IJRIT International Journal of Research in Information Technology, Volume 1, Issue 7, July 2014, Pg. 210-217

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Mobile data offloading (Android application) to cloud to save energy Ashwini G L Mtech student(Computer science) Vishweshwaraiah technological university Tumkur, Karnataka- 572106, India [email protected]

Harish H K Assistant Professor Vishweshwaraiah technological university Tumkur, Karnataka- 572106, India [email protected]

Abstract—A smartphone, or smart phone, is a mobile phone with more advanced computing capability and connectivity than basic feature phones. Modern smartphones include all of those features plus the features of a touch screen computer, including web browsing, Wi-Fi and 3rdparty apps. Smartphones are now capable of supporting a wide range of applications, many of which demand an ever increasing computational power. This poses a challenge because smartphones are resource-constrained devices with limited computation power, memory, storage, and energy. The cloud computing technology offers virtually unlimited dynamic resources for computation, storage, and service provision. Mobile cloud computing, with its promise to meet the urgent need for richer applications and services of resource-constrained mobile devices, is emerging as a new computing paradigm and has recently attracted significant attention. Cloud computing services to mobile devices to overcome the smartphones constraints. Many issues related to offloading have been investigated in the past decade. This article presents mobile cloud architecture, offloading decision affecting entities, the latest mobile cloud application models, Energy saving concept and their future research directions.

Keywords- Cloud computing, Mobile applications, Energy saving

1. INTRODUCTION Cloud computing is a new paradigm in which computing resources such as processing, memory, and storage are not physically present at the user’s location. Instead, a service provider owns and manages these resources, and users access them via the Internet. For example, Amazon Web Services lets users store personal data via its Simple Storage Service (S3) and perform computations on stored data using the Elastic Compute Cloud (EC2). This type of computing provides many advantages for businesses including low initial capital investment, shorter start-up time for new services, lower maintenance and operation costs, higher utilization through virtualization, and easier disaster recovery that make cloud computing an attractive option. Reports suggest that there are several benefits in shifting computing from the desk top to the cloud. What about cloud computing for mobile users? The primary constraints for mobile computing are limited energy and wireless bandwidth. Cloud computing can provide energy savings as a service to mobile users, though it also poses some unique challenges. Mobile systems, such as smart phones, have become the primary computing platform for many users. Various studies have identified longer battery lifetime as the most desired feature of such systems. In this project, we propose a mechanism to save mobile phone energy by offloading to cloud.

Fig. 1 Shows general computation offloading schemes in mobile wireless environments. Ashwini G L, IJRIT

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From the application layer perspective, the entire application or part of the code/data can be offloaded to the mobile cloud side to achieve better energy utility or better performance. The context and run time of the application process is still maintained by operating system in mobile host. In the meantime, the whole execution platform (application run time or infrastructure) can also be migrated to the cloud side, which indicates that the burden on programmers can be reduced. Advancements in computing technology have expanded the usage of computers from desktops and main- frames to a wide range of mobile and embedded applications, including surveillance, environmental sensing, GPS navigation, mobile phones, autonomous robots, etc. Many of these applications run on systems with limited resources. For example, mobile phone save battery powered. Environmental sensors have small physical sizes, slow processors, and small amounts of storage. Most of these applications use wireless networks and their bandwidths are orders-of-magnitude lower than wired networks. Meanwhile, increasingly complex programs are running on these systems for example, video processing on mobile phones and object recognition on mobile robots. Thus there is an increasing gap between the demand for complex programs and the availability of limited resources. Offloading is a solution to augment these mobile systems capabilities by migrating computation to more resourceful computers (i.e., servers). This is different from the traditional client-server architecture, where a thin client always migrates computation to a server. Computation offloading is also different from the migration model used in multiprocessor systems and grid computing, where a process may be migrated for load balancing [1]. The key difference is that computation offloading migrates programs to servers outside of the users immediate computing environment; process migration for grid computing typically occurs from one computer to another within the same computing environment, i.e., the grid. Offloading is in principle similar to efforts like SETI@home [2], where requests are sent to surrogates for performing computation. The difference is that SETI@home is a large scale distributed computing effort involving several thousands of users, whereas offloading is typically used to augment the computational capability of a resource constrained device for a single user. The terms “cyber foraging” and “surrogate computing” are also used to describe computation offloading. In this paper, we use the above terms interchangeably.

