Mobile Computation Dynamic Offloading using Cloud

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1 Mobile Computation Dynamic Offloading using Cloud Pooja V. Bhokare 1 Chitra J. Patil 2 Assistant Professor, Computer Engineering Department SSBT s college of engineering and technology, Bambhori, post box no.94, Jalgaon , India Abstract The mobile cloud computing integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy).the Mobile devices are becoming global for online web services and applications. Many services available for the mobile devices, but the mobile devices have some limitation in the sense of computation low memory, limited battery. First, we present several challenges for the design of Mobile Cloud computing service. Second, a concept model has been proposed to analyze related research work. Third, we survey recent mobile cloud computing architecture, application & offloading, and generalize services for mobile. Keywords Offloading, Mobile computation, Cloud computing, Intensive applications. I. INTRODUCTION Today, mobile devices, such as smart phones, tablets, PDAs, and ipods, have become indispensable in our daily lives. With their growing popularity, users have come to rely more and more on them as their primary computation and communication devices, and are even beginning to expect functionality and performance similar to that from traditional computing devices such as desktops and workstations. Mobile devices (e.g., Smartphone, tablet pcs, etc) are increasingly becoming an essential part of human life as the most effective and convenient communication tools not bounded by time and place. Mobile users accumulate rich experience of various services from mobile applications (e.g., iphone apps, Google apps, etc), which run on the devices and/or on remote servers via wireless networks. The rapid progress of mobile computing becomes a powerful trend in the development of IT technology as well as commerce and industry fields. However, the mobile devices are facing many challenges in their resources (e.g., battery life, storage, and bandwidth) and communications (e.g., mobility and security). The limited resources significantly impede the improvement of service qualities. However, meeting such expectations on these devices is challenging due to several reasons. First, the mobility of these devices implies that they are battery- powered, limiting their power capabilities. As the devices become more popular and their use becomes more frequent, the increasing energy consumption becomes a key bottleneck in their computational capability. Second the computational power, in terms of processing and memory, is severely limited compared to traditional computers. Thus, these devices are limited in their ability to execute rich user applications involving extensive use of computer vision and graphics, speech recognition, or machine learning, which can be resource intensive. Mobile Cloud Computing at its simplest refers to an infrastructure where both the data storage and the data processing happen outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and mobile computing to not just Smartphone users but a much broader range of mobile subscribers. The concept of Mobile Cloud Computing (MCC) intends to make the advantages of Cloud Computing available for mobile users but will provide additional functionality to the cloud as well. Mobile Cloud Computing (MCC) will help to overcome limitations of mobile devices in particular of the processing power and data storage. It might also help to extend the battery life by moving the execution of commutation-intensive application to the cloud. However, a significant gain in battery stand-by Time will require that the wireless connectivity for the MCC operation is at least as energy-efficient as the state of the art. ISSN: Page 319

