Hadoop Distributed File System(HDFS) often experiences skew in data storage over time, mainly because of random data block allocation policy, datanode failure, replica reconstruction, and client activity, leading to utilization and load imbalance in the system. Although HDFS provides tools to rebalance the data in the cluster, balancer only considers balancing disk space utilization among nodes which re-allocates the data from highly utilized nodes to low utilized nodes. Thus, data access skew which is caused by piling a large amount of popular data in one node is not addressed in the default HDFS balancer. To address this issue, we present popularity-aware balancer based on node popularity score which spreads the popular data uniformly among datanodes, resulting in the balance of future access load balancing and reduction of hot spots in the cloud storage system. Simulation results demonstrate the promising benefits of proposed popularity-aware balancer by evaluating the uniform distribution of popular data across nodes without compromising the amount of data transfers and variance in disk space.
{"title":"Preventing Data Popularity Concentration in HDFS based Cloud Storage","authors":"T. Shwe, M. Aritsugi","doi":"10.1145/3368235.3368843","DOIUrl":"https://doi.org/10.1145/3368235.3368843","url":null,"abstract":"Hadoop Distributed File System(HDFS) often experiences skew in data storage over time, mainly because of random data block allocation policy, datanode failure, replica reconstruction, and client activity, leading to utilization and load imbalance in the system. Although HDFS provides tools to rebalance the data in the cluster, balancer only considers balancing disk space utilization among nodes which re-allocates the data from highly utilized nodes to low utilized nodes. Thus, data access skew which is caused by piling a large amount of popular data in one node is not addressed in the default HDFS balancer. To address this issue, we present popularity-aware balancer based on node popularity score which spreads the popular data uniformly among datanodes, resulting in the balance of future access load balancing and reduction of hot spots in the cloud storage system. Simulation results demonstrate the promising benefits of proposed popularity-aware balancer by evaluating the uniform distribution of popular data across nodes without compromising the amount of data transfers and variance in disk space.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116871472","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}
Danilo Charântola, Alexandre C. Mestre, Rafael Zane, L. Bittencourt
Cloud computing have established the utility computing paradigm as a standard for application development and execution. As heterogeneity in applications requirements become a norm, fog computing has emerged recently to introduce computing capacity layers between the edge and the cloud, creating a hierarchy of computing power that can be used as a utility to run highly heterogeneous applications. However, in order to make this layered infrastructure a reality, new resource management mechanisms are necessary. In this paper we propose a component-based scheduler that considers application requirements heterogeneity as well as the fog-cloud computing hierarchy to improve applications execution in a cloud-fog computing infrastructure. The proposed algorithm takes into account the delay-priority of applications when taking scheduling decision on the fog-cloud infrastructure. We evaluate the proposal in a simulator, and preliminary results suggest the component-based scheduling algorithm is able to reduce average delays for applications with stricter requirements.
{"title":"Component-based Scheduling for Fog Computing","authors":"Danilo Charântola, Alexandre C. Mestre, Rafael Zane, L. Bittencourt","doi":"10.1145/3368235.3368829","DOIUrl":"https://doi.org/10.1145/3368235.3368829","url":null,"abstract":"Cloud computing have established the utility computing paradigm as a standard for application development and execution. As heterogeneity in applications requirements become a norm, fog computing has emerged recently to introduce computing capacity layers between the edge and the cloud, creating a hierarchy of computing power that can be used as a utility to run highly heterogeneous applications. However, in order to make this layered infrastructure a reality, new resource management mechanisms are necessary. In this paper we propose a component-based scheduler that considers application requirements heterogeneity as well as the fog-cloud computing hierarchy to improve applications execution in a cloud-fog computing infrastructure. The proposed algorithm takes into account the delay-priority of applications when taking scheduling decision on the fog-cloud infrastructure. We evaluate the proposal in a simulator, and preliminary results suggest the component-based scheduling algorithm is able to reduce average delays for applications with stricter requirements.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125066286","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}
Growing number of Smart Applications in recent years bring a completely new landscape of cyber-attacks and exploitation scenario that have not been seen in wild before. Devices in Edge commonly have very limited computational resources and corresponding power source reducing the number of conventional cybersecurity measures available for deployment. This also puts strict requirements on how the signatures of malicious actions can be updated and actualized. It has been proved efficiency of Machine Learning models, Neural Networks in particular, in multiple tasks related to cybersecurity due to the high-abstract precise models and training from historical data. However, when it comes to the devices in Edge, it is clear that the extensive training of the model is not possible, while testing of new unseen data can be successfully done. In addition to the conventional understanding of off-line and on-line model training, this contribution looks into how the Machine Learning can be successfully deployed on IoT while putting unnecessary computations off-chip through parameters transfer over MQTT network, reducing computational footprint on micro-controllers. We believe that proposed approach will be beneficial for many applications in resource-constrained environment.
