The Experts below are selected from a list of 6864 Experts worldwide ranked by ideXlab platform

Lizhe Wang - One of the best experts on this subject based on the ideXlab platform.

  • cross layer multi cloud real time application qos monitoring and benchmarking as a service framework
    IEEE International Conference on Cloud Computing Technology and Science, 2019
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Chang Liu, Fethi A Rabhi, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
    IEEE Transactions on Cloud Computing, 2015
    Co-Authors: Khalid Alhamazani, Karan Mitra, Fethi Rabhi, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and Networking Equipment) and software (databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single or multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS:Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers, spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • CLAMS: Cross-layer multi-cloud application monitoring-as-a-service framework
    Proceedings - 2014 IEEE International Conference on Services Computing SCC 2014, 2014
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Zhiqiang Huang, Lizhe Wang, Fethi Rabhi
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers, data processing frameworks, etc.) platforms. Application services hosted on single/multiple cloud provider platforms have diverse characteristics that require extensive monitoring mechanisms to aid in controlling run-time quality of service (e.g., access latency and number of requests being served per second, etc.). To provide essential real-time information for effective and efficient cloud application quality of service (QoS) monitoring, in this paper we propose, develop and validate CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework is capable of: (a) performing QoS monitoring of application components (e.g., database, web server, application server, etc.) that may be deployed across multiple cloud platforms (e.g., Amazon and Azure), and (b) giving visibility into the QoS of individual application component, which is something not supported by current monitoring services and techniques. We conduct experiments on real-world multi-cloud platforms such as Amazon and Azure to empirically evaluate our framework and the results validate that CLAMS efficiently monitors applications running across multiple clouds.

Khalid Alhamazani - One of the best experts on this subject based on the ideXlab platform.

  • cross layer multi cloud real time application qos monitoring and benchmarking as a service framework
    IEEE International Conference on Cloud Computing Technology and Science, 2019
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Chang Liu, Fethi A Rabhi, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
    IEEE Transactions on Cloud Computing, 2015
    Co-Authors: Khalid Alhamazani, Karan Mitra, Fethi Rabhi, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and Networking Equipment) and software (databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single or multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS:Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers, spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • CLAMS: Cross-layer multi-cloud application monitoring-as-a-service framework
    Proceedings - 2014 IEEE International Conference on Services Computing SCC 2014, 2014
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Zhiqiang Huang, Lizhe Wang, Fethi Rabhi
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers, data processing frameworks, etc.) platforms. Application services hosted on single/multiple cloud provider platforms have diverse characteristics that require extensive monitoring mechanisms to aid in controlling run-time quality of service (e.g., access latency and number of requests being served per second, etc.). To provide essential real-time information for effective and efficient cloud application quality of service (QoS) monitoring, in this paper we propose, develop and validate CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework is capable of: (a) performing QoS monitoring of application components (e.g., database, web server, application server, etc.) that may be deployed across multiple cloud platforms (e.g., Amazon and Azure), and (b) giving visibility into the QoS of individual application component, which is something not supported by current monitoring services and techniques. We conduct experiments on real-world multi-cloud platforms such as Amazon and Azure to empirically evaluate our framework and the results validate that CLAMS efficiently monitors applications running across multiple clouds.

Lorenz M Hilty - One of the best experts on this subject based on the ideXlab platform.

  • Assessing Internet energy intensity: A review of methods and results, Environmental Impact Assessment Review 45
    2020
    Co-Authors: Vlad C Coroama, Lorenz M Hilty
    Abstract:

    Abstract Assessing the average energy intensity of Internet transmissions is a complex task that has been a controversial subject of discussion. Estimates published over the last decade diverge by up to four orders of magnitude -from 0.0064 kilowatt--hours per gigabyte (kWh/GB) to 136 kWh/GB. This article presents a review of the methodological approaches used so far in such assessments: i) top--down analyses based on estimates of the overall Internet energy consumption and the overall Internet traffic, whereby average energy intensity is calculated by dividing energy by traffic for a given period of time, ii) model--based approaches that model all components needed to sustain an amount of Internet traffic, and iii) bottom--up approaches based on case studies and generalization of the results. Our analysis of the existing studies shows that the large spread of results is mainly caused by two factors: a) the year of reference of the analysis, which has significant influence due to efficiency gains in electronic Equipment, and b) whether end devices such as personal computers or servers are included within the system boundary or not. For an overall assessment of the energy needed to perform a specific task involving the Internet, it is necessary to account for the types of end devices needed for the task, while the energy needed for data transmission can be added based on a generic estimate of Internet energy intensity for a given year. Separating the Internet as a data transmission system from the end devices leads to more accurate models and to results that are more informative for decision makers, because end devices and the Networking Equipment of the Internet usually belong to different spheres of control