2 II.CHALLENGES OF MOBILE CLOUD COMPUTING Mobile Cloud Computing services are implemented in mobile wireless environment, incorporating several challenges such as the dependency on continuous network connections. Also Mobile Cloud Computing concepts rely on an always on Connectivity and will need to provide a scalable and high quality mobile access. A. Network latency and limited bandwidth in the mobile network: First, Mobile Cloud Computing may face the challenge from the transmission channel due to the intrinsic nature and constraints of wireless networks and devices. This is especially true when it comes to rich- internet and immersive mobile applications, e.g. On line gaming and augmented reality that require high- processing capacity and minimum network latency. These will most probably continue to be processed locally on powerful smart phones and mobile tablets. Mobile broadband networks generally require longer execution times for a given application to run in the cloud and network latency issues may deem certain applications and Services unfit for the mobile cloud. B. Various access scheme in mobile environments: Mobile Cloud Computing would be deployed in a heterogeneous access scenario with different radio access technologies such as GPRS, 3G, WLAN, WiMax. Mobile Cloud Computing requires wireless connectivity with the following features: Mobile Cloud Computing requires an always-on connectivity for a low data rate cloud control signalling channel. Mobile Cloud Computing requires an on-demand available wireless connectivity with a scalable link bandwidth. Mobile Cloud Computing requires a network selection and use that takes energy-efficiency and costs into account. The most critical challenge of Mobile Cloud Computing is probably to guarantee a wireless connectivity that meets the requirements of Mobile Cloud Computing with respect to scalability, availability, energy- and costefficiency. C. Elastic application models: Cloud Computing services are scalable, via dynamic provisioning of resources on a Fine-grained self-service basis near real-time, without users consideration for peal loads. This requirement is particularly important towards mobile cloud computing scenario. Mobile applications can be launched on the device or cloud, and can be migrated between them according to dynamic changes of the computing context or user preferences. Also, limited resource of mobile device will restrict application processing. Thus, elastic application model should be proposed to solve fundamental processing problem. D. Security and Privacy: Cloud computing users prove their identities with digital credentials, typically passwords and digital certificates. If an attacker could fake or steal these credentials, the cloud computing system will suffer from spoofing attacks. In mobile cloud computing, the problem is even severe because mobile devices often lack of computing power to execute sophisticated security algorithms. Moreover, it is difficult to enforce a standardized credential protection mechanism due to the variety of mobile devices. There are four basic approaches to saving energy and extending battery lifetime in mobile devices: 1) Adopt a new generation of semiconductor technology. As transistors become smaller, each transistor consumes less power. Unfortunately, as transistors become smaller, more transistors are needed to pro-vide more functionalities and better performance; as a result, power consumption actually increases. 2) Avoid wasting energy. Whole systems or individual components may enter standby or sleep modes to save power. 3) Execute programs slowly. When a processor s clock speed doubles, the power consumption nearly couples. If the clock speed is reduced by half, the execution time doubles, but only one quarter of the energy is consumed. 4) Eliminate computation all together. The mobile system does not perform the computation; instead, computation is performed somewhere else, thereby extending the mobile system s battery lifetime. We focus on the last approach for energy management. Transfer computation to another machine is not a new idea. Thus, cloud computing can save energy for mobile users through computation off-load. Virtualization, ISSN: Page 320

3 a fundamental feature in cloud computing, lets applications from different customers run on different virtual machines thereby providing separation and protection. In this paper we also tray to solve the problem of the proxy time consuming we will try to implement a web logic that will run on cloud in this way we can eliminate this approach to solve the timing and the response time problems. III.CLOUD SERVICES Generally, a cloud computing is a large-scale distributed network system implemented based on a number of servers in data centres. The cloud services are generally classified based on a layer concept. In the upper layers of this paradigm, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are stacked. Data centres layer: This layer provides the hardware facility and infrastructure for clouds. In data centre layer, a number of servers are linked with high-speed networks to provide services for customers. Typically, data centres are built in less populated places, with high power supply stability and a low risk of disaster. 1) Infrastructure as a Service (IaaS): IaaS is built on top of the data centre layer. IaaS enables the provision of storage, hardware, servers and networking components. The client typically pays on a per-use basis. Thus, clients can save cost as the payment is only based on how much resource they really use. Infrastructure can be expanded or shrunk dynamically as needed. The examples of IaaS are Amazon EC2 (Elastic Cloud Computing) and S3 (Simple Storage Service). 2) Platform as a Service (PaaS): PaaS offers an advanced integrated environment for building, testing Accepted in Wireless Communications and Mobile Computing Wiley and deploying custom applications. The examples of PaaS are Google App Engine, Microsoft Azure, and Amazon Map Reduce/Simple Storage Service. 3) Software as a Service (SaaS): SaaS supports a software distribution with specific requirements. In this layer, the users can access an application and information remotely via the Internet and pay only for that they use. Salesforce is one of the pioneers in providing this service model. Microsoft s Live Mesh also allows sharing files and folders across multiple devices simultaneously. Although the cloud computing architecture can be divided into four layers, it does not mean that the top layer must be built on the layer directly below it. For example, the SaaS application can be deployed directly on IaaS, instead of PaaS. IV.FRAMEWORK AND METHODOLOGY The mobile computation framework will provide the global accessing for any web enabled mobile platform and can be deployed easily on any backend platform. There are two components at the cloud end: the web agent and server. The web logic provides a gateway sandwiched between the mobile device and the cloud backend. In this case the web logic will perform the duty of proxy and stabiles the connection and respond for the services of the mobile agent. Mobile devices are communicating with the web logic using mobile application, and get connection to the cloud. So the direct communication wills stabiles between the mobile devices and cloud for the fast data access. The code is previously running in the cloud, the code is available in the storage area but not deploy, or the code is available only on the mobile and should be uploaded by the mobile device. So the web logic is not on the data communication path, which makes the web logic not a bottleneck. V.PROFILING METHODOLOGY The choice to contract out will be dependent both on the application characteristics (e.g., its computational requirements) and the current environment (e.g., the amount of remaining battery, network connectivity, etc.). Thus, in sort to make the right outsourcing choice, the application s feat and resource habit needs to be profiled under dissimilar environments. In our current implementation, five parameters are monitored and analysed in different execution configurations, i.e., input data size, CPU usage, memory usage, execution time and power consumption. Three execution modes are considered: i.e., Local (executed on the mobile), ISSN: Page 321