{"title":"MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices","authors":"Andrii Shalaginov, Oleksandr Semeniuta, M. Alazab","doi":"10.1145/3368235.3368876","DOIUrl":"https://doi.org/10.1145/3368235.3368876","url":null,"abstract":"Growing number of Smart Applications in recent years bring a completely new landscape of cyber-attacks and exploitation scenario that have not been seen in wild before. Devices in Edge commonly have very limited computational resources and corresponding power source reducing the number of conventional cybersecurity measures available for deployment. This also puts strict requirements on how the signatures of malicious actions can be updated and actualized. It has been proved efficiency of Machine Learning models, Neural Networks in particular, in multiple tasks related to cybersecurity due to the high-abstract precise models and training from historical data. However, when it comes to the devices in Edge, it is clear that the extensive training of the model is not possible, while testing of new unseen data can be successfully done. In addition to the conventional understanding of off-line and on-line model training, this contribution looks into how the Machine Learning can be successfully deployed on IoT while putting unnecessary computations off-chip through parameters transfer over MQTT network, reducing computational footprint on micro-controllers. We believe that proposed approach will be beneficial for many applications in resource-constrained environment.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068129","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}
Recommender Systems (RS) are web-based intelligent decision-making tools, which narrow down the user's choices based on their defined and undefined behavior. An evolutionary algorithm, namely, Genetic Algorithm (GA) has shown significant results in the field of RS in the past. Despite its huge success, it suffers from the limitation of premature convergence. Memetic Algorithm (MA), also called parallel or hybrid GA is one such technique which introduces local search to reduce the likelihood of premature convergence. This work presents a novel MA-based Similarity Metric (MASM) for RS, leveraging the collaborative behavior of memes. We use publicly available Movielens dataset (100K ratings) to conduct experiments. Results demonstrate that the proposed metric outperforms the conventional GA-based Similarity Metric (GASM). The precision of RS using MASM is improved by 28% over RS using GASM, resulting in improved predictive recommendation accuracy.
{"title":"Memetic Algorithm based Similarity Metric for Recommender System","authors":"Saumya Bansal, Niyati Baliyan","doi":"10.1145/3368235.3369372","DOIUrl":"https://doi.org/10.1145/3368235.3369372","url":null,"abstract":"Recommender Systems (RS) are web-based intelligent decision-making tools, which narrow down the user's choices based on their defined and undefined behavior. An evolutionary algorithm, namely, Genetic Algorithm (GA) has shown significant results in the field of RS in the past. Despite its huge success, it suffers from the limitation of premature convergence. Memetic Algorithm (MA), also called parallel or hybrid GA is one such technique which introduces local search to reduce the likelihood of premature convergence. This work presents a novel MA-based Similarity Metric (MASM) for RS, leveraging the collaborative behavior of memes. We use publicly available Movielens dataset (100K ratings) to conduct experiments. Results demonstrate that the proposed metric outperforms the conventional GA-based Similarity Metric (GASM). The precision of RS using MASM is improved by 28% over RS using GASM, resulting in improved predictive recommendation accuracy.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358560","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}
P. Harsh, Juan Francisco Ribera Laszkowski, A. Edmonds, Tran Quang Thanh, Michael Pauls, Radoslav Vlaskovski, Orlando Avila-García, Enric Pages, Francisco Gortázar-Bellas, Micael Gallego-Carrillo
Testing large-scale distributed systems (also known as testing in the large) is a challenge that spreads across different technical domains and areas of expertise. Current methods and tools provide some minimal guarantees in relation to the correctness of their functional properties and have serious limitations when evaluating their extra-functional properties in realistic conditions, such as scalability, availability and performance efficiency. Cloud Testing and more specifically "testing in the cloud'' has arisen to tackle those challenges. In this new paradigm, cloud-based environment and infrastructure are used to run realistic end-to-end and/or system-level tests, collect test data and analyse them. In this paper we present a set of cloud-native services to take from the tester the responsibility of managing the resources and complementary services required to simulate realistic operational conditions and production environments. Specifically, they provide cloud testing capabilities such as logs and measurements collection from both testing jobs and system under test; test data analytics and visualization; provisioning and operation of additional services and processes to replicate realistic production ecosystems; support to scalability and diversity of underlying testing infrastructure; and replication of the operational conditions of the software under test through its instrumentation. We present the architecture of the cloud testing solution and the detailed design of each of the services; we also evaluate their relative contribution to satisfy different needs in the context of test execution.
