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Application Function

The Experts below are selected from a list of 3273 Experts worldwide ranked by ideXlab platform

Aruna Seneviratne – 1st expert on this subject based on the ideXlab platform

  • seamless resource sharing in wearable networks by Application Function virtualization
    IEEE Transactions on Mobile Computing, 2019
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Diego Perino, Dwight Makaroff, Aruna Seneviratne

    Abstract:

    The prevalence of smart wearable devices is increasing exponentially and we are witnessing a wide variety of fascinating new services that leverage the capabilities of these wearables. Wearables are truly changing the way mobile computing is deployed and mobile apps are being developed. It is possible to leverage the capabilities such as connectivity, processing, and sensing of wearable devices in an adaptive manner for efficient resource usage and information accuracy within the personal area network. We show that app developers are not yet taking advantage of these cross-device capabilities, however, instead using wearables as passive sensors or simple end displays to provide notifications to the user. We thus design Application Function Virtualization (AFV), an architecture enabling automated dynamic Function virtualization and scheduling across devices in a personal area network, simplifying the development of the apps that are adaptive to context changes. AFV provides a simple set of APIs hiding complex architectural tasks from app developers whilst continuously monitoring the user, device, and network context, to enable the adaptive invocation of Functions across devices. We show the feasibility of our design by implementing AFV on Android, and the benefits for the user in terms of resource efficiency, especially in saving energy consumption, and quality of experience with multiple use cases.

  • LCN – Maximizing the Wearable Network Lifetime through Virtualized Application Function Chaining
    2018 IEEE 43rd Conference on Local Computer Networks (LCN), 2018
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Aruna Seneviratne

    Abstract:

    The smart devices usage is growing rapidly driven by innovative new smart wearables and service offerings. This has led to Applications that utilize multiple devices around the body to provide immersive environments such as mixed reality that rely on a number of different types of Functions and require considerable resources. Thus one of the major challenges in supporting these Applications is dependent on the battery lifetime of devices that provide the necessary Functionality. The focus of this paper is to improve the battery efficiency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, it is possible to consider them as a resource pool and further utilize them intelligently to improve the system lifetime via Function virtualization. We formulate the intelligent Function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an efficient heuristic solution. Next, we demonstrate the orchestration of the virtualized Functions in order to achieve specific Functionalities. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper Function allocation and orchestration.

  • Virtualized Application Function Chaining: Maximizing the Wearable System Lifetime.
    arXiv: Networking and Internet Architecture, 2018
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Aruna Seneviratne

    Abstract:

    The number of smart devices wear and carry by users is growing rapidly which is driven by innovative new smart wearables and interesting service o erings. This has led to Applications that utilize multiple devices around the body to provide immersive environments such as mixed reality. These Applications rely on a number of di erent types of Functions such as sensing, communication and various types of processing, that require considerable resources. Thus one of the major challenges in supporting of these Applications is dependent on the battery lifetime of the devices that provide the necessary Functionality. The battery lifetime can be extended by either incorporating a battery with larger capacity and/or by utilizing the available resources e ciently. However, the increases in battery capacity are not keeping up with the demand and larger batteries add to both the weight and size of the device. Thus, the focus of this paper is to improve the battery e ciency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, and or is in close proximity, it is possible consider them as a resource pool and further utilize them intelligently to improve the system lifetime. Speci cally, we formulate the Function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an e cient heuristic solution. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper Function allocation and orchestration.

Harini Kolamunna – 2nd expert on this subject based on the ideXlab platform

  • seamless resource sharing in wearable networks by Application Function virtualization
    IEEE Transactions on Mobile Computing, 2019
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Diego Perino, Dwight Makaroff, Aruna Seneviratne

    Abstract:

    The prevalence of smart wearable devices is increasing exponentially and we are witnessing a wide variety of fascinating new services that leverage the capabilities of these wearables. Wearables are truly changing the way mobile computing is deployed and mobile apps are being developed. It is possible to leverage the capabilities such as connectivity, processing, and sensing of wearable devices in an adaptive manner for efficient resource usage and information accuracy within the personal area network. We show that app developers are not yet taking advantage of these cross-device capabilities, however, instead using wearables as passive sensors or simple end displays to provide notifications to the user. We thus design Application Function Virtualization (AFV), an architecture enabling automated dynamic Function virtualization and scheduling across devices in a personal area network, simplifying the development of the apps that are adaptive to context changes. AFV provides a simple set of APIs hiding complex architectural tasks from app developers whilst continuously monitoring the user, device, and network context, to enable the adaptive invocation of Functions across devices. We show the feasibility of our design by implementing AFV on Android, and the benefits for the user in terms of resource efficiency, especially in saving energy consumption, and quality of experience with multiple use cases.