4 Remote- WIFI (outsource to the remote server through WIFI),and Remote-3G (Outsource to the remote server through Sprint 3G network). The system file /proc/stat and the Debug class can provide CPU and memory procedure for each process in the web enabled system. If the execution mode is Local, the total execution time is recorded. The result of the open source framework for mobile, it will increase the speed of the mobile computation that will help the mobile users to use mobile devices longer. It is a new concept for the mobile user to save the battery power and the speed up the computation power. VI.STRATEGY Our results highlight three different application patterns that impact the outsourcing strategy. Simple Blur and Simple Sharpen are computation intensive, which gains significant speedup and energy saving through computation outsourcing. Tile Filter and Gray Filer are computation non intensive, which gains no performance or energy improvement. Face Recognition demonstrates a case where offloading benefits depends on the execution environment (input data size and network connectivity). Thus, the decision to outsource depends heavily on the application characteristics and the execution environment. The offloading decision can be made based on the user s preference, i.e., low response time, long battery life, or both. A group of pre-defined preferencepolicies can be stored at the mobile device which can be selected by the user. Preference-policies include the desired operating range threshold for each metric (CPU usage, response time, power consumption, etc.) Generated based on the profiling data. For instance, if the users prefer to run more applications, then applications should offload as many operations as possible to the Cloud. If the user prefers long battery life or low response time, the mobile can make decision based on the preference-policy and the available resources. After the operation is offloaded to the remote server, the outsourcing controller will monitor the execution and adjust the offloading accordingly. We are building such a system. In some cases, outsourcing may be necessary simply to make an application execution feasible. For instance, the Android system has an upper limit of heap memory for each application, e.g., 24M for the Nexus One, so that it cannot execute applications consuming memory over this limit. In our experiments, an image over 300K cannot be processed by Simple Blur locally. Thus, offloading such memory-intensive applications to the Cloud can extend the computability of mobile devices. Besides the performance and energy benefits to the application being offloaded, outsourcing can also benefit other applications running on the mobile. Thus, it can share the same image manipulation server as other users. VII.ADVANTAGES OF MOBILE CLOUD COMPUTING A. Extending battery lifetime: Battery is one of the main concerns for mobile devices. Several solutions have been proposed to enhance the CPU performance, and to manage the disk and screen in an intelligent manner, to reduce power consumption. B. Improving data storage capacity and processing power: Storage capacity is also a constraint for mobile devices. MCC is developed to enable mobile users to store/access the large data on the cloud through wireless networks. C. Improving reliability: Storing data or running applications on clouds is an effective way to improve the reliability since the data and application are stored and backed up on a number of computers. This reduces the chance of data and application lost on the mobile devices D. Dynamic provisioning: Dynamic on-demand provisioning of resources on a fine-grained, selfservice basis is a flexible way for service providers and mobile users to run their applications without advanced reservation of resources. E. Scalability: The deployment of mobile applications can be performed and scaled to meet the unpredictable user demands due to flexible resource provisioning. Service providers can easily add and expand an application and service without or with little constraint on the resource usage. F. Multi-tenancy: Service providers (e.g., network operator and data centre owner) can share the ISSN: Page 322