{"title":"Cloud Enablers For Testing Large-Scale Distributed Applications","authors":"P. Harsh, Juan Francisco Ribera Laszkowski, A. Edmonds, Tran Quang Thanh, Michael Pauls, Radoslav Vlaskovski, Orlando Avila-García, Enric Pages, Francisco Gortázar-Bellas, Micael Gallego-Carrillo","doi":"10.1145/3368235.3368838","DOIUrl":"https://doi.org/10.1145/3368235.3368838","url":null,"abstract":"Testing large-scale distributed systems (also known as testing in the large) is a challenge that spreads across different technical domains and areas of expertise. Current methods and tools provide some minimal guarantees in relation to the correctness of their functional properties and have serious limitations when evaluating their extra-functional properties in realistic conditions, such as scalability, availability and performance efficiency. Cloud Testing and more specifically \"testing in the cloud'' has arisen to tackle those challenges. In this new paradigm, cloud-based environment and infrastructure are used to run realistic end-to-end and/or system-level tests, collect test data and analyse them. In this paper we present a set of cloud-native services to take from the tester the responsibility of managing the resources and complementary services required to simulate realistic operational conditions and production environments. Specifically, they provide cloud testing capabilities such as logs and measurements collection from both testing jobs and system under test; test data analytics and visualization; provisioning and operation of additional services and processes to replicate realistic production ecosystems; support to scalability and diversity of underlying testing infrastructure; and replication of the operational conditions of the software under test through its instrumentation. We present the architecture of the cloud testing solution and the detailed design of each of the services; we also evaluate their relative contribution to satisfy different needs in the context of test execution.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131152376","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}
The call for tutorials at UCC'19 and BDCAT'19 attracted submissions from Australia and Europe. The tutorial chairs reviewed and accepted two revised tutorials, and decided to award a third spot on a FCFS basis to ensure that conference attendees have access to learning resources across all conference topics.
{"title":"UCC/BDCAT Tutorial Chairs' Welcome","authors":"Yan Tang, Tamara Matthews","doi":"10.1145/3368235.3368881","DOIUrl":"https://doi.org/10.1145/3368235.3368881","url":null,"abstract":"The call for tutorials at UCC'19 and BDCAT'19 attracted submissions from Australia and Europe. The tutorial chairs reviewed and accepted two revised tutorials, and decided to award a third spot on a FCFS basis to ensure that conference attendees have access to learning resources across all conference topics.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129132483","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}
Aji John, Kristiina Ausmees, Kathleen Muenzen, Catherine Kuhn, A. Tan
Scientific and commercial applications are increasingly being executed in the cloud, but the difficulties associated with cluster management render on-demand resources inaccessible or inefficient to many users. Recently, the serverless execution model, in which the provisioning of resources is abstracted from the user, has gained prominence as an alternative to traditional cyberinfrastructure solutions. With its inherent elasticity, the serverless paradigm constitutes a promising computational model for scientific workflows, allowing domain specialists to develop and deploy workflows that are subject to varying workloads and intermittent usage without the overhead of infrastructure maintenance. We present the Serverless Workflow Enablement and Execution Platform (SWEEP), a cloud-agnostic workflow management system with a purely serverless execution model that allows users to define, run and monitor generic cloud-native workflows. We demonstrate the use of SWEEP on workflows from two disparate scientific domains and present an evaluation of performance and scaling.
