Performance Analysis of Fault-Tolerant Offloading Systems for Pervasive Services in Mobile Wireless Environments

Ou, Shumao; Wu, Yumin; Yang, Kun; Zhou, Bolei

doi:10.1109/icc.2008.356

Cited by 16 publications

(4 citation statements)

References 8 publications

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“…Ou et at. [9] discussed system failure in offloading systems. They analyzed system execution time considering system failure and recovery.…”

Section: Rel At Ed Workmentioning

confidence: 99%

Modeling multi-factor multi-site risk-based offloading for mobile cloud computing

Huang

2014

10th International Conference on Network and Service Management (CNSM) and Workshop

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Offtoading decisions for computation-intensive ap plications in mobile cloud computing may involve many decision factors. Important decision factors such as offtoading node reliability and privacy protection have not been well studied. Moreover, existing offtoading models mainly focus on the one-to one offtoading relation. To address the multi-factor and multi-site offtoading mobile cloud application scenarios, we present a multi factor multi-site risk-based offtoading model that abstracts the offtoading impact factors as for offtoading benefit and offtoading risk. The offtoading decision is made based on a comprehensive offtoading risk evaluation. This presented model is generic and extendible. Four offtoading impact factors are presented to show the construction and operation of the presented offtoading model, which can be easily extended to incorporate more factors to make offtoading decision more comprehensive. The overall offtoading benefits and risks are aggregated based on the mobile cloud users' preference. The performance evaluation presents the practicality of the presented solution. I. IN TRODUCTIONIn mobile cloud computing, offloading is an important approach to overcome the resources and functionalities con strains of mobile devices. In a mobile cloud offloading model, applications deploy their components on multiple application processing nodes such as mobile smart phones and virtual machines in a cloud. A mobile device can rely on an offloading decision model based on multiple factors such as offloading computational-intensive, time-or energy-consuming applica tion functions.Many existing mobile cloud offloading models focus on a one-to-one directional offloading model (e.g.,offloading from a mobile device to a cloud server (e.g., a VM).Study in [4] had shown the benefits of using one-to-many (i.e., multi-site) offloading models to improve the performance of mobile devices, where a mobile device can offload multiple application functions to multiple computing nodes. However, existing offloading models only consider one offloading factor such as computation overhead, networking delay, or energy consumption. Moreover, they do not consider other important offloading factors such privacy, reliability, etc.One of important focuses of this work is to model the offloading risks that have not been addressed by previous research. Particularly, we focus on two main sources of risks in mobile cloud computing: privacy breach and offloading reliability. Mobile application components can be offloaded to malicious nodes that can potentially compromise the ap plication's privacy, e.g., the offloaded data and functions may contain private data or expose the purpose of the application. In addition to the privacy-breach risk, another offloading risk is the reliability of offloading targets. For example, the surrogates may be unavailable due to unstable communication link or software crash, etc. In this case, offloading effort has to include offloading recovery strategies. Thus, both privacy breach and reliability ris...

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“…Ou et at. [9] discussed system failure in offloading systems. They analyzed system execution time considering system failure and recovery.…”

Section: Rel At Ed Workmentioning

confidence: 99%

Modeling multi-factor multi-site risk-based offloading for mobile cloud computing

Huang

2014

10th International Conference on Network and Service Management (CNSM) and Workshop

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“…In [10] X. Gu et al proposed a method to reduce the execution time of a mobile task by proper task partition while taking into account the MD memory constraint. In [11] S. Ou et al evaluated the offloading performance for a MD in mobile environments, with the consideration of unreachable servers due to the mobility of the MD. In [12] a task offloading system was proposed to support an efficient deployment of mobile applications for a MD.…”

Section: Mobile Task Offloadingmentioning

confidence: 99%

Optimal radio resource allocation for mobile task offloading in cellular networks

