{"title":"基于萤火虫算法的云计算环境下工作流调度","authors":"R. Sundarrajan, V. Vasudevan, S. Mithya","doi":"10.1109/ICEEOT.2016.7754828","DOIUrl":null,"url":null,"abstract":"Cloud computing is the new generation of networks that uses remote servers hosted on the Internet for various uses such as data storage, data management, software usage etc. There are huge amount of resources provided and users can make use of the resources in any way they want to. Today, researchers attempt to find newer ways for Workflow scheduling which could work well in the cloud environment. Workflow scheduling is the most important task in cloud computing field and users have to pay for resources that were used based in a pay-per-usage scheme. Hence Workflow scheduling plays a vital role in getting maximum benefit from the resources that are provided. Another important element to be considered about cloud computing is Load balancing. This controlling of fill assures that every exclusive machine does the very same amount of labour at any immediate of time. To make sure this, we want to recommend on using the idea of fill controlling. Here in this document, we recommend heuristic criteria known as Firefly criteria for effective fill controlling in reasoning processing. This criterion is based on the travel behaviour of the fireflies which go looking for the closest possible maximum alternatives. We employ Firefly algorithm to schedule the jobs and thereby evenly distribute the load and in turn reduce the overall completion time (makespan).","PeriodicalId":383674,"journal":{"name":"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Workflow scheduling in cloud computing environment using firefly algorithm\",\"authors\":\"R. Sundarrajan, V. Vasudevan, S. Mithya\",\"doi\":\"10.1109/ICEEOT.2016.7754828\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cloud computing is the new generation of networks that uses remote servers hosted on the Internet for various uses such as data storage, data management, software usage etc. There are huge amount of resources provided and users can make use of the resources in any way they want to. Today, researchers attempt to find newer ways for Workflow scheduling which could work well in the cloud environment. Workflow scheduling is the most important task in cloud computing field and users have to pay for resources that were used based in a pay-per-usage scheme. Hence Workflow scheduling plays a vital role in getting maximum benefit from the resources that are provided. Another important element to be considered about cloud computing is Load balancing. This controlling of fill assures that every exclusive machine does the very same amount of labour at any immediate of time. To make sure this, we want to recommend on using the idea of fill controlling. Here in this document, we recommend heuristic criteria known as Firefly criteria for effective fill controlling in reasoning processing. This criterion is based on the travel behaviour of the fireflies which go looking for the closest possible maximum alternatives. We employ Firefly algorithm to schedule the jobs and thereby evenly distribute the load and in turn reduce the overall completion time (makespan).\",\"PeriodicalId\":383674,\"journal\":{\"name\":\"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)\",\"volume\":\"109 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEOT.2016.7754828\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEOT.2016.7754828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Workflow scheduling in cloud computing environment using firefly algorithm
Cloud computing is the new generation of networks that uses remote servers hosted on the Internet for various uses such as data storage, data management, software usage etc. There are huge amount of resources provided and users can make use of the resources in any way they want to. Today, researchers attempt to find newer ways for Workflow scheduling which could work well in the cloud environment. Workflow scheduling is the most important task in cloud computing field and users have to pay for resources that were used based in a pay-per-usage scheme. Hence Workflow scheduling plays a vital role in getting maximum benefit from the resources that are provided. Another important element to be considered about cloud computing is Load balancing. This controlling of fill assures that every exclusive machine does the very same amount of labour at any immediate of time. To make sure this, we want to recommend on using the idea of fill controlling. Here in this document, we recommend heuristic criteria known as Firefly criteria for effective fill controlling in reasoning processing. This criterion is based on the travel behaviour of the fireflies which go looking for the closest possible maximum alternatives. We employ Firefly algorithm to schedule the jobs and thereby evenly distribute the load and in turn reduce the overall completion time (makespan).