Maui

Cuervo, Eduardo; Balasubramanian, Aruna; Cho, Dae-ki; Wolman, Alec; Saroiu, Stefan; Chandra, Ranveer; Bahl, Paramvir

doi:10.1145/1814433.1814441

Cited by 1,921 publications

(152 citation statements)

References 25 publications

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“…They have been widely used in mobile and ubiquitous computing, including location systems [27], measurement [18] and contextsensing [28]. Additional efforts have focussed on understanding and optimising the platform itself, considering the measurement of energy use [23], computation offload [7], or resource sharing between devices [25]. However, understanding the importance or effectiveness of these contributions is difficult due to the lack of generally available, detailed data about how smartphones are actually used.…”

Section: Introductionmentioning

confidence: 99%

Device Analyzer: Understanding Smartphone Usage

Wagner

Rice

Beresford

2014

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. We describe Device Analyzer, a robust data collection tool which is able to reliably collect information on Android smartphone usage from an open community of contributors. We collected the largest, most detailed dataset of Android phone use publicly available to date. In this paper we systematically evaluate smartphones as a platform for mobile ubiquitous computing by quantifying access to critical resources in the wild. Our analysis of the dataset demonstrates considerable diversity in behaviour between users but also over time. We further demonstrate the value of handset-centric data collection by presenting case-study analyses of human mobility, interaction patterns, and energy management and identify notable differences between our results and those found by other studies.

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Section: Introductionmentioning

confidence: 99%

Device Analyzer: Understanding Smartphone Usage

Wagner

Rice

Beresford

2014

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Offloading decisions can be done in two ways 1) Manually by the developers [14] 2) Automatically using tools [15].…”

Section: Offloading Mobile Applications In Cloudmentioning

confidence: 99%

Survey on Three Components of Mobile Cloud Computing: Offloading, Distribution and Privacy

Paranjothi¹,

Khan²,

Nijim³

2017

JCC

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Mobile Cloud Computing (MCC) brings rich computational resource to mobile users, network operators, and cloud computing providers. It can be represented in many ways, and the ultimate goal of MCC is to enable execution of rich mobile application with rich user experience. Mobility is one of the main characteristics of MCC environment where user can be able to continue their work regardless of movement. This literature review paper presents the state-of-the-art survey of MCC. Also, we provide the communication architecture of MCC and taxonomy of mobile cloud in which specifically concentrates on offloading, mobile distribution computing, and privacy. Through an extensive literature review, we found that MCC is a technologically beneficial and expedient paradigm for virtual environments in terms of virtual servers in a distributed environment, multi-tenant architecture and data storing in a cloud. We further identified the drawbacks in offloading, mobile distribution computing, privacy of MCC and how this technology can be used in an effective way.

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“…In 2010 Cuervo et al presents the MAUI system [20], a server-based system that enables a fine-grained energy-aware offload of mobile code to a server infrastructure. It embraces the programmer-guided partitioning approaches in which the programmer can annotate the method that can be offloaded for remote execution.…”

Section: A Server-based Architecturementioning

confidence: 99%

Towards Distributed Mobile Computing

Massari

Zanella

Fornaciari

2016

2016 Mobile System Technologies Workshop (MST)

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Abstract-In the latest years, we observed an exponential growth of the market of the mobile devices. In this scenario, it assumes a particular relevance the rate at which mobile devices are replaced. According to the International Telecommunicaton Union in fact, smart-phone owners replace their device every 20 months, on average. The side effect of this trend is to deal with the disposal of an increasing amount of electronic devices which, in many cases, are still working. We believe that it is feasible to recover such an unexploited computational power. Through a change of paradigm in fact, it is possible to achieve a two-fold objective: 1) extend the mobile devices lifetime; 2) enable a new opportunity to speed up mobile applications. In this paper we aim at providing a survey of state-of-art solutions aim at going in the direction of a Distributed Mobile Computing paradigm. We put in evidence the challenges to be addressed in order to implement this paradigm and we propose some possible future improvements.

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Maui

Cited by 1,921 publications

References 25 publications

Device Analyzer: Understanding Smartphone Usage

Device Analyzer: Understanding Smartphone Usage

Survey on Three Components of Mobile Cloud Computing: Offloading, Distribution and Privacy

Towards Distributed Mobile Computing

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