.8987887","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987887","url":null,"abstract":"Fluctuating solar radiation greatly diminishes the output power from the solar photovoltaic modules. The most capable approach for improving the power output is considered as reconfiguration of solar photovoltaic modules through dynamically switching electrical connections. Efforts are made in this paper to discuss the current state of photovoltaic PV systems, sizing, optimizing and reconfiguration methodologies and recommended the efficient reconfiguration technique.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"23 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120991880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987746
Anmol Verma, H. Monga, V. Singh
In this paper, sine-cosine algorithm (SCA) based tuning of three-mode proportional-integral-derivative (PID) controller is projected for controlling liquid level of interacting three tanks system that have their linear resistance elements. The optimal controller parameters are attained by minimizing the integral-square-error (ISE) of step response. Alpha and beta tables are used for calculating the ISE. For minimizing the ISE, the SCA algorithm is employed. The outcomes found using SCA are related with the other existing systems. Computer simulation shows that the SCA achieves better results in contrast to other controller settings.
{"title":"Sine-Cosine Algorithm Based PID Controller Design For Interacting Three-Tank System With Linear Resistance Elements","authors":"Anmol Verma, H. Monga, V. Singh","doi":"10.1109/ICSSIT46314.2019.8987746","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987746","url":null,"abstract":"In this paper, sine-cosine algorithm (SCA) based tuning of three-mode proportional-integral-derivative (PID) controller is projected for controlling liquid level of interacting three tanks system that have their linear resistance elements. The optimal controller parameters are attained by minimizing the integral-square-error (ISE) of step response. Alpha and beta tables are used for calculating the ISE. For minimizing the ISE, the SCA algorithm is employed. The outcomes found using SCA are related with the other existing systems. Computer simulation shows that the SCA achieves better results in contrast to other controller settings.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127222110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987824
N. Sundari, D. Anandhavalli, K. Dhivyalakshmi, S. Reshma
Breast Cancer is considered as the most frequent cancer among women nearly 2.1 million women are affected in each year. In order to recover the scenario, various image processing algorithms are used to detect breast cancer in its initial stage. Breast cancer can be usually recognized by following methodologies such as Mammograms, MRI, Ultrasound and Biopsy. Mammogram is a preliminary diagnosis methodology in breast cancer. In the proposed method three main stages are used. In the first stage input image is preprocessed by Discrete Wavelet Transform, Gray Level Co-occurrence Matrix is used as second stage to extract features in an image and In third stage Probabilistic Neural Network is used for classification of trained input images. Finally, the percentage of affected cells by tumor is calculated by Fuzzy C Means algorithm. By using proposed method the accuracy of tumor cells detection in its preliminary stage has been improved.
{"title":"Classification of Trained Input Images using Neural Networks","authors":"N. Sundari, D. Anandhavalli, K. Dhivyalakshmi, S. Reshma","doi":"10.1109/ICSSIT46314.2019.8987824","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987824","url":null,"abstract":"Breast Cancer is considered as the most frequent cancer among women nearly 2.1 million women are affected in each year. In order to recover the scenario, various image processing algorithms are used to detect breast cancer in its initial stage. Breast cancer can be usually recognized by following methodologies such as Mammograms, MRI, Ultrasound and Biopsy. Mammogram is a preliminary diagnosis methodology in breast cancer. In the proposed method three main stages are used. In the first stage input image is preprocessed by Discrete Wavelet Transform, Gray Level Co-occurrence Matrix is used as second stage to extract features in an image and In third stage Probabilistic Neural Network is used for classification of trained input images. Finally, the percentage of affected cells by tumor is calculated by Fuzzy C Means algorithm. By using proposed method the accuracy of tumor cells detection in its preliminary stage has been improved.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122042091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987895
G. Madhu, B Lalith Bharadwaj, G. Nagachandrika, K. Vardhan
Missing data value plays a significant role in medical research and its presence causes an adverse effect on machine learning and AI models which leads to the wrong insights for decision making. Past few decades, researchers have developed and applied various imputation approaches to real-world applications. In addition, imputation methods help us to build effective models to discover hidden patterns in medical applications that can provide insightful outcomes for better decision-making. In this paper, a new approach is proposed to impute the missing data value using XGBoost (eXtreme Gradient Boosting) of ensemble learning method for continuous attributes in medical datasets. The proposed methods are continuous type attribute imputations for continuous and discrete data attributes. In this approach, we impute each missing data attribute value by predicting its data value from non-missing data attributes. The experiments are conducted on benchmark medical datasets missing values ranging from 1.98% to 50.65% and compared with iterative imputation, KNN imputation, and missForest imputation. In our study, we observe that missXGBoost can successfully handle missing data attributes of continuous types of attributes and it outperforms other imputation methods.