{"title":"SWEEP: Accelerating Scientific Research Through Scalable Serverless Workflows","authors":"Aji John, Kristiina Ausmees, Kathleen Muenzen, Catherine Kuhn, A. Tan","doi":"10.1145/3368235.3368839","DOIUrl":"https://doi.org/10.1145/3368235.3368839","url":null,"abstract":"Scientific and commercial applications are increasingly being executed in the cloud, but the difficulties associated with cluster management render on-demand resources inaccessible or inefficient to many users. Recently, the serverless execution model, in which the provisioning of resources is abstracted from the user, has gained prominence as an alternative to traditional cyberinfrastructure solutions. With its inherent elasticity, the serverless paradigm constitutes a promising computational model for scientific workflows, allowing domain specialists to develop and deploy workflows that are subject to varying workloads and intermittent usage without the overhead of infrastructure maintenance. We present the Serverless Workflow Enablement and Execution Platform (SWEEP), a cloud-agnostic workflow management system with a purely serverless execution model that allows users to define, run and monitor generic cloud-native workflows. We demonstrate the use of SWEEP on workflows from two disparate scientific domains and present an evaluation of performance and scaling.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117278912","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}
Josef Spillner, Manar AbuTalib, Q. Nasir, Farhad Khalilnia
It is our great pleasure to welcome you to the first International Workshop on Cloud, IoT and Fog Security - CIFS 2019 collocated with the 12th IEEE/ACM International Conference on Utility and Cloud Computing - UCC 2019. Welcome to Auckland! The processing of sensitive information is a cross-cutting topic unimpressed by imaginary system boundaries. In many scenarios, sensors or actors are connected to on-site compute units and fog systems which themselves are connected to clouds. The transmission, processing and storage of information needs to be secured across the entire chain or network, using diverse mechanisms often outside the control of the application developer. This workshop aims to discuss recent advances around holistic security aspects involving availability, integrity, confidentiality, non-repudiability and other guaranteeable properties. With six peer-reviewed research papers and one impulse talk, this workshop combines the most pressing topics on the intersection between distributed systems and (cyber-)security aspects. Two papers investigate the use of blockchains: «Intelligent Price Alert System for Blockchain-based Digital Assets» and «Blockchain as a Trusted Component in Cloud SLA Verification». Intrusion detection and avoidance is the suject of three papers: «MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices», «An Algorithm to Prevent Unauthorised Data Modification using Collaborative Nodes» and «Techniques for Mutual Auditability in a Cloud Environment». Finally, algorithmic aspects of data encoding are covered in «Concurrent Failure Recovery for MSR Regenerating Code via Product Matrix Construction». These papers represent authors from six countries over four continents, and thus a significant glimpse into emerging research around the world. With an impulse talk «Novel Applications of Stealth Computing», the workshop also conveys recent research efforts on holistic combinations of data encoding, dispersal and processing for industrially relevant systems combining connected devices and continuum services from fogs to clouds.
{"title":"CIFS'19 Workshop Chairs' Welcome & Organization","authors":"Josef Spillner, Manar AbuTalib, Q. Nasir, Farhad Khalilnia","doi":"10.1145/3368235.3368885","DOIUrl":"https://doi.org/10.1145/3368235.3368885","url":null,"abstract":"It is our great pleasure to welcome you to the first International Workshop on Cloud, IoT and Fog Security - CIFS 2019 collocated with the 12th IEEE/ACM International Conference on Utility and Cloud Computing - UCC 2019. Welcome to Auckland! The processing of sensitive information is a cross-cutting topic unimpressed by imaginary system boundaries. In many scenarios, sensors or actors are connected to on-site compute units and fog systems which themselves are connected to clouds. The transmission, processing and storage of information needs to be secured across the entire chain or network, using diverse mechanisms often outside the control of the application developer. This workshop aims to discuss recent advances around holistic security aspects involving availability, integrity, confidentiality, non-repudiability and other guaranteeable properties. With six peer-reviewed research papers and one impulse talk, this workshop combines the most pressing topics on the intersection between distributed systems and (cyber-)security aspects. Two papers investigate the use of blockchains: «Intelligent Price Alert System for Blockchain-based Digital Assets» and «Blockchain as a Trusted Component in Cloud SLA Verification». Intrusion detection and avoidance is the suject of three papers: «MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices», «An Algorithm to Prevent Unauthorised Data Modification using Collaborative Nodes» and «Techniques for Mutual Auditability in a Cloud Environment». Finally, algorithmic aspects of data encoding are covered in «Concurrent Failure Recovery for MSR Regenerating Code via Product Matrix Construction». These papers represent authors from six countries over four continents, and thus a significant glimpse into emerging research around the world. With an impulse talk «Novel Applications of Stealth Computing», the workshop also conveys recent research efforts on holistic combinations of data encoding, dispersal and processing for industrially relevant systems combining connected devices and continuum services from fogs to clouds.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124819352","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}
Cloud computing is changing from a buzz word to a common technology nowadays. Though there is clarification for cloud computing for three types/layers: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), generally people talk about IaaS and PaaS when they talking about cloud computing. There are already several giant players, like AWS, Azure and GoogleCloud, in the market and it's a 100+ billions USD market[1],but it's a competitive market and there are new players coming in. And on the other hand, people can see big demand for private cloud as well. In this talk, I'd like to share our journey about building a public cloud with OpenStack[2]. Our journey started since 2014 and the idea incubated even earlier. I will generally cover how we design and implement our cloud from bare metal, to VM, then container/Kubernetes, and the road map targeting to serverless, mainly focus on the current stage, about building a high quality managed Kubernetes service. Recently having gone through the experience of building, implementing and running a Kubernetes platform service in our public cloud, Catalyst Cloud has some interesting experiences and war stories to share about the journey.