2. RELATED WORK Many related researches have been take place on offloading the data. The decision of where to place the execution (local or remote mode) should be anyway made based on the quantity of computation and communication that is required by the application. A little amount of communication combined with a large amount of computation should be performed preferably in remote mode, while a large amount of communication combined with a little amount of computation should be performed preferably in local mode. Face recognition a sample application[3] to evaluate the tradeoff of offloading computation with the intuitive idea of the required intensive calculus puts in commitment the hardware features of the mobile device. Whereas that, if the same calculus are executed by other systems with better hardware features, these processes are realized with less effort and in much less time. Analyzing the intensive calculus dividing it in sub processes that are distributed between the mobile device and the cloud infrastructure using a cascade of classifiers based on the Adaboost algorithm [4] to detect the presence of faces in an image and the Eigenfaces algorithm [5] to make the training and recognition of these faces. Finally, emulating the wireless channel between the mobile device and the cloud server to view how the end-to-end response time can affect at application. And also emulating allows to find limitations where we can get advantage with the use of this technique. When comes to the energy saving concept Power Booter, an automated power model construction technique[6] that uses builtin battery volt- age sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe Power Tutor, a component power management and activity state introspection based tool that uses the model generated by Power Booter for online power estimation. Power Booter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants. Combined, Power Booter and Power Tutor have the goal of opening power modeling and analysis for more smartphone variants and their users.

3. PROBLEM STATEMENT With the rapid development of mobile networking and device capability, energy efficiency becomes an important design consideration due to the limited battery life of mobile terminals. Processing energy cost by CPU is one of the most significant power consuming components in mobile terminals. The emergence of mobile cloud computing (MCC) provides the opportunity to save processing energy through the way of offloading computation tasks to remote server(s). For offloading to get maximum energy conservation, a way to split the task & identify which task to run in cloud & which has to be run in mobile phone has to be found. The advances in technology of the last decades have un- doubtedly turned yesterday’s must-have devices into today’s stock. Think of the phones with aerials of the late ’80, or the Pentium 4 PCs of a few years ago. None of them is comparable to the power of nowadays smartphones, whose recent worldwide market boost is undeniable. We use smartphones to do many of the jobs we used to do on desktops, and many new ones. We browse the Internet, send emails, organize our lives, watch videos, Ashwini G L, IJRIT

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upload data on social networks, use online banking, find our way by using GPS and online maps, and communicate in revolutionary ways. New apps are coming out at an incredible pace. Apple iPhone commercial’s call to action “There’s an app for everything” says a lot on this matter. Nonetheless, the more eager we get when using our smart phones by installing new apps, the less happy weare with the lifetime of the battery. The problem is that we rely upon a number of crucial pieces of information that are only stored in the device (phone numbers, addresses, notes, appointments, etc.), or, in some cases, that can be got only by using the Internet on the fly as many of us are used to do. It is so important to keep our smartphone operational that everyday we pay attention to our battery and try to save it by reducing the number of phone calls, or by avoiding to watch too many videos, just enough to be able to reach home and recharge it. But that means that we cannot use our device to the fullest. Many researchers believe that cloud computing is an excel- lent candidate to help reduce battery consumption of smartphones, as well as to backup user’s data. Indeed, many recent works have focused on building frame works that enable mobile computation offloading to software clones of smartphones on the cloud (see [7], [8], [9] among others), as well as to backup systems for data and applications stored in our devices [10], [11], [12]. Both mobile computation offloading and data backup involve communication between the real device and the cloud.

