Nitesh Emmadi, Praveen Gauravaram, Harika Narumanchi, H. Syed
In this paper, we show implementation results of various algorithms that sort data encrypted with Fully Ho-momorphic Encryption scheme based on Integers. We analyze the complexities of sorting algorithms over encrypted data by considering Bubble Sort, Insertion Sort, Bitonic Sort and Odd-Even Merge sort. Our complexity analysis together with implementation results show that Odd-Even Merge Sort has better performance than the other sorting techniques. We observe that complexity of sorting in homomorphic domain will always have worst case complexity independent of the nature of input. In addition, we show that combining different sorting algorithms to sort encrypted data does not give any performance gain when compared to the application of sorting algorithms individually.
{"title":"Updates on Sorting of Fully Homomorphic Encrypted Data","authors":"Nitesh Emmadi, Praveen Gauravaram, Harika Narumanchi, H. Syed","doi":"10.1109/ICCCRI.2015.28","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.28","url":null,"abstract":"In this paper, we show implementation results of various algorithms that sort data encrypted with Fully Ho-momorphic Encryption scheme based on Integers. We analyze the complexities of sorting algorithms over encrypted data by considering Bubble Sort, Insertion Sort, Bitonic Sort and Odd-Even Merge sort. Our complexity analysis together with implementation results show that Odd-Even Merge Sort has better performance than the other sorting techniques. We observe that complexity of sorting in homomorphic domain will always have worst case complexity independent of the nature of input. In addition, we show that combining different sorting algorithms to sort encrypted data does not give any performance gain when compared to the application of sorting algorithms individually.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041374","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}
Quanqing Xu, Wilson Yonghong Wang, K. L. Yong, Khin Mi Mi Aung
In order to meet the needs of increasing users and improve user-perceived latency, online services distribute and replicate data across geographically diverse data centers and direct user requests to the closest or least loaded server. Distributed Hash Table (DHT) is a structured overlay network that is widely utilized in geo-replicated storage systems, e.g., Dynamo. Some geo-replicated storage systems may need to locate an item with only keywords. In this paper, we present Jupiter, a DHT-based middleware system for building geo-replicated storage systems. Jupiter provides robust and efficient routing mechanisms under geo-replicated environments. The key innovation in Jupiter is the integration of two concepts: robustness and efficiency. We have prototyped Jupiter, deployed it on a network of Linux machines, and used it to develop several distributed applications. We confirm the practicality, effectiveness and efficiency of Jupiter by conducting an extensive performance benchmark measured by efficiency, robustness and consistency.
{"title":"Building a Robust and Efficient Middleware for Geo-replicated Storage Systems","authors":"Quanqing Xu, Wilson Yonghong Wang, K. L. Yong, Khin Mi Mi Aung","doi":"10.1109/ICCCRI.2015.19","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.19","url":null,"abstract":"In order to meet the needs of increasing users and improve user-perceived latency, online services distribute and replicate data across geographically diverse data centers and direct user requests to the closest or least loaded server. Distributed Hash Table (DHT) is a structured overlay network that is widely utilized in geo-replicated storage systems, e.g., Dynamo. Some geo-replicated storage systems may need to locate an item with only keywords. In this paper, we present Jupiter, a DHT-based middleware system for building geo-replicated storage systems. Jupiter provides robust and efficient routing mechanisms under geo-replicated environments. The key innovation in Jupiter is the integration of two concepts: robustness and efficiency. We have prototyped Jupiter, deployed it on a network of Linux machines, and used it to develop several distributed applications. We confirm the practicality, effectiveness and efficiency of Jupiter by conducting an extensive performance benchmark measured by efficiency, robustness and consistency.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122170524","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}
Y. W. Foo, C. Goh, Hong Chee Lim, Zhi-hui Zhan, Yun Li
The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising.
{"title":"Evolutionary Neural Network Based Energy Consumption Forecast for Cloud Computing","authors":"Y. W. Foo, C. Goh, Hong Chee Lim, Zhi-hui Zhan, Yun Li","doi":"10.1109/ICCCRI.2015.17","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.17","url":null,"abstract":"The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114085009","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. Date, H. Abe, Khureltulga Dashdavaa, Keichi Takahashi, Y. Kido, Yasuhiro Watashiba, Pongsakorn U-chupala, Koheix Ichikawa, Hiroaki Yamanaka, Eiji Kawai, S. Shimojo
High performance computing is required for Big Science application because the proliferation and huge amount of scientific data that needs to be analyzed is a serious problem. Traditionally, network resources were generally assumed as a static resource users cannot control on demand. By integrating network programmability to every stage of a scientific workflow, this study explores a next-generation high performance computing infrastructure where both computational and network resources are flexibly sliced and efficiently leveraged based on the resource requirements of the scientific applications. Technically, Software Defined Networking has been adopted as a key technology for this purpose. In this paper the concept and goals of a next-generation high performance computing infrastructure is introduced and the current status of our research is discussed.
