Mobile Cloud Computing: Offloading Mobile Processing to the Cloud

Sanjay P Ahuja ,Jesús Zambrano

doi:10.5539/cis.v9n1p90

Sanjay P Ahuja , Jesús Zambrano

Open Access

https://doi.org/10.5539/cis.v9n1p90

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Abstract

<p class="zhengwen">The current proliferation of mobile systems, such as smart phones and tablets, has let to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device (such as phone + mp3 player + camera + Web browser + GPS + mobile apps + sensors). However, this conjunction penalizes the mobile system both with respect to computational resources such as processor speed, memory consumption, disk capacity, and in weight, size, ergonomics and the component most important to users, battery life. Therefore, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems.</p><p>Offloading mobile processing is an excellent solution to augment mobile capabilities by migrating computation to powerful infrastructures. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for offloading computation and data processing from mobile devices restricted by reduced resources. This research uses cloud computing as processing platform for intensive-computation workloads while measuring energy consumption and response times on a Samsung Galaxy S5 Android mobile phone running Android 4.1OS.</p>

Highlights

Recent trends aim towards the integration of huge variety of functionality and applications within a mobile device, which means mobile devices are directly affected by two main factors: battery life limited by energy consumption and processing time limited by poor mobile resources
The reason is that there is a communication penalty in response times and energy consumption when offloading this kind of workload to both cloud computing platforms using Wi-Fi or 4G
This paper benchmarked the benefits in response times and energy consumption when offloading mobile computing-intensive workloads to Amazon EC2 and Windows Azure using Wi-Fi and 4G network technologies

Summary

Introduction

Recent trends aim towards the integration of huge variety of functionality and applications within a mobile device, which means mobile devices are directly affected by two main factors: battery life limited by energy consumption and processing time limited by poor mobile resources. The limited hardware capability in current mobile devices is an obstacle to supporting the increasing high-processing demands of the latest applications and of future developments. Energy efficiency and performance have been critical concerns for developing electronic devices such as personal computers and mobile devices, and this importance seems to be increasing (Miettinen, 2010). Offloading mobile processing could mean an effective solution to overcome the limited resources on mobile phones. When using this approach, the critical aspect for mobile clients is the trade-off between energy consumed by computation and the energy consumed by communication (Miettinen, 2010). There are many concerns when offloading data to remote infrastructures such as data delivery, standard interface, trust, security, privacy (Mandeep, 2015) and reliability on the wireless network as well as on remote infrastructure service availability

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