4. SYSTEM MODEL In the last two decades, there have been many attempts to enable mobile devices to use remote execution for the purpose of improving energy efficiency and application performance. These approaches reduce application execution time on mobile devices, thus decreasing the energy consumption of both CPU and memory. These attempts could be classified into two approaches discussed in the following paragraph. The first approach involves fine-grained, energy-aware offloading of mobile code to the infrastructure. This approach relies on programmers to modify the program to handle partitioning, state migration, and adaptation to various changes in network conditions. Application can offload only part of the methods which benefits from remote execution to gain large energy savings. For instance, a media streaming application contains a decoder part and a video player part, with the former being a CPU-intensive part (and thus mainly consuming energy for the CPU and memory). As such, we could simply offload this CPU-intensive part without offloading any of the screen intensive part. The second approach is coarse-grained task offloading scheme in which full process/program or full virtual machine is migrated to the infrastructure, and then programmers do not have to modify the application source code to take advantage of computation offloading. This approach reduces the burden placed on programmers. However, some parts of the program (e.g. those that are user interactive part) may not benefit from remote execution. Additionally, whole program migration may result in additional transmission overhead. The key issue of fine-grained offloading is program partitioning. Partitioning brings extra overhead and the partitioning algorithm directly influences the efficiency of the offloading. As manual partitioning puts much burden on programmers and cannot adapt to changes of network conditions very well, it’s not practical. Therefore, developing a partitioning algorithm to automatically calculate the approximated partitioning solution is more reasonable and has been widely explored. However, during the process of determining an appropriate partitioning, extra computing cost is involved. The performance of partitioning algorithm relies much on the dynamic estimation of communication energy consumption. 4.1 Design goals Firstly to implement the offloading solution for image clustering, secondly to measure the energy consumtion locally and offloading to cloud and then to take decision for completion of the task with or without offloading. Here initially the program executes on the portable client with a timeout, if the computation is not completed after the timeout, it is offloaded to the server. The timeout is first set to be the minimum computation time that can benefit from offloading. It further consider collecting online statistics of the computation time to find the statistically The observed result indicates that up to 51% energy is saved when large tasks are offloaded, which means larger tasks (as determined by the number of computation instructions) are likely to benefit from offloading significantly. In the meantime, they

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also showed that more efficient ways of moving the data have the ability to provide greater savings.

5. PROPOSED SCHEME A survey last year by Change Wave Research4 revealed short battery life to be the most disliked characteristic of Apple’s iPhone 3GS, while a 2009 Nokia poll showed that battery life was the top concern of music phone users. Many applications are too computation intensive to perform on a mobile system. If a mobile user wants to use such applications, the computation must be performed in the cloud. Other applications such as image retrieval, voice recognition, gaming, and navigation can run on a mobile system. However, they consume significant amounts of energy. . Offloading computation to save energy: Sending computation to another machine is not a new idea. The currently popular client-server computing model enables mobile users to launch Web browsers, search the Internet, and shop online. What distinguishes cloud computing from the existing model is Instead of service providers managing programs running on servers, virtualization allows cloud vendors to run arbitrary applications from different customers on virtual machines. Cloud vendors thus provide computing cycles, and users can use these cycles to reduce the amounts of computation on mobile systems and save

energy. Thus, cloud computing can save energy for mobile users through computation

Fig 2. Shows the data offloading to cloud for computation

5.1 Notation & preliminaries k-means clustering is a method of vector quantization, originally

Fig 3.Shows k-means from signal processing, that is popular for cluster analysis in image processing. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean[13] serving as a prototype of the cluster. Given a set of observations (x1, x2, …, xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition [14] the n observations into k sets (k ≤ n) S = {S1, S2, …, Sk} so as to minimize the within-cluster sum of squares (WCSS): 5.2 Basic solution In this solution the program is initially executed on the portable client with a timeout. If the computation is not completed after the timeout, it is offloaded to the server. The timeout is first set to be the minimum computation time that can benefit from offloading. It further consider collecting online statistics of the computation time to find statistically. Later by considering the online statistics we can decide where much energy is consuming. 5.3 Our construction System architecture is the conceptual design that defines the structure and behaviour of a system. An architecture description is a formal description of a system, organized in a way that supports reasoning about the structural properties of the system.