{"title":"An Empirical Study of SDN-accelerated HPC Infrastructure for Scientific Research","authors":"S. Date, H. Abe, Khureltulga Dashdavaa, Keichi Takahashi, Y. Kido, Yasuhiro Watashiba, Pongsakorn U-chupala, Koheix Ichikawa, Hiroaki Yamanaka, Eiji Kawai, S. Shimojo","doi":"10.1109/ICCCRI.2015.13","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.13","url":null,"abstract":"High performance computing is required for Big Science application because the proliferation and huge amount of scientific data that needs to be analyzed is a serious problem. Traditionally, network resources were generally assumed as a static resource users cannot control on demand. By integrating network programmability to every stage of a scientific workflow, this study explores a next-generation high performance computing infrastructure where both computational and network resources are flexibly sliced and efficiently leveraged based on the resource requirements of the scientific applications. Technically, Software Defined Networking has been adopted as a key technology for this purpose. In this paper the concept and goals of a next-generation high performance computing infrastructure is introduced and the current status of our research is discussed.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116659652","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}
Mohd Bazli Ab Karim, Luke Jing Yuan, Ming-Tat Wong, H. Ong
Distributed object storage system has advantages in scalability and accessibility over standard block and file storage. However, the object approach lacks maturity when it comes to real-time systems such as transactional databases that are constantly being written. Specifically, the object approach cannot rival block-based systems for the dynamic read and write speeds required by disk resource-intensive applications such as databases. As more organizations migrate to cloud hosting solution, the need to address rapid application read and write will become top of the requirements list. This paper proposes a method to increase I/O performance of database appliances running in the cloud environment with distributed object storage as the underlying data stores. The proposed method involves separating the distributed storage's journal and data partitions to different hard drives and also separating a few database application directories to multiple RBD images from different storage pools in order to speed up the I/O operations. Experiments with SATA, SAS, and SSD type-drives with Ceph distributed storage system have been conducted based on proposed method and the results show significant performance compared to local drives and default distributed storage setup.
{"title":"Improving Performance of Database Appliances on Distributed Object Storage","authors":"Mohd Bazli Ab Karim, Luke Jing Yuan, Ming-Tat Wong, H. Ong","doi":"10.1109/ICCCRI.2015.21","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.21","url":null,"abstract":"Distributed object storage system has advantages in scalability and accessibility over standard block and file storage. However, the object approach lacks maturity when it comes to real-time systems such as transactional databases that are constantly being written. Specifically, the object approach cannot rival block-based systems for the dynamic read and write speeds required by disk resource-intensive applications such as databases. As more organizations migrate to cloud hosting solution, the need to address rapid application read and write will become top of the requirements list. This paper proposes a method to increase I/O performance of database appliances running in the cloud environment with distributed object storage as the underlying data stores. The proposed method involves separating the distributed storage's journal and data partitions to different hard drives and also separating a few database application directories to multiple RBD images from different storage pools in order to speed up the I/O operations. Experiments with SATA, SAS, and SSD type-drives with Ceph distributed storage system have been conducted based on proposed method and the results show significant performance compared to local drives and default distributed storage setup.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"373 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121665274","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}
Security has always been highlighted as a key impediment to cloud adoption in many cloud conferences and surveys. However, tolerance to security risks varies from user to user. This leads to the development of the multi-tiered cloud security (MTCS) standards to meet different security needs of cloud users. Building on the tiered structure of MTCS, a unique framework of layered approach to certification of cloud services, in particular, certifying Software-as-a-Service hosted on MTCS-certified infrastructure service providers is described in details in this paper to shed lights on the implementation considerations.