  • LCN – Maximizing the Wearable Network Lifetime through Virtualized Application Function Chaining
    2018 IEEE 43rd Conference on Local Computer Networks (LCN), 2018
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Aruna Seneviratne

    Abstract:

    The smart devices usage is growing rapidly driven by innovative new smart wearables and service offerings. This has led to Applications that utilize multiple devices around the body to provide immersive environments such as mixed reality that rely on a number of different types of Functions and require considerable resources. Thus one of the major challenges in supporting these Applications is dependent on the battery lifetime of devices that provide the necessary Functionality. The focus of this paper is to improve the battery efficiency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, it is possible to consider them as a resource pool and further utilize them intelligently to improve the system lifetime via Function virtualization. We formulate the intelligent Function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an efficient heuristic solution. Next, we demonstrate the orchestration of the virtualized Functions in order to achieve specific Functionalities. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper Function allocation and orchestration.

  • Virtualized Application Function Chaining: Maximizing the Wearable System Lifetime.
    arXiv: Networking and Internet Architecture, 2018
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Aruna Seneviratne

    Abstract:

    The number of smart devices wear and carry by users is growing rapidly which is driven by innovative new smart wearables and interesting service o erings. This has led to Applications that utilize multiple devices around the body to provide immersive environments such as mixed reality. These Applications rely on a number of di erent types of Functions such as sensing, communication and various types of processing, that require considerable resources. Thus one of the major challenges in supporting of these Applications is dependent on the battery lifetime of the devices that provide the necessary Functionality. The battery lifetime can be extended by either incorporating a battery with larger capacity and/or by utilizing the available resources e ciently. However, the increases in battery capacity are not keeping up with the demand and larger batteries add to both the weight and size of the device. Thus, the focus of this paper is to improve the battery e ciency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, and or is in close proximity, it is possible consider them as a resource pool and further utilize them intelligently to improve the system lifetime. Speci cally, we formulate the Function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an e cient heuristic solution. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper Function allocation and orchestration.

Kanchana Thilakarathna – 3rd expert on this subject based on the ideXlab platform

  • seamless resource sharing in wearable networks by Application Function virtualization
    IEEE Transactions on Mobile Computing, 2019
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Diego Perino, Dwight Makaroff, Aruna Seneviratne

    Abstract:

    The prevalence of smart wearable devices is increasing exponentially and we are witnessing a wide variety of fascinating new services that leverage the capabilities of these wearables. Wearables are truly changing the way mobile computing is deployed and mobile apps are being developed. It is possible to leverage the capabilities such as connectivity, processing, and sensing of wearable devices in an adaptive manner for efficient resource usage and information accuracy within the personal area network. We show that app developers are not yet taking advantage of these cross-device capabilities, however, instead using wearables as passive sensors or simple end displays to provide notifications to the user. We thus design Application Function Virtualization (AFV), an architecture enabling automated dynamic Function virtualization and scheduling across devices in a personal area network, simplifying the development of the apps that are adaptive to context changes. AFV provides a simple set of APIs hiding complex architectural tasks from app developers whilst continuously monitoring the user, device, and network context, to enable the adaptive invocation of Functions across devices. We show the feasibility of our design by implementing AFV on Android, and the benefits for the user in terms of resource efficiency, especially in saving energy consumption, and quality of experience with multiple use cases.

  • LCN – Maximizing the Wearable Network Lifetime through Virtualized Application Function Chaining
    2018 IEEE 43rd Conference on Local Computer Networks (LCN), 2018
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Aruna Seneviratne

    Abstract:

    The smart devices usage is growing rapidly driven by innovative new smart wearables and service offerings. This has led to Applications that utilize multiple devices around the body to provide immersive environments such as mixed reality that rely on a number of different types of Functions and require considerable resources. Thus one of the major challenges in supporting these Applications is dependent on the battery lifetime of devices that provide the necessary Functionality. The focus of this paper is to improve the battery efficiency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, it is possible to consider them as a resource pool and further utilize them intelligently to improve the system lifetime via Function virtualization. We formulate the intelligent Function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an efficient heuristic solution. Next, we demonstrate the orchestration of the virtualized Functions in order to achieve specific Functionalities. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper Function allocation and orchestration.

  • Virtualized Application Function Chaining: Maximizing the Wearable System Lifetime.
    arXiv: Networking and Internet Architecture, 2018
    Co-Authors: Harini Kolamunna, Kanchana Thilakarathna, Aruna Seneviratne

    Abstract:

    The number of smart devices wear and carry by users is growing rapidly which is driven by innovative new smart wearables and interesting service o erings. This has led to Applications that utilize multiple devices around the body to provide immersive environments such as mixed reality. These Applications rely on a number of di erent types of Functions such as sensing, communication and various types of processing, that require considerable resources. Thus one of the major challenges in supporting of these Applications is dependent on the battery lifetime of the devices that provide the necessary Functionality. The battery lifetime can be extended by either incorporating a battery with larger capacity and/or by utilizing the available resources e ciently. However, the increases in battery capacity are not keeping up with the demand and larger batteries add to both the weight and size of the device. Thus, the focus of this paper is to improve the battery e ciency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, and or is in close proximity, it is possible consider them as a resource pool and further utilize them intelligently to improve the system lifetime. Speci cally, we formulate the Function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an e cient heuristic solution. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper Function allocation and orchestration.