  • assessing internet energy intensity a review of methods and results
    Environmental Impact Assessment Review, 2014
    Co-Authors: Vlad C Coroama, Lorenz M Hilty
    Abstract:

    Abstract Assessing the average energy intensity of Internet transmissions is a complex task that has been a controversial subject of discussion. Estimates published over the last decade diverge by up to four orders of magnitude — from 0.0064 kilowatt-hours per gigabyte (kWh/GB) to 136 kWh/GB. This article presents a review of the methodological approaches used so far in such assessments: i) top–down analyses based on estimates of the overall Internet energy consumption and the overall Internet traffic, whereby average energy intensity is calculated by dividing energy by traffic for a given period of time, ii) model-based approaches that model all components needed to sustain an amount of Internet traffic, and iii) bottom–up approaches based on case studies and generalization of the results. Our analysis of the existing studies shows that the large spread of results is mainly caused by two factors: a) the year of reference of the analysis, which has significant influence due to efficiency gains in electronic Equipment, and b) whether end devices such as personal computers or servers are included within the system boundary or not. For an overall assessment of the energy needed to perform a specific task involving the Internet, it is necessary to account for the types of end devices needed for the task, while the energy needed for data transmission can be added based on a generic estimate of Internet energy intensity for a given year. Separating the Internet as a data transmission system from the end devices leads to more accurate models and to results that are more informative for decision makers, because end devices and the Networking Equipment of the Internet usually belong to different spheres of control.

Karan Mitra - One of the best experts on this subject based on the ideXlab platform.

  • cross layer multi cloud real time application qos monitoring and benchmarking as a service framework
    IEEE International Conference on Cloud Computing Technology and Science, 2019
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Chang Liu, Fethi A Rabhi, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
    IEEE Transactions on Cloud Computing, 2015
    Co-Authors: Khalid Alhamazani, Karan Mitra, Fethi Rabhi, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and Networking Equipment) and software (databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single or multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS:Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers, spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • CLAMS: Cross-layer multi-cloud application monitoring-as-a-service framework
    Proceedings - 2014 IEEE International Conference on Services Computing SCC 2014, 2014
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Zhiqiang Huang, Lizhe Wang, Fethi Rabhi
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers, data processing frameworks, etc.) platforms. Application services hosted on single/multiple cloud provider platforms have diverse characteristics that require extensive monitoring mechanisms to aid in controlling run-time quality of service (e.g., access latency and number of requests being served per second, etc.). To provide essential real-time information for effective and efficient cloud application quality of service (QoS) monitoring, in this paper we propose, develop and validate CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework is capable of: (a) performing QoS monitoring of application components (e.g., database, web server, application server, etc.) that may be deployed across multiple cloud platforms (e.g., Amazon and Azure), and (b) giving visibility into the QoS of individual application component, which is something not supported by current monitoring services and techniques. We conduct experiments on real-world multi-cloud platforms such as Amazon and Azure to empirically evaluate our framework and the results validate that CLAMS efficiently monitors applications running across multiple clouds.

Prem Prakash Jayaraman - One of the best experts on this subject based on the ideXlab platform.

  • cross layer multi cloud real time application qos monitoring and benchmarking as a service framework
    IEEE International Conference on Cloud Computing Technology and Science, 2019
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Chang Liu, Fethi A Rabhi, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
    IEEE Transactions on Cloud Computing, 2015
    Co-Authors: Khalid Alhamazani, Karan Mitra, Fethi Rabhi, Rajiv Ranjan, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Lizhe Wang
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (multi-core CPUs, GPUs, disks, and Networking Equipment) and software (databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single or multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS:Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers, spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.

  • CLAMS: Cross-layer multi-cloud application monitoring-as-a-service framework
    Proceedings - 2014 IEEE International Conference on Services Computing SCC 2014, 2014
    Co-Authors: Khalid Alhamazani, Karan Mitra, Rajiv Ranjan, Prem Prakash Jayaraman, Zhiqiang Huang, Lizhe Wang, Fethi Rabhi
    Abstract:

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and Networking Equipment) and software (e.g., databases, application servers, data processing frameworks, etc.) platforms. Application services hosted on single/multiple cloud provider platforms have diverse characteristics that require extensive monitoring mechanisms to aid in controlling run-time quality of service (e.g., access latency and number of requests being served per second, etc.). To provide essential real-time information for effective and efficient cloud application quality of service (QoS) monitoring, in this paper we propose, develop and validate CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework is capable of: (a) performing QoS monitoring of application components (e.g., database, web server, application server, etc.) that may be deployed across multiple cloud platforms (e.g., Amazon and Azure), and (b) giving visibility into the QoS of individual application component, which is something not supported by current monitoring services and techniques. We conduct experiments on real-world multi-cloud platforms such as Amazon and Azure to empirically evaluate our framework and the results validate that CLAMS efficiently monitors applications running across multiple clouds.