The case for cyber foraging

Balan, Rajesh Krishna; Flinn, Jason; Satyanarayanan, Mahadev; Sinnamohideen, Shafeeq; Yang, Hen‐I

doi:10.1145/1133373.1133390

Cited by 237 publications

(131 citation statements)

References 9 publications

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“…The origins of computational offloading are in cyberforaging, which aimed at distributing computations from a resource constrained (mobile) device to nearby infrastructure [21]. Since then, most of the work has focused on linking the device with remote infrastructure or a hybrid that combines remote and local connectivity.…”

Section: Related Workmentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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Abstract-Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterising execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.

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Section: Related Workmentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

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“…Offloading computation from mobile devices to remote surrogates, known as cyber foraging [13] has been subject for over a decade. In recent work, Kristensen et al [9] propose Scavenger, a cyber foraging system in Python which outsources Python methods, and the scheduler orchestrating this outsourcing process uses history-based profiles.…”

Section: Related Workmentioning

confidence: 99%

Adaptive deployment and configuration for mobile augmented reality in the cloudlet

Verbelen

Simoens

Turck

et al. 2014

Journal of Network and Computer Applications

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Despite recent advances in mobile hardware, most mobile devices still fall short to execute complex multimedia applications with real-time requirements such as augmented reality (AR). Because offloading the application to the cloud is not always a solution due to the high and often unpredictable WAN latencies, the concept of cloudlets has been introduced: nearby infrastructure offering virtual machines for remote execution.In this paper we present a cloudlet platform, providing two important contributions. First, the platform allows cloudlets to be formed in a dynamic way, including (fixed) virtualized infrastructure co-located with the wireless access point, as well as any device in the LAN network supporting the platform. The approach can also be extended towards the cloud, facilitating distribution of applications over three tiers (i.e. the device, the cloudlet and the cloud). Second, instead of moving a complete virtual machine to the cloudlet, we propose a more fine-grained approach, by managing and deploying applications on the component level. Application components are declared by the developer, together with their real-time constraints and configuration parameters, which are configured and distributed among the cloudlet by the platform at runtime depending on the application context, in order to meet the constraints and to optimize the user experience.An OSGi-based prototype implementation on the Android platform is highlighted and evaluated using a mobile AR use case, showing the need for a component-based approach for the cloudlet.

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“…Plenty of research has been conducted to outsource computing tasks to external computing sources [10,18,26,11]. Typically, these schemes focus on how to properly split the job and deploy on the external computing sources.…”

Section: Related Workmentioning

confidence: 99%

“…Flinn et al [18] also propose a similar idea, in which remote execution simultaneously leverages the mobility of mobile devices and the richer resources of large devices. Balan et al [10,11] propose to augment the computation and storage capabilities of mobile devices by exploiting the nearby (surrogate) computers. Recently, MAUI [14] is proposed to partition the program dynamically and submit it on surrogate computers.…”

Section: Related Workmentioning

confidence: 99%

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Mobile MapReduce: Minimizing Response Time of Computing Intensive Mobile Applications

Hassan

Chen

2012

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

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Abstract. The increasing popularity of mobile devices calls for effective execution of mobile applications. A lot of research has been conducted on properly splitting and outsourcing computing intensive tasks to external resources (e.g., public clouds) by considering insufficient computing resources on mobile devices. However, little attention has been paid to the overall users' response time, where the network may dominate. In this study, we set to investigate how to effectively minimize users' response time for mobile applications. We consider both the impact of the network and the computing itself. We first show that outsourcing to nearby residential computers may be more advantageous than public clouds for mobile applications due to network impact. Furthermore, to speed up computing, we leverage parallel processing techniques. Accordingly, we propose to build Mobile MapReduce (MMR) to effectively execute outsource computing intensive mobile applications. Based on the original MapReduce framework, a new scheduling model is built in MMR that can always leverage the best computing resources to conduct computation with appropriate parallel processing. To demonstrate the performance of MMR, we run several real-world applications, such as text searching, face detection, and image processing, on the prototype. The results show great potentials of MMR in minimizing the response time of the outsourced mobile applications.

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The case for cyber foraging

Cited by 237 publications

References 9 publications

Evidence-Aware Mobile Computational Offloading

Evidence-Aware Mobile Computational Offloading

Adaptive deployment and configuration for mobile augmented reality in the cloudlet

Mobile MapReduce: Minimizing Response Time of Computing Intensive Mobile Applications

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