Mobile offloading in the wild: Findings and lessons learned through a real-life experiment with a new cloud-aware system

Barbera, Marco Valerio; Kosta, Sokol; Mei, Alessandro; Perta, Vasile C.; Stefa, Julinda

doi:10.1109/infocom.2014.6848180

Cited by 43 publications

(25 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies of computational offloading in the wild have mostly shown that offloading increases computational effort rather than reduces it [26]. This is due to the large amount of mobile usage contexts, and the poor understanding of the conditions and configurations in which a device offloads to the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…Some recent work attempts to overcome this problem by dynamically alleviating the issues of inferring the right matching between mobile and cloud considering multiple levels of granularity [11], [27]. Similarly, CDroid [26] attempts to improve offloading in real scenarios. However, the framework focuses more on data offloading rather than computational offloading.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evidence-Aware Mobile Computational Offloading

Flores

Hui

Nurmi

et al. 2018

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…Motivated by the aforementioned consideration and based on the self-configuring framework as a prototype which is recently presented in [25], we define a cases study, named "StreamVehicularFog" (SVF), as a paradigm which is covered in an Internet assisted peer-to-peer (P2P) service architecture. The SVF aim is to minimize the total energy cost in mobile devices and data centers with an adaptive synchronization of the resource management operations that guarantee the stream of data passed/process over the underutilized networking/computing servers in FDC.…”

Section: A Case Study: Streamvehicularfogmentioning

confidence: 99%

Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study

Naranjo¹,

Pooranian²,

Shamshirband³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract:In this paper, we discuss the most significant application opportunities and outline the challenges in performing a real-time and energy-efficient management of the distributed resources available at mobile devices and Internet-to-Data Center. We also present an energy-efficient adaptive scheduler for Vehicular Fog Computing (VFC) that operates at the edge of a vehicular network, connected to the served Vehicular Clients (VCs) through an Infrastructure-to-Vehicular (I2V) over multiple Foglets (Fls). The scheduler optimizes the energy by leveraging the heterogeneity of Fls, where the Fl provider shapes the system workload by maximizing the task admission rate over data transfer and computation. The presented scheduling algorithm demonstrates that the resulting adaptive scheduler allows scalable and distributed implementation.

show abstract

“…[6] propose a new Framework CDroid (Cloud-anDroid) for Android. CDroid is designed to enable code offloading of intensive communication application like Facebook mobile, twitter and online gaming.…”

Section: Related Workmentioning

confidence: 99%

“…Regarding the challenge of communication and access to Cloud resources, Barbera and al. [6] propose a new Offloading Framework CDroid (Cloud-anDroid).…”

Section: Introductionmentioning

confidence: 99%

Towards a smart cloud gate for smart devices

Gherari

Amirat

Oussalah

et al. 2014

2014 Third IEEE International Colloquium in Information Science and Technology (CIST)

View full text Add to dashboard Cite

Abstract-Mobile Cloud Computing (MCC) is a newparadigm that has been introduced as an optimal solution for mobile technology's issues. Mobile technology aims to exploit the advantages offered by the Cloud in order to provide ubiquitous PC-like functionalities to mobile users. It is noteworthy that despite the benefits associated with the adoption of the Cloud by mobile technology the gate to the Cloud remains frozen. This means that mobile applications often use the same services without having an update of the novelty in Cloud. Thus, applications lack awareness of new services which are more advantageous in terms of features and qualities than the currently used ones. To enable mobile applications to exploit the Cloud intelligently, we propose Smart Mobile Cloud Architecture (SMCA). We consider this new architecture as referential allowing MCC users to have a full awareness of both contexts (Cloud and Mobile) at the same time. We introduce a new concept called Smart Cloud Gate (SCG), which aims to profile both mobile applications and the Cloud to extract knowledge that will be used as a criteria to select the appropriate services, which will be suggested to mobile applications and give each different application the appropriate view of the Cloud.

show abstract

Mobile offloading in the wild: Findings and lessons learned through a real-life experiment with a new cloud-aware system

Cited by 43 publications

References 31 publications

Evidence-Aware Mobile Computational Offloading

Evidence-Aware Mobile Computational Offloading

Fog over Virtualized IoT: New Opportunity for Context-Aware Networked Applications and a Case Study

Towards a smart cloud gate for smart devices

Contact Info

Product

Resources

About