{"title":"A Novel Algorithm for Missing Data Imputation on Machine Learning","authors":"G. Madhu, B Lalith Bharadwaj, G. Nagachandrika, K. Vardhan","doi":"10.1109/ICSSIT46314.2019.8987895","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987895","url":null,"abstract":"Missing data value plays a significant role in medical research and its presence causes an adverse effect on machine learning and AI models which leads to the wrong insights for decision making. Past few decades, researchers have developed and applied various imputation approaches to real-world applications. In addition, imputation methods help us to build effective models to discover hidden patterns in medical applications that can provide insightful outcomes for better decision-making. In this paper, a new approach is proposed to impute the missing data value using XGBoost (eXtreme Gradient Boosting) of ensemble learning method for continuous attributes in medical datasets. The proposed methods are continuous type attribute imputations for continuous and discrete data attributes. In this approach, we impute each missing data attribute value by predicting its data value from non-missing data attributes. The experiments are conducted on benchmark medical datasets missing values ranging from 1.98% to 50.65% and compared with iterative imputation, KNN imputation, and missForest imputation. In our study, we observe that missXGBoost can successfully handle missing data attributes of continuous types of attributes and it outperforms other imputation methods.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128772672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987933
Sunil K. Patidar, Saima Khan, S. Singh
In the normal feeling of the world, the word ‘security’ signifies the condition of being safe and the measures taken to guarantee security. In any case, wellbeing isn't an objective or an outright thing in light of the fact that regardless of utilizing a considerable lot of the security systems accessible there is no 100 percent security. Individuals have been making and utilizing numerous wellbeing strategies since antiquated occasions to secure their lives. Before, just things with physical nearness required insurance and security (physical security); for instance: a house was utilized to get security against the brutality of nature, watches were utilized to secure spots, and weapons were utilized to ensure people, watchtowers, doors, channels, locks, and different types of insurances. This paper present digital watermarking (DW) and steganography based secured technique to secure the text and image. The proposed technique is implemented MATLAB software and calculates MSE and PSNR.
{"title":"Security for Digital Image and Text Message with Steganography and Watermarking Technique","authors":"Sunil K. Patidar, Saima Khan, S. Singh","doi":"10.1109/ICSSIT46314.2019.8987933","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987933","url":null,"abstract":"In the normal feeling of the world, the word ‘security’ signifies the condition of being safe and the measures taken to guarantee security. In any case, wellbeing isn't an objective or an outright thing in light of the fact that regardless of utilizing a considerable lot of the security systems accessible there is no 100 percent security. Individuals have been making and utilizing numerous wellbeing strategies since antiquated occasions to secure their lives. Before, just things with physical nearness required insurance and security (physical security); for instance: a house was utilized to get security against the brutality of nature, watches were utilized to secure spots, and weapons were utilized to ensure people, watchtowers, doors, channels, locks, and different types of insurances. This paper present digital watermarking (DW) and steganography based secured technique to secure the text and image. The proposed technique is implemented MATLAB software and calculates MSE and PSNR.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128567345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987788
A.K. Thasleem Sulthana
The target of this paper is to re-enact and execute the BPSK framework on Spartan 3E FPGA. The balanced flag accomplished in transmitter pack, disregarded a channel and transmitted to second unit acts as demodulator. The adjusting signal accomplished at end of demodulator. BPSK framework is utilized as a part of is generally utilized as a part of CDMA innovation. This framework is mimicked by utilizing VHDL dialect and actualized on two Spartan 3E Starter unit sheets.
