Mobile clients that consume and produce data are abundant in fog environments and low latency access to this data can only be achieved by storing it in their close physical proximity. To adapt data replication in fog data stores in an efficient manner and make client data available at the fog node that is closest to the client, the systems need to predict both client movement and pauses in data consumption. In this paper, we present variations of Markov model algorithms that can run on clients to increase the data availability while minimizing excess data. In a simulation, we find the availability of data at the closest node can be improved by 35% without incurring the storage and communication overheads of global replication.
{"title":"Predictive replica placement for mobile users in distributed fog data stores with client-side markov models","authors":"M. Bellmann, Tobias Pfandzelter, David Bermbach","doi":"10.1145/3492323.3495595","DOIUrl":"https://doi.org/10.1145/3492323.3495595","url":null,"abstract":"Mobile clients that consume and produce data are abundant in fog environments and low latency access to this data can only be achieved by storing it in their close physical proximity. To adapt data replication in fog data stores in an efficient manner and make client data available at the fog node that is closest to the client, the systems need to predict both client movement and pauses in data consumption. In this paper, we present variations of Markov model algorithms that can run on clients to increase the data availability while minimizing excess data. In a simulation, we find the availability of data at the closest node can be improved by 35% without incurring the storage and communication overheads of global replication.","PeriodicalId":440884,"journal":{"name":"Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122664430","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}
S. U. K. Bukhari, Syed Azeemuddin, S. S. Khalid, S. Shah
Background: Breast cancer is one of the most prevalent cause of morbidity and mortality in women all over the world. Histopathological diagnosis is a vital component in the management of breast cancer. The application of artificial intelligence is yielding promising results for the better patientcare. Aim: The main aim of the present research project is to explore the potential of spatial supervised technique to develop scale invariant system for the histological diagnosis of breast cancer. Materials and Methods: The anonymized images of hematoxylin and eosin stained section of the dataset, which has been acquired from the website. The slides were taken at different zoom (magnification) levels. Spatial supervised learning has been employed to make a scale invariant system. We used 400x and 40x to generate the results. For the 400x, we trained our network on a dataset of 200x, 100x, and 40x images. The datasets were split into training and validation sets. The training set contained 80% digital slides of the respected dataset, and the validation set contained 20% digital slides of the respected dataset. The final result was generated by splitting the dataset of 400x into the training and test dataset. The training set contained 50% digital slides, and the test set also contained 50% digital slides. This unusual split is done to show how good spatial supervised learning works. Similarly, for 40x, we trained our networks on a dataset of 400x, 200x, and 100x. The same steps were followed to obtain the 40x results. Results: The result analysis revealed that the ResNet 18 with spatial supervised learning on dataset of 40x yielded the F-1 score of 1.0, while ResNet 18 with supervised learning only, on dataset of 40x yielded F-1 score of 0.9823. ResNet 18 with spatial supervised learning on dataset of 400x revealed F-1 score of 0.9957, and ResNet 18 with supervised learning only, on dataset of 400x showed the F-1 score of 0.9591. For supervised learning dataset is spited into training (80%) and testing (20% of dataset). Conclusion: The analysis of digitized pathology images with the application of convolutional neural network Resnet - 18 architecture with spatial supervised learning revealed excellent results, which is demonstrated by a very high F-1 score of 1.0. The development of scale invariant system with application of spatial supervised technique solved the problem of images with variable magnifications. The finding would further pave the pathway for application of deep learning for the histological diagnosis of pathological lesions.