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Fig 4. System architecture Data flow diagram (DFD) can also be used for the visualization of data processing (structured design). On a DFD, data items flow from an external data source or an internal data store to an internal data store or an external data sink, via an internal process. Level 0 At level 0 the interaction between the system and external agents which act as data sources and data sinks. On the context diagram (also known as the Level 0 DFD) the system's interactions with the outside world are modeled purely in terms of data flows across the system boundary. The context diagram shows the entire system as a single process, and gives no clues as to its internal organization Level 1 At Level 1 the system is divided into sub- systems (processes), each of which deals with one or more of the data flows to or from an external agent, and which together provide all of the functionality of the system as a whole. It also identifies internal data stores that must be present in order for the system to do its job, and shows the flow of data between the various parts of the system. 5.4 Performance analysis We now assess the performance of the proposed energy saving in android devices using cloud computing schemes to show that they are indeed lightweight. We will focus on the cost of the efficiency of the battery life and our proposed k-means technique. The experiment is conducted using java on a Jdk1.6 with an Intel Intel 2.1GHZ processor,4gb memory,LAN interface, Android device and a cloud platform.

Fig 5. Shows performance analysis

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5.5 Implementation procedure

Fig 6. Shows where to go for computation

Fig 7. Shows similar images

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Fig 8. Shows which is better cloud or mobile? Fig 9. Shows if cloud is better means, it will remove the option of mobile computation.

6.

CONCLUSION

Cloud computing can potentially save energy for mobile users. Mobile cloud computing services would be significantly different from cloud services for desktops because they must offer energy saving. Growth of complex applications to mobile devices with support of cloud computing infrastructure demands better understanding and battery life time. The computation in mobile devices require more battery compare to cloud. For this reason in this paper, we presented an application designed to save battery life time of a device by offloading data to cloud. From the obtained results, we consider that offloading computation from mobile devices to cloud computing infrastructure can be done easily.

7. ACKNOWLEDGMENTS This work was supported in part of my Mtech course, All the faculties of my institution supported well to complete this paper, so I would like to thank my guide and all faculties.

8. REFERENCES [1]

PowellML,MillerBP(1983)Processmigrationindemos/mp. ACM SIGOPS Oper Syst Rev 17(5):110–119

[2]

Anderson DP, Cobb J, Korpela E, Lebofsky M, Werthimer D (2002) SETI@ home: an experiment in public-resource computing. Commun ACM 45(11):56–61

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Jorge Luzuriaga, Juan Carlos Cano, Carlos Calafate, Pietro Manzoni, “Evaluating Computation Mobile Cloud Computing:ASample Application”, 2013 IEEE paper

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OpenCV, “Opencv v2.4.3 documentation,” URL: http://docs.opencv.org/modules/ml/doc/boosting.html, [retrieved: 03, 2013].

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L. Lorente Gim´enez, “Representaci´on de caras mediante eigenfaces,” in Buran, vol. n´um. 11, 1998, pp. 13–20.

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Lide Zhang, Birjodh Tiwana, Zhiyun Qian, Zhaoguang Wang, Robert P. Dick, Z. Morley Mao,Lei Yang, “Accurate Online Power Estimation and Automatic Battery Behavior Based Power Model Generation for Smartphones” , 2010 IEEE paper

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OffloadingTrade-offsin

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S. Kosta, C. Perta, J. Stefa, P. Hui, and A. Mei, “Clone2clone (c2c): Enable peer-to-peer networking of smartphones on the cloud,” T-Labs, Deutsche Telekom, Tech. Rep. TR-SK032012AM, 2012.

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S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, “Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading.” in Proc. of IEEE INFOCOM 2012, 2012.

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E. Cuervo, A. Balasubramanian, D. Cho, A. Wolman, S. Saroiu, R. Chandra, and P. Bahl, “Maui: making smartphones last longer with code offload,” in Proc. of MobiSys ’10, 2010.

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I. Joe and Y. Lee, “Design of remote control system for data protection and backup in mobile devices,” in Proc. of ICIS 2011, 2011.

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V. Ottaviani, A. Lentini, A. Grillo, S. D. Cesare, and G. Italiano, “Shared backup & restore: Save, recover and share personal information into closed groups of smartphones,” in Proc. of IFIP NTMS 2011, 2011.

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C. Ai, J. Liu, C. Fan, X. Zhang, and J. Zou, “Enhancing personal information security on android with a new synchronization scheme,” in Proc. of WiCOM 2011, 2011.

[13]

http://en.wikipedia.org/wiki/K-means_clustering

[14]

Hamerly, G. and Elkan, C. (2002). "Alternatives to the k-means algorithm that find better clusterings". Proceedings of the eleventh international conference on Information and knowledge management (CIKM).

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