本文的目标是在Spartan 3E FPGA上重新制定和执行BPSK框架。在发射机组中完成的平衡标志，忽略一个信道并作为解调器发送到第二个单元。调节信号在解调器端完成。BPSK框架被用作CDMA创新的一部分。该框架利用VHDL方言进行模拟，并在两个Spartan 3E Starter单元表上实现。
{"title":"Simulation and Implementation of BPSK Modulator and Demodulator System on Spartan-3E FPGA","authors":"A.K. Thasleem Sulthana","doi":"10.1109/ICSSIT46314.2019.8987788","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987788","url":null,"abstract":"The target of this paper is to re-enact and execute the BPSK framework on Spartan 3E FPGA. The balanced flag accomplished in transmitter pack, disregarded a channel and transmitted to second unit acts as demodulator. The adjusting signal accomplished at end of demodulator. BPSK framework is utilized as a part of is generally utilized as a part of CDMA innovation. This framework is mimicked by utilizing VHDL dialect and actualized on two Spartan 3E Starter unit sheets.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130323461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987837
M. Kaur, Aakash Mohta
Recurrent Neural Network (RNN) is a deep learning model that uses the concept of supervised learning. Deep learning belongs to the family of machine learning. It is also called hierarchical learning or deep structured learning. The classic machine learning algorithms are definite, while the deep learning algorithms follow a chain of command. Deep learning has the capability to deal with more complex neural networks and it mainly deals with sequential data. Recurrent networks can process examples one at a time, preserving an element, that reflects over a long period of time.
{"title":"A Review of Deep Learning with Recurrent Neural Network","authors":"M. Kaur, Aakash Mohta","doi":"10.1109/ICSSIT46314.2019.8987837","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987837","url":null,"abstract":"Recurrent Neural Network (RNN) is a deep learning model that uses the concept of supervised learning. Deep learning belongs to the family of machine learning. It is also called hierarchical learning or deep structured learning. The classic machine learning algorithms are definite, while the deep learning algorithms follow a chain of command. Deep learning has the capability to deal with more complex neural networks and it mainly deals with sequential data. Recurrent networks can process examples one at a time, preserving an element, that reflects over a long period of time.","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129075765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/ICSSIT46314.2019.8987804
G. J. Mirobi, L. Arockiam
The cloud computing system complies the customer to access data and programs outside of the user's computing environment. Rather than storing the user's data and software on the user's personal computer or server, it is stored in the cloud. These cloud services comprise of applications, email, databases and file services. To access the cloud services, the requests are given as workflow. A workflow is a series of steps comprised of achieving a well-defined objective in a cloud environment. These steps are in a specific order to enhance the execution process and ensure efficiency. The main issue in executing the workflows is the uncertainty of the request process period and response period. The existing algorithms are not suitable to handle the above-mentioned issue. An efficient workflow scheduler is necessary to schedule the requests, select the requests and map the selected tasks to the appropriate VMs for handling their execution procedure while satisfying all dependencies, constraints and objective functions. The goal of the proposed workflow scheduler is to offer cloud services in a quite short time. The workflow scheduler is used to provide the cloud services with minimum process time and response time as defined in the Service Level Agreement (SLA).
{"title":"Dynamic Workflow Scheduling Approach for Minimizing the Response Time Using An Efficient Workflow Scheduler in Cloud Computing","authors":"G. J. Mirobi, L. Arockiam","doi":"10.1109/ICSSIT46314.2019.8987804","DOIUrl":"https://doi.org/10.1109/ICSSIT46314.2019.8987804","url":null,"abstract":"The cloud computing system complies the customer to access data and programs outside of the user's computing environment. Rather than storing the user's data and software on the user's personal computer or server, it is stored in the cloud. These cloud services comprise of applications, email, databases and file services. To access the cloud services, the requests are given as workflow. A workflow is a series of steps comprised of achieving a well-defined objective in a cloud environment. These steps are in a specific order to enhance the execution process and ensure efficiency. The main issue in executing the workflows is the uncertainty of the request process period and response period. The existing algorithms are not suitable to handle the above-mentioned issue. An efficient workflow scheduler is necessary to schedule the requests, select the requests and map the selected tasks to the appropriate VMs for handling their execution procedure while satisfying all dependencies, constraints and objective functions. The goal of the proposed workflow scheduler is to offer cloud services in a quite short time. The workflow scheduler is used to provide the cloud services with minimum process time and response time as defined in the Service Level Agreement (SLA).","PeriodicalId":330309,"journal":{"name":"2019 International Conference on Smart Systems and Inventive Technology (ICSSIT)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123371403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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