{"title":"Certifying SaaS in the MTCS Framework","authors":"Yao-Sing Tao, Hing-Yan Lee","doi":"10.1109/ICCCRI.2015.12","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.12","url":null,"abstract":"Security has always been highlighted as a key impediment to cloud adoption in many cloud conferences and surveys. However, tolerance to security risks varies from user to user. This leads to the development of the multi-tiered cloud security (MTCS) standards to meet different security needs of cloud users. Building on the tiered structure of MTCS, a unique framework of layered approach to certification of cloud services, in particular, certifying Software-as-a-Service hosted on MTCS-certified infrastructure service providers is described in details in this paper to shed lights on the implementation considerations.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129079079","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}
While governments are transitioning to the cloud to leverage efficiency, transparency and accessibility advantages, public opinion - the backbone of democracy - is being left behind. Statistics show that traditional paper voting is failing to reach the technological-savvy generation, with voter turnout decreasing every election for many first-world countries. Remote electronic voting is a possible solution facilitator to this problem, but it still faces several security, privacy and accountability concerns. This paper introduces a practical application of partially homomorphic encryption to help address these challenges. We describe a cloud-based mobile electronic voting scheme, evaluating its security against a list of requirements, and benchmarking performance on the cloud and mobile devices. In order to protect voter privacy, we propose moving away from a public bulletin board so that no individual cipher votes are saved, while still allowing vote verification. As the majority of the security threats faced by electronic voting are from the underlying system, we also introduce the novel concept of using a dedicated hardware server for homomorphic tallying and decryption.
{"title":"Secure Voting in the Cloud Using Homomorphic Encryption and Mobile Agents","authors":"M. Will, B. Nicholson, Marc Tiehuis, R. Ko","doi":"10.1109/ICCCRI.2015.30","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.30","url":null,"abstract":"While governments are transitioning to the cloud to leverage efficiency, transparency and accessibility advantages, public opinion - the backbone of democracy - is being left behind. Statistics show that traditional paper voting is failing to reach the technological-savvy generation, with voter turnout decreasing every election for many first-world countries. Remote electronic voting is a possible solution facilitator to this problem, but it still faces several security, privacy and accountability concerns. This paper introduces a practical application of partially homomorphic encryption to help address these challenges. We describe a cloud-based mobile electronic voting scheme, evaluating its security against a list of requirements, and benchmarking performance on the cloud and mobile devices. In order to protect voter privacy, we propose moving away from a public bulletin board so that no individual cipher votes are saved, while still allowing vote verification. As the majority of the security threats faced by electronic voting are from the underlying system, we also introduce the novel concept of using a dedicated hardware server for homomorphic tallying and decryption.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"743 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116093110","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 Scientific Platform for the Cloud (SPC) presents a framework to support the rapid design and deployment of scientific applications (apps) in the cloud. It provides common infrastructure for running typical IXP (Input-execute-Plot) style apps, including: a web interface, post-processing and plotting capabilities, job scheduling, real-time monitoring of running jobs, and case manager. In this paper we (1) describe the design of the system architecture, (2) evaluate its applicability to a scientific workload, and (3) present a number of case studies which represent a wide variety of scientific applications including Population Genetics, Geophysics, Turbulence Physics, DNA analysis, and Big Data.
{"title":"An Integrated Cloud Platform for Rapid Interface Generation, Job Scheduling, Monitoring, Plotting, and Case Management of Scientific Applications","authors":"W. Brewer, W. Scott, John Sanford","doi":"10.1109/ICCCRI.2015.24","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.24","url":null,"abstract":"The Scientific Platform for the Cloud (SPC) presents a framework to support the rapid design and deployment of scientific applications (apps) in the cloud. It provides common infrastructure for running typical IXP (Input-execute-Plot) style apps, including: a web interface, post-processing and plotting capabilities, job scheduling, real-time monitoring of running jobs, and case manager. In this paper we (1) describe the design of the system architecture, (2) evaluate its applicability to a scientific workload, and (3) present a number of case studies which represent a wide variety of scientific applications including Population Genetics, Geophysics, Turbulence Physics, DNA analysis, and Big Data.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131043010","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}
Data center networks require densely interconnected topologies to provide high bandwidth for various cloud computing services. It is required to fully utilize the bandwidth resource in such networks with varying traffic patterns. Conventional tree network topologies and routing mechanisms with single shortest path routing will cause congestion along oversubscribed links. Thus, path selection in a load-balanced way is needed to alleviate congestion and improve application performance. Multi-path routing algorithms can distribute traffic over diverse paths optimally than simple solutions like ECMP. They, however, require considerable computations, i.e. Frequently updating the path cost tree and dynamically optimizing path selections, thus they do not adapt well for large scale data center networks. In this paper, we propose a local rerouting mechanism in software defined networking (SDN) based Data Center networks to effectively manage congestion in the event of link congestion or failure. Unlike the works that deal with congestion by notifying the source which then reacts by rate adjustment or rerouting flows from source, our rerouting approach will re-forward flows (at the point of congestion or one hop before) to other possible paths based on our flow classification scheme. We demonstrate the effectiveness of our proposal through the simulation results on various topologies.