{"title":"The histological diagnosis of breast cancer by employing scale invariant ResNet 18 with spatial supervised technique","authors":"S. U. K. Bukhari, Syed Azeemuddin, S. S. Khalid, S. Shah","doi":"10.1145/3492323.3495596","DOIUrl":"https://doi.org/10.1145/3492323.3495596","url":null,"abstract":"Background: Breast cancer is one of the most prevalent cause of morbidity and mortality in women all over the world. Histopathological diagnosis is a vital component in the management of breast cancer. The application of artificial intelligence is yielding promising results for the better patientcare. Aim: The main aim of the present research project is to explore the potential of spatial supervised technique to develop scale invariant system for the histological diagnosis of breast cancer. Materials and Methods: The anonymized images of hematoxylin and eosin stained section of the dataset, which has been acquired from the website. The slides were taken at different zoom (magnification) levels. Spatial supervised learning has been employed to make a scale invariant system. We used 400x and 40x to generate the results. For the 400x, we trained our network on a dataset of 200x, 100x, and 40x images. The datasets were split into training and validation sets. The training set contained 80% digital slides of the respected dataset, and the validation set contained 20% digital slides of the respected dataset. The final result was generated by splitting the dataset of 400x into the training and test dataset. The training set contained 50% digital slides, and the test set also contained 50% digital slides. This unusual split is done to show how good spatial supervised learning works. Similarly, for 40x, we trained our networks on a dataset of 400x, 200x, and 100x. The same steps were followed to obtain the 40x results. Results: The result analysis revealed that the ResNet 18 with spatial supervised learning on dataset of 40x yielded the F-1 score of 1.0, while ResNet 18 with supervised learning only, on dataset of 40x yielded F-1 score of 0.9823. ResNet 18 with spatial supervised learning on dataset of 400x revealed F-1 score of 0.9957, and ResNet 18 with supervised learning only, on dataset of 400x showed the F-1 score of 0.9591. For supervised learning dataset is spited into training (80%) and testing (20% of dataset). Conclusion: The analysis of digitized pathology images with the application of convolutional neural network Resnet - 18 architecture with spatial supervised learning revealed excellent results, which is demonstrated by a very high F-1 score of 1.0. The development of scale invariant system with application of spatial supervised technique solved the problem of images with variable magnifications. The finding would further pave the pathway for application of deep learning for the histological diagnosis of pathological lesions.","PeriodicalId":440884,"journal":{"name":"Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121272958","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}
Pradipta Ghosh, Quynh Nguyen, Pranav Sakulkar, Aleksandra Knezevic, Jason A. Tran, Jiatong Wang, Zhifeng Lin, B. Krishnamachari, M. Annavaram, A. Avestimehr
Modern latency-sensitive applications such as real-time multi-camera video analytics require networked computing to meet the time constraints. We present Jupiter, an open-source networked computing system that inputs a Directed Acyclic Graph (DAG)-based computational task graph to efficiently distribute the tasks among a set of networked compute nodes and orchestrates the execution thereafter. This Kubernetes container-orchestration-based system includes a range of profilers: network profilers, resource profilers, and execution time profilers; to support both centralized and decentralized scheduling algorithms. While centralized scheduling algorithms with global knowledge have been popular among the grid/cloud computing community, we argue that a distributed scheduling approach is better suited for networked computing due to lower communication and computation overhead in the face of network dynamics. We propose a new class of distributed scheduling algorithms called WAVE and show that despite using more localized knowledge, the WAVE algorithm can match the performance of a well-known centralized scheduling algorithm called Heterogeneous Earliest Finish Time (HEFT). To this, we present a set of real-world experiments on two separate testbeds: (1) a worldwide network of 90 cloud computers across eight cities and (2) a cluster of 30 Raspberry pi nodes.
{"title":"Jupiter: a networked computing architecture","authors":"Pradipta Ghosh, Quynh Nguyen, Pranav Sakulkar, Aleksandra Knezevic, Jason A. Tran, Jiatong Wang, Zhifeng Lin, B. Krishnamachari, M. Annavaram, A. Avestimehr","doi":"10.1145/3492323.3495630","DOIUrl":"https://doi.org/10.1145/3492323.3495630","url":null,"abstract":"Modern latency-sensitive applications such as real-time multi-camera video analytics require networked computing to meet the time constraints. We present Jupiter, an open-source networked computing system that inputs a Directed Acyclic Graph (DAG)-based computational task graph to efficiently distribute the tasks among a set of networked compute nodes and orchestrates the execution thereafter. This Kubernetes container-orchestration-based system includes a range of profilers: network profilers, resource profilers, and execution time profilers; to support both centralized and decentralized scheduling algorithms. While centralized scheduling algorithms with global knowledge have been popular among the grid/cloud computing community, we argue that a distributed scheduling approach is better suited for networked computing due to lower communication and computation overhead in the face of network dynamics. We propose a new class of distributed scheduling algorithms called WAVE and show that despite using more localized knowledge, the WAVE algorithm can match the performance of a well-known centralized scheduling algorithm called Heterogeneous Earliest Finish Time (HEFT). To this, we present a set of real-world experiments on two separate testbeds: (1) a worldwide network of 90 cloud computers across eight cities and (2) a cluster of 30 Raspberry pi nodes.","PeriodicalId":440884,"journal":{"name":"Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124733622","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}
{"title":"Session details: Doctoral symposium","authors":"H. Sundaram, K. Aizawa","doi":"10.1145/3256787","DOIUrl":"https://doi.org/10.1145/3256787","url":null,"abstract":"","PeriodicalId":440884,"journal":{"name":"Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122719042","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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