2014

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obile devices (MDs), for example, smart phones and tablets, are becoming the major computing workspaces for individuals thanks to their increasing capabilities. Users expect to run desktop-level applications (e.g. document processing, media playback, and 3D gaming) on MDs anywhere. Specifically, a mobile application can consist of multiple tasks, which are termed mobile tasks. For instance, real-time rendering is a representative mobile task for 3D gaming on the MD. Although the capabilities of MDs keep increasing, executing resource-demanding mobile tasks on MDs would result in poor performance and short battery lifetime. A recent example is that the rendering capability requirement (in pixels per second) of mainstream desktop 3D gaming in 2012 is more than four times the rendering capability of the most powerful MDs in the same year, such as iPhone 5 [1]. One solution to the resource scarcity problem is to enable MDs to offload resource-demanding mobile tasks to surrogates, which are remote servers with stronger capabilities, through wireless networks [2]. As a result, the local resources on MDs can be reserved, and energy consumption can be reduced. Moreover, the emerging cloud computing technique can provide powerful Internet-based surrogates to help MDs [3].To offload the mobile task from a MD to an Internetbased surrogate, related data would be transferred between the MD and the surrogate through multiple layers of wired and wireless networks. For example, state-of-the-art cellular networks, such as long term evolution (LTE) systems [4], could serve as access networks for MDs to effectively offload mobile tasks. The process of mobile task offloading usually has a deadline to make MD users feel like running applications locally on their MDs. However, when multiple MDs in a cell offload mobile tasks, how to complete the offloading process before deadlines becomes a challenging issue. Specifically, the radio resources in a cell for data transmissions could be insufficient when a number of MDs offload mobile tasks, which would result in large communication latencies and degrade the MD user experience on the mobile task offloading significantly. As a result, it is crucial to effectively allocate radio resources to the data transmissions of the mobile task offloading from multiple MDs. Moreover, heterogenous requirements for mobile task offloading can be utilized to improve performance. Here, heterogenous requirements mean that the amount of data to be transferred between the MD and the surrogate, the surrogate response time, and the deadline of the mobile task offloading, could be different for different MDs.As motivated, we pose the radio resource allocation problem for the case that multiple MDs request mobile task offloading in a cell. Specifically, the purpose of radio resource allocation is to minimize the overall execution time for offloaded mobile tasks from multiple MDs, while satisfying the execution deadline for each mobile task. Moreover, the optimal radio resource allocation plan should take into acc...

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“…Memory and transferred data size were taken into the consideration, but not network availability. Ou et al [12] discussed system failure in offloading systems. They analyzed system execution time considering system failure and recovery.…”

Section: Related Workmentioning

confidence: 99%

“…where data exchanges between bundles are not counted as they happen locally and cost little time compared to time of data exchange over network. For a particular configuration c, offloading rate p [12] is defined as the proportion of offloaded task to all task in terms of computation time.…”

Section: A Application Model and Ideal Network Modelmentioning

confidence: 99%

Making Offloading Decisions Resistant to Network Unavailability for Mobile Cloud Collaboration

Wu¹,

Huang²,

Bouzefrane³

2013

Proceedings of the 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Offloading is one major type of collaborations between mobile devices and clouds to achieve less execution time and less energy consumption. Offloading decisions for mobile cloud collaboration involve many decision factors. One of important decision factors is the network unavailability that has not been well studied. This paper presents an offloading decision model that takes network unavailability into consideration. Network with some unavailability can be modeled as an alternating renewal process. Then, application execution time and energy consumption in both ideal network and network with some unavailability are analyzed. Based on the presented theoretical model, an application partition algorithm and a decision module are presented to produce an offloading decision that is resistant to network unavailability. Simulation results demonstrate good performance of proposed scheme, where the proposed partition algorithm is analyzed in different application and cloud scenarios.

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Performance Analysis of Fault-Tolerant Offloading Systems for Pervasive Services in Mobile Wireless Environments

Cited by 16 publications

References 8 publications

Modeling multi-factor multi-site risk-based offloading for mobile cloud computing

Modeling multi-factor multi-site risk-based offloading for mobile cloud computing

Optimal radio resource allocation for mobile task offloading in cellular networks

Making Offloading Decisions Resistant to Network Unavailability for Mobile Cloud Collaboration

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