Towards an Offloading Framework based on Big Data ... - ScienceDirect

Procedia Computer Science Volume 53, 2015, Pages 292–297 2015 INNS Conference on Big Data

Towards an offloading framework based on Big Data analytics in Mobile Cloud Computing Environments Hamdi Kchaou1*, Zied Kechaou1 and Adel M. Alimi1 1 The University of Sfax, National School of Engineers (ENIS), REsearch Groups in Intelligent Machines (REGIM), BP 1173, Sfax 3038, Tunisia [email protected], [email protected], [email protected]

Abstract Mobile Cloud Computing (MCC) is the combination between cloud computing and mobile devices. The challenge for mobile devices is to provide solutions for their limited resources, and it would be possible through cloud computing by running memory intensive operations on distant servers. This paper proposes a framework for code offloading based on big data analytics in mobile cloud environments. Keywords: Mobile Cloud Computing; Big Data; Offloading

1 Introduction Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction [1]. The cloud model is composed of five essential characteristics (On-demand self-service, Broad network access, Resource pooling, Rapid elasticity, Measured service, Resource usage), three service models (SaaS: Software as a Service, IaaS: Infrastructure as a Service and PaaS: Platform as a Service), and four deployment models (private cloud, community cloud, public cloud and hybrid cloud). Smart Mobile Devices (SMDs) are the future computing devices with high user expectations for accessing computational intensive applications. Mobile Cloud Computing (MCC) is simply to carry your office where you go through a Smartphone connected to the Internet. MCC aims to overcome many limitations like, computation and storage capacity, energy, shared wireless medium, by integrating cloud computing into the mobile environment to elastically utilize resources. The amount of data stored and processed on the Internet nowadays exceeds the processing capacity of modern computer systems, which gave the birth of the term big data. *

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292 Selection and peer-review under responsibility of the Scientific Programme Committee of INNS-BigData2015 c The Authors. Published by Elsevier B.V.

doi:10.1016/j.procs.2015.07.306

An offloading framework in MCC

H. Kchaou, Z. Kechaou and A.M. Alimi

2 Mobile Cloud Computing Mobile Cloud Computing is a rich mobile computing technology that leverages unified elastic resources of varied clouds and network technologies toward unrestricted functionality, storage, and mobility to serve a multitude of mobile devices anywhere, anytime through the channel of Ethernet or Internet regardless of heterogeneous environments and platforms based on the pay-as-you-use principle [6]. Another definition refers MCC as the set of techniques that use cloud resources to empower mobile applications. The main goal is to provide a better experience for mobile users whose devices have limited resources and capacities like computation, storage and battery [12 and 13]. MCC is the most recent mobile computing technology that uses cloud-computing technology to achieve two main goals: x x

Conserve native resources, especially battery while performing intensive application/computation. Augment mobiles’ computing power to enable them perform tasks and run computations that could not be otherwise done. Cloud-based mobile devices can perform infinite computation using infinite elastic cloud-based resources.

With the explosive growth in mobile applications, platforms and end user demands, limitations at the mobile device (e.g.: computation and storage capacity, energy, shared wireless medium) impede further improvements in application quality of service (QoS). Mobile cloud applications [9] are considered as the next generation of mobile applications, due to their promise of linked and elastic computational cloud functionality that enables to augment their processing capabilities on demand, power-aware decision mechanisms that allow to utilize efficiently the resources of the device and dynamic resource allocation approaches that allow to program and utilize cloud services at different levels (SaaS, IaaS, PaaS).

3 Big Data Big data is a term utilized to refer to the increase in the volume of data that are difficult to store, process, and analyze through traditional database technologies [3]. Big data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis [2]. This topic has appeared, as organizations must deal with petabyte-scale collections of data. In fact, in the last 2 years we have produced 90% of the total data generated in history [7]. Therefore, as mentioned in [4], Big Data technology aims to minimize hardware and processing costs and to verify the value of Big Data before committing significant company resources. We also can define Big data in the form of architecture, inspired of [7], and which we propose in Figure 1. It is consisting of four main components namely: x x x x

Data Sources, which include databases, sensors, mobiles, web, etc. Data Management, which include Distributed file system (HDFS), parallelize computing (Map Reduce), Data storage (NoSQL), Data cleaning, Data security, etc. Data Analytics, which refers to Data mining, Machine learning, Statistics, Network analysis, etc. Applications.

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Figure 1: Big Data Architecture

4 Cloud Computing and Big Data In this section, we will set up the relationship between Cloud Computing and Big Data. Then we conduct a study on the existing infrastructures to access big data effectively via mobile devices.

4.1. Relationship Big data and cloud computing are currently receiving more and more attention from both industry and academia [8]. To solve the problems of big data cloud computing is seen as the most powerful platform. Cloud computing provides a powerful, flexible and elastic platform which enables collection, analytics, processing and visualization of Big Data [5]. The storage of Big Data is realized by file systems that determine standardized methods such as MapReduce. Refers to [3], Cloud computing and big data are conjoined. Big data provides users the ability to use commodity computing to process distributed queries across multiple datasets and return resultant sets in a timely manner. Cloud computing provides the underlying engine through the use of Hadoop, a class of distributed data-processing platforms.

4.2. Big Data access via Mobile devices Access to Big Data in the cloud through mobile devices (termed Mobile-Cloud Computing) significantly expands the reach of Big Data due to the widespread availability of smartphones and tablets [5].

5 Review of Offloading Frameworks for MCC References [14] defines Offloading that is a solution to alleviate resource limitations on mobile devices and provide improved capabilities for these devices by migrating partial or full computations (code, status and data) to more resourceful computers. Three questions should been asked which are:

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x x

x


What to offload: before offloading, the program needs to be partitioned. When to offload: applications may have different requirements on performance and mobile devices may have different capabilities and energy concerns. Offloading decisions need to be made based on different target goals, such as improving performance and/or saving energy, or, reducing the network overhead. How to offload: the development of virtualization and the emerging cloud computing technologies provides a powerful, flexible, manageable and secure platform for offloading.

MCC uses the computational power of cloud data centers by offloading the burden cloud computing individual server nodes [6, 11, 15 and 16]. Contemporary computational Offloading Frameworks implement computational offloading at different granularity levels, such as at the object, class, component, bundle, thread, method and task levels [10]. Other frameworks [17 and 18], uses offloading for the migration of the entire module of the application.

6 Proposed Framework In this section, we propose our framework inspired of the work of [3]. Reference [3] presents a framework, which demonstrate the use of cloud computing in big data. And, we presented in this work the use of Mobile Cloud Computing in big data. As shown in Figure 2, large data sources from the cloud and Web are stored in a distributed faulttolerant database and processed through a programing model for large datasets with a parallel distributed algorithm in a cluster. The main purpose of data visualization is to view analytical results presented visually through different graphs for decision making. Big data utilizes distributed storage technology based on cloud computing rather than local storage attached to a computer or electronic device. Mobile smart devices and smart devices are connected to cloud computing via network connection. Our proposed framework implement computational offloading at different levels.

Figure 2: Mobile Cloud Computing use in Big Data

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7 Conclusion and Future Work This paper presents two major axes of research, which are Mobile Cloud Computing and Big Data. Then, it presents relationship between them. Moreover, it proposes a framework for offloading based on big data analytics in mobile cloud environment. In the future, we will present a data analytics comparison between different technics and algorithms of machine learning. In addition, we will present results for a GPS application based on the proposed framework.

Acknowledgements The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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