vStore: A Context-Aware Framework for Mobile Micro-Storage at the Edge

Gedeon, Julien; Himmelmann, Nicolás; Felka, Patrick; Herrlich, Fabian; Stein, Michael; Mühlhäuser, Max

doi:10.1007/978-3-319-90740-6_10

Cited by 11 publications

(10 citation statements)

References 22 publications

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“…Dynamic offloading is a technique that extends the capabilities of mobile devices, as well as their battery life, by migrating hardcomputation tasks to resource-rich devices such as remote servers or cloud platforms (e.g., Google Awareness) [25,26] . Consequently, code offloading solutions require a set of surrogate devices on to which the client can offload tasks, as well as connectivity to the internet.…”

Section: Dynamic Offloadingmentioning

confidence: 99%

Energy efficient adaptation engines for android applications

Cañete

Horcas

Ayala

et al. 2020

Information and Software Technology

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ContextThe energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application.Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained.Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption. ResultsThe proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%. ConclusionThe adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.

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Section: Dynamic Offloadingmentioning

confidence: 99%

Energy efficient adaptation engines for android applications

Cañete

Horcas

Ayala

et al. 2020

Information and Software Technology

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“…In the domain of Wireless Sensor Networks this is a popular approach [53] that can easily be mapped to aggregation by intermediate edge nodes. Besides aggregation, edge computing can also offer storage capabilities [54] that take into account contextual information for the decision on where to store the data [10]. For example, at large-scale events with overloaded mobile networks, edge nodes can provide storage to share data among people that are close-by.…”

Section: A Promises Benefits and Drawbacks Of Edge Computingmentioning

confidence: 99%

“…For example, we can imagine sharing content from large-scale events or tourist sites. In this domain, Gedeon et al [10]* have presented vStore, a rulebased framework that uses contextual information of the user to make informed storage decisions. Simoens et al [181] have presented GigaSight, a framework for the decentralized collection of videos through cloudlets.…”

Section: ) Data Storage Content Delivery and Cachingmentioning

confidence: 99%

“…This mismatch-the demand to offload demanding processing tasks, but close to the mobile device-has led to the emergence of edge computing [7]- [9]. Edge computing avoids the costly transfer of latency-and bandwidthcritical data to the cloud and uses heterogeneous, nearby resources for storage [10] and computation [11]. Small data centers, often accessed via 1-hop wireless connections, have been termed cloudlets [12], [13] and are a crucial enabler for edge computing.…”

Section: Introductionmentioning

confidence: 99%

“…https://www.docker.com/ (Accessed: 2019-08-09)8 https://linuxcontainers.org/ (Accessed: 2019-08-09)9 https://openvz.org/ (Accessed: 2019-08-09)10 https://coreos.com/rkt/ (Accessed: 2019-08-09)11 https://podman.io/ (Accessed: 2019-08-09) VOLUME 7, 2019…”

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confidence: 99%

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What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

et al. 2019

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Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

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SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience

Chen

Zhou

et al. 2021

Int J of Intelligent Sys

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With the development of edge computing, edge storage solutions are attracting widespread attention. When facing the requirements of lower latency and faster access speed from end devices, edge storage solutions are considered to be an alternative to the cloud. However, edges are usually owned by small organizations which have limited operations and maintenance capabilities. This makes these edge devices can be easily disabled by external attacks or internal hardware failures. Besides, the heterogeneity of the edge devices will also make it difficult to price the edge resources uniformly. To tackle these problems, we propose SmartStore: an auction mechanism based on blockchain to allocate edge resources. Considering centralized solutions have access bottlenecks and trust issues, we built SmartStore on the smart contract. With Bayesian game theory, SmartStore can analyze how data owners (DO) and edges price the resources can maximize their benefits. From an economic perspective, both DO and edges can make full use of edge heterogeneous resources with SmartStore. Besides, a two‐stage submission strategy is proposed to complete the sealed auction. Furthermore, considering the reliability of edge storage, we propose a cluster‐based block distribution algorithm for SmartStore's intelligent edge recommendation process. SmartStore ensures the reliability of edge storage while maximizing the benefits and resource utilization of both parties. Finally, we conduct specific experiments on the proposed auction smart contract through “Ethereum” and the experimental results of implementation show the effectiveness and efficiency of our SmartStore.

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vStore: A Context-Aware Framework for Mobile Micro-Storage at the Edge

Cited by 11 publications

References 22 publications

Energy efficient adaptation engines for android applications

Energy efficient adaptation engines for android applications

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

SmartStore: A blockchain and clustering based intelligent edge storage system with fairness and resilience

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