Pub Date : 2017-12-01DOI: 10.1109/PDCAT.2017.00038
Z. Shan, Jianzhong Qiao, Shukuan Lin
The virtual machine (VM) live migration could achieve VM redistribution among distributed system hosts without reducing normal working performance. Post-copy is one of the wildly used VM live migration algorithm and has lots of advantages, such as less total migration time, less downtime, lower network overhead and so on. Its disadvantage is that the VM will be suspended frequently due to the page faults caused by the incomplete memory while VM is restored to run on destination host, which may lead to an extremely negative affect on VM work efficiency. To solve this problem, this paper proposes the pre-record algorithm. Pre-record extends the VM execution on source host, records the accessed memory pages during this period to obtain pre-recorded page set (PPS), and preferentially completes the migration of PPS to avoid page faults as much as possible. It also proposes PDoPMP algorithm to analysis the trend of the trajectory of memory address in PPS, in order to further expand the predict range of memory pages. The experimental results show that the pre-record has better efficiency than traditional post-copy, especially after combining with PDoPMP. It could obviously reduce the page faults number and then the total VM migration time without prolonging the downtime, and could improve VM migration efficiency under different workload and network conditions.
{"title":"Live Migration of Virtual Machine with Pre-Record and Use PDoPMP to Analyse Memory Access Trend","authors":"Z. Shan, Jianzhong Qiao, Shukuan Lin","doi":"10.1109/PDCAT.2017.00038","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00038","url":null,"abstract":"The virtual machine (VM) live migration could achieve VM redistribution among distributed system hosts without reducing normal working performance. Post-copy is one of the wildly used VM live migration algorithm and has lots of advantages, such as less total migration time, less downtime, lower network overhead and so on. Its disadvantage is that the VM will be suspended frequently due to the page faults caused by the incomplete memory while VM is restored to run on destination host, which may lead to an extremely negative affect on VM work efficiency. To solve this problem, this paper proposes the pre-record algorithm. Pre-record extends the VM execution on source host, records the accessed memory pages during this period to obtain pre-recorded page set (PPS), and preferentially completes the migration of PPS to avoid page faults as much as possible. It also proposes PDoPMP algorithm to analysis the trend of the trajectory of memory address in PPS, in order to further expand the predict range of memory pages. The experimental results show that the pre-record has better efficiency than traditional post-copy, especially after combining with PDoPMP. It could obviously reduce the page faults number and then the total VM migration time without prolonging the downtime, and could improve VM migration efficiency under different workload and network conditions.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121071370","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 : 2017-12-01DOI: 10.1109/PDCAT.2017.00051
Aye Min, Zin Mar Kyu
Brain tumor is the abnormal growth of cancerous cells in Brain. In medical field, segmentation of brain regions and detection of brain tumor are very challenging tasks because of its complex structure. Magnetic resonance imaging (MRI) provides the detailed information about brain anatomy. Proper brain tumor segmentation using MR brain images helps in identifying exact size and shape of Brain tumor, this intern helps in diagnosis and treatment of brain tumor. However, manual segmentation in magnetic resonance data is a time-consuming task and is still being difficult to detect brain tumor area in MRI. The main challenges of brain tumor detection are less of accuracy to detect tumor area and to segment the tumor area. The system proposed the results fusion method for image enhancement and combination of adaptive k-means clustering and morphological operation for tumor segmentation. All of the experimental results will be tested on BRATS multimodal images of brain tumor Segmentation Benchmark dataset.
{"title":"MRI Images Enhancement and Tumor Segmentation for Brain","authors":"Aye Min, Zin Mar Kyu","doi":"10.1109/PDCAT.2017.00051","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00051","url":null,"abstract":"Brain tumor is the abnormal growth of cancerous cells in Brain. In medical field, segmentation of brain regions and detection of brain tumor are very challenging tasks because of its complex structure. Magnetic resonance imaging (MRI) provides the detailed information about brain anatomy. Proper brain tumor segmentation using MR brain images helps in identifying exact size and shape of Brain tumor, this intern helps in diagnosis and treatment of brain tumor. However, manual segmentation in magnetic resonance data is a time-consuming task and is still being difficult to detect brain tumor area in MRI. The main challenges of brain tumor detection are less of accuracy to detect tumor area and to segment the tumor area. The system proposed the results fusion method for image enhancement and combination of adaptive k-means clustering and morphological operation for tumor segmentation. All of the experimental results will be tested on BRATS multimodal images of brain tumor Segmentation Benchmark dataset.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114935411","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 : 2017-06-17DOI: 10.1109/PDCAT.2017.00021
Hui Tian, Jiahuai Wu, Hong Shen
Abstract--- The virtual machine (VM) placement problem is a major issue in optimizing resource ulitization of cloud data centers. With the rapid development of cloud computing, efficient algorithms are needed to reduce the power consumption and save energy in data centers. Many models and algorithms are designed with a objective to minimize the number of physical machines (PMs) used in a cloud data center. In this paper, we take into account the execution time of the PM, and formulat a new optimization problem of VM placement, which aims to minimize the total execution time of the PMs. We discuss the NP-hardness of the problem, and present heuristic algorithms to solve it in both offline and online scenarios. Furthermore, we conduct experiments to evaluate the performance of the proposed algorithms and the result show that our methods are able to perform better than other commonly used algorithms.
{"title":"Efficient Algorithms for VM Placement in Cloud Data Centers","authors":"Hui Tian, Jiahuai Wu, Hong Shen","doi":"10.1109/PDCAT.2017.00021","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00021","url":null,"abstract":"Abstract--- The virtual machine (VM) placement problem is a major issue in optimizing resource ulitization of cloud data centers. With the rapid development of cloud computing, efficient algorithms are needed to reduce the power consumption and save energy in data centers. Many models and algorithms are designed with a objective to minimize the number of physical machines (PMs) used in a cloud data center. In this paper, we take into account the execution time of the PM, and formulat a new optimization problem of VM placement, which aims to minimize the total execution time of the PMs. We discuss the NP-hardness of the problem, and present heuristic algorithms to solve it in both offline and online scenarios. Furthermore, we conduct experiments to evaluate the performance of the proposed algorithms and the result show that our methods are able to perform better than other commonly used algorithms.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131877301","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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