5 resources and costs to support a variety of applications and large number of users. G. Ease of Integration: Multiple services from different service providers can be integrated easily through the cloud and the Internet to meet the users demands. VIII.APPLICATIONS OF MOBILE CLOUD COMPUTING I. Mobile Commerce: Mobile commerce (m-commerce) is a business model for commerce using mobile devices. The m-commerce applications generally fulfil some tasks that require mobility (e.g., mobile transactions and payments, mobile messaging, and mobile ticketing). II. Mobile Learning: Mobile learning (m-learning) is designed based on electronic learning (e-learning) and mobility. However, traditional m-learning applications have limitations in terms of high cost of devices and network, low network transmission rate, and limited educational resources. III. Mobile Healthcare: The purpose of applying MCC in medical applications is to minimize the limitations of traditional medical treatment (e.g., small physical storage, security and privacy, and medical errors. IV. Mobile Gaming: Mobile game (m-game) is a potential market generating revenues for service providers. M-game can completely offload game engine requiring large computing resource (e.g., graphic rendering) to the server in the cloud, and gamers only interact with the screen interface on their devices. IX.CONCLUSION As we seen, Mobile cloud computing is one of mobile technology trends in the future since it combines the advantages of both mobile computing and cloud computing, thereby providing optimal services for mobile user. In this paper, we have shown the mobile Computation using cloud, we come with the outcome that we can implement the new offloading concept in the generalized form that would help the user to use mobile cloud very interactive and easy to use the services of the WWW. We are unaware of any other live experimental analysis to date. The results show that offloading compute intensive applications to the cloud can greatly reduce the response time and battery consumption. With WIFI network, it can achieve more than 28_ speedup and 9.5_ power efficiency. The system can make decisions dynamically and deploy the services. The user can define their own preferences as well, e.g., save power, improve performance or both. Since the open source framework is much more computationally powerful, the mobile client and the cloud server can work on different data sets for the fidelity concern. For instance, the image processing application can perform on a coarse grain scale while the cloud server can work on a finer grain scale. Thus when the user needs quick, less accurate results, then the user can execute it locally on the mobile. In this way, we have discussed about mobile offloading using cloud. ACKNOWLEDGMENT Our thanks to the experts who have contributed towards development of the paper. X.REFERENCES [1] Chong lei Mei, James Shimek, Chenyu Wang, Aphis her Chandra, and Jon Weismann Department of Computer Science and Engineering University of Minnesota, Twin Cities Dynamic Outsourcing Mobile Computation to the Cloud March 23, 2011 [2] Andreas Klein, Christian Mannweiler, Joerg Schneider and Hans D. Schotten Chair for Wireless Communications and Navigation University of Kaiserslautern, Germany Access Schemes for Mobile Cloud Computing /10 $ IEEE. [3] Karthik. Kumar and Yung-Hsiang Lu, Purdue University /10/$ IEEE Published by the IEEE Computer Society APRIL 2010 [4] Sheng Xiao and Weibo Gong. Mobility Can Help: Protect User Identity with Dynamic Credential. in Mobile Data Management (MDM), 2010 Eleventh International Conference on [5] Dynamic Outsourcing Mobile Computation to the Cloud Chonglei Mei, James Shimek, Chenyu Wang, Abhishek Chandra, and Jon Weissman March 14, 2011 [6] Karthik Kumar and Yung-Hsiang Lu, Purdue University [7] M. Satyanarayanan, Mobile computing: the next decade, in Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond (MCS), June ISSN: Page 323

Kusum Lata, Sugandha Sharma

Kusum Lata, Sugandha Sharma International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2017 IJSRCSEIT Volume 2 Issue 4 ISSN : 2456-3307 A Survey on Cloud Computing and Mobile Cloud Computing