{"title":"SDN Controlled Local Re-routing to Reduce Congestion in Cloud Data Center","authors":"Renuga Kanagavelu, Khin Mi Mi Aung","doi":"10.1109/ICCCRI.2015.27","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.27","url":null,"abstract":"Data center networks require densely interconnected topologies to provide high bandwidth for various cloud computing services. It is required to fully utilize the bandwidth resource in such networks with varying traffic patterns. Conventional tree network topologies and routing mechanisms with single shortest path routing will cause congestion along oversubscribed links. Thus, path selection in a load-balanced way is needed to alleviate congestion and improve application performance. Multi-path routing algorithms can distribute traffic over diverse paths optimally than simple solutions like ECMP. They, however, require considerable computations, i.e. Frequently updating the path cost tree and dynamically optimizing path selections, thus they do not adapt well for large scale data center networks. In this paper, we propose a local rerouting mechanism in software defined networking (SDN) based Data Center networks to effectively manage congestion in the event of link congestion or failure. Unlike the works that deal with congestion by notifying the source which then reacts by rate adjustment or rerouting flows from source, our rerouting approach will re-forward flows (at the point of congestion or one hop before) to other possible paths based on our flow classification scheme. We demonstrate the effectiveness of our proposal through the simulation results on various topologies.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127216391","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}
With the increasing demand for large bandwidth and diversity of bandwidth requests, to maximize the revenue, cloud providers nowadays try to offer different bandwidth request models that include guaranteed bandwidth reservation requests and on-demand flexible bandwidth requests. While guaranteed bandwidth reservation requests are beneficial for providers from the point of view of cash flow due to the upfront fee, it faces the problem of bandwidth under-utilization. On the other hand, on-demand flexible requests generate higher revenue, but they suffer from future demand uncertainty. Controlling the admission and trade-off between these kinds of requests while maximizing the revenue becomes a challenging problem for providers. In this paper, we present an optimal bandwidth allocation approach which supports the above bandwidth request models and maximizes the revenue for providers. We model the bandwidth allocation problem as a Markov Decision Process (MDP) which takes into account the utilization of guaranteed bandwidth reservation requests and the future demand uncertainty of on-demand flexible requests. We solve the MDP problem by using a dynamic programming algorithm. We demonstrate that the proposed model can be integrated into cloud data centers by leveraging on the new features of software defined networks to control the bandwidth for users. The numerical results show that the proposed model outperforms the baseline schemes and generates high revenue for providers.
{"title":"Handling Uncertainty and Diversity in Cloud Bandwidth Demands for Revenue Maximization","authors":"Tram Truong-Huu, M. Gurusamy","doi":"10.1109/ICCCRI.2015.16","DOIUrl":"https://doi.org/10.1109/ICCCRI.2015.16","url":null,"abstract":"With the increasing demand for large bandwidth and diversity of bandwidth requests, to maximize the revenue, cloud providers nowadays try to offer different bandwidth request models that include guaranteed bandwidth reservation requests and on-demand flexible bandwidth requests. While guaranteed bandwidth reservation requests are beneficial for providers from the point of view of cash flow due to the upfront fee, it faces the problem of bandwidth under-utilization. On the other hand, on-demand flexible requests generate higher revenue, but they suffer from future demand uncertainty. Controlling the admission and trade-off between these kinds of requests while maximizing the revenue becomes a challenging problem for providers. In this paper, we present an optimal bandwidth allocation approach which supports the above bandwidth request models and maximizes the revenue for providers. We model the bandwidth allocation problem as a Markov Decision Process (MDP) which takes into account the utilization of guaranteed bandwidth reservation requests and the future demand uncertainty of on-demand flexible requests. We solve the MDP problem by using a dynamic programming algorithm. We demonstrate that the proposed model can be integrated into cloud data centers by leveraging on the new features of software defined networks to control the bandwidth for users. The numerical results show that the proposed model outperforms the baseline schemes and generates high revenue for providers.","PeriodicalId":183970,"journal":{"name":"2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116019635","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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