{"title":"The Journey of Cloud Computing with Open Source","authors":"Feilong Wang","doi":"10.1145/3368235.3369378","DOIUrl":"https://doi.org/10.1145/3368235.3369378","url":null,"abstract":"Cloud computing is changing from a buzz word to a common technology nowadays. Though there is clarification for cloud computing for three types/layers: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), generally people talk about IaaS and PaaS when they talking about cloud computing. There are already several giant players, like AWS, Azure and GoogleCloud, in the market and it's a 100+ billions USD market[1],but it's a competitive market and there are new players coming in. And on the other hand, people can see big demand for private cloud as well. In this talk, I'd like to share our journey about building a public cloud with OpenStack[2]. Our journey started since 2014 and the idea incubated even earlier. I will generally cover how we design and implement our cloud from bare metal, to VM, then container/Kubernetes, and the road map targeting to serverless, mainly focus on the current stage, about building a high quality managed Kubernetes service. Recently having gone through the experience of building, implementing and running a Kubernetes platform service in our public cloud, Catalyst Cloud has some interesting experiences and war stories to share about the journey.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128815644","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}
L. Bittencourt, B. Schulze, Rafael Tolosana-Calasanz
Welcome to the 8th International Workshop on Cloud and Edge Computing, and Applications Management - CloudAM2019, which will be held in conjunction with the 12th IEEE/ACM Utility and Cloud Computing Conference (UCC) in Auckland, New Zealand, from 2-5 December 2019. CloudAM is a successful series of workshops that bring together practitioners and researchers on current research advances on cloud computing, virtualization technologies and real applications. As it is anticipated that this interest will keep expanding with the emergence of edge computing infrastructures, this 8th edition of CloudAM will also cover the topics of edge and fog computing. Cloud and edge infrastructures can work together to fulfill requirements from a variety of applications, composing the so-called Cloud Continuum to the edge. Furthermore, clouds must provide appropriate levels of performance to large groups of diverse users, and those clouds are accessed through virtualized wide area networks, where edge/fog devices can act as a first layer of computing capacity closer to the user. Management systems are essential for that and thereby for the future success of the fog-cloud hierarchy. New systems, methods, and approaches for cloud and edge computing, virtualization, and applications management are to be discussed at this workshop. In this edition of CloudAM, we received six submissions and we could only accept three of them. On the other hand, another the CloudAM program also includes nine high-quality papers that were submitted to the UCC main track and directed for presentation in the workshop. All papers were reviewed and evaluated based on relevance, quality, and novelty. Overall, a number of 12 contributions, covering a broad number of topics, will be presented and discussed during a one-day workshop.
{"title":"12th IEEE/ACM International UCC/BDCAT'19 CloudAM'19 Workshop Chairs' Welcome Message & Organization","authors":"L. Bittencourt, B. Schulze, Rafael Tolosana-Calasanz","doi":"10.1145/3368235.3368884","DOIUrl":"https://doi.org/10.1145/3368235.3368884","url":null,"abstract":"Welcome to the 8th International Workshop on Cloud and Edge Computing, and Applications Management - CloudAM2019, which will be held in conjunction with the 12th IEEE/ACM Utility and Cloud Computing Conference (UCC) in Auckland, New Zealand, from 2-5 December 2019. CloudAM is a successful series of workshops that bring together practitioners and researchers on current research advances on cloud computing, virtualization technologies and real applications. As it is anticipated that this interest will keep expanding with the emergence of edge computing infrastructures, this 8th edition of CloudAM will also cover the topics of edge and fog computing. Cloud and edge infrastructures can work together to fulfill requirements from a variety of applications, composing the so-called Cloud Continuum to the edge. Furthermore, clouds must provide appropriate levels of performance to large groups of diverse users, and those clouds are accessed through virtualized wide area networks, where edge/fog devices can act as a first layer of computing capacity closer to the user. Management systems are essential for that and thereby for the future success of the fog-cloud hierarchy. New systems, methods, and approaches for cloud and edge computing, virtualization, and applications management are to be discussed at this workshop. In this edition of CloudAM, we received six submissions and we could only accept three of them. On the other hand, another the CloudAM program also includes nine high-quality papers that were submitted to the UCC main track and directed for presentation in the workshop. All papers were reviewed and evaluated based on relevance, quality, and novelty. Overall, a number of 12 contributions, covering a broad number of topics, will be presented and discussed during a one-day workshop.","PeriodicalId":166357,"journal":{"name":"Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing Companion","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126195479","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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