Heterogeneous and Elastic Computation Framework for Mobile Cloud Computing

2014 ◽  
Vol 24 (07) ◽  
pp. 1013-1037 ◽  
Author(s):  
Chi-Sheng Shih ◽  
Joen Chen ◽  
Yu-Hsin Wang ◽  
Norman Chang
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Network Capacity ◽  
Physical Computation ◽  
Imprecise Computation ◽  
Cloud Servers ◽  
Real Time Computing ◽  
And Storage ◽  
Computing Literature

The number and variety of applications for mobile devices continue to grow. However, the resources on mobile devices including computation and storage do not keep pace with the growth. How to incorporate the computation capacity on cloud servers into mobile computing has been desired and challenge issues to resolve. In this work, we design an elastic computation framework to take advantage the heterogeneous computation capacity on cloud servers, which consist of CPUs and GPGPUs, to meet the computation demands of ever growing mobile applications. The computation framework extends OpenCL framework to link remote processors with local mobile applications. The framework is flexible in the sense that the computation can be stopped at any time and gains results, which is called imprecise computation in real-time computing literature. The framework has been evaluated against OpenCL benchmark and physical computation engine for gaming. The results show that the framework supports OpenCL benchmark, RODINIA, without modifying the codes with few exceptions. The elastic computation framework allows the cloud servers to support more mobile clients without sacrificing their QoS requirements. The experiment results also show that IO intensive applications do not perform well when the network capacity is insufficient or unreliable.

scholarly journals MCAF: Developing an Annotation-Based Offloading Framework for Mobile Cloud Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yilian Zhou ◽  
Ligang He ◽  
Bin Wang ◽  
Yi Su ◽  
Hao Chen
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Source Code ◽  
Cloud Infrastructure ◽  
Original Source ◽  
Automatic Integration ◽  
Android Applications ◽  
Reduce Energy Consumption ◽  
Cloud Servers

Offloading computation from mobile to remote cloud servers is a promising way to reduce energy consumption and improve the performance of mobile applications. However, a great challenge arises as automatic integration of powerful computing resources in remote cloud infrastructure and the portability of mobile devices. In this paper, we develop a Java annotation-based offloading framework, called MCAF, for android mobile devices. This framework is designed and committed to simplifying the development of android applications enabled with the offload capability. All the developers need to do is to import the SDK library of our MCAF and annotate the computation-intensive methods. MCAF can automatically extract the annotated source code and generate the code that will be run in the Cloud. Moreover, the codes of making the offloading decisions are automatically inserted into the original source code. We also conducted the real experiments to show the applicability of our MCAF.

scholarly journals Enhancing Offloading Systems with Smart Decisions, Adaptive Monitoring, and Mobility Support

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Paulo A. L. Rego ◽  
Fernando A. M. Trinta ◽  
Masum Z. Hasan ◽  
Jose N. de Souza
Keyword(s):  
Mobile Devices ◽  
Performance Improvement ◽  
Mobile Cloud Computing ◽  
Mobility Support ◽  
Data Intensive ◽  
Adaptive Monitoring ◽  
Computationally Intensive ◽  
Processing And Storage ◽  
Reduced Power Consumption ◽  
And Storage

Mobile cloud computing is an approach for mobile devices with processing and storage limitations to take advantage of remote resources that assist in performing computationally intensive or data-intensive tasks. The migration of tasks or data is commonly referred to as offloading, and its proper use can bring benefits such as performance improvement or reduced power consumption on mobile devices. In this paper, we face three challenges for any offloading solution: the decision of when and where to perform offloading, the decision of which metrics must be monitored by the offloading system, and the support for user’s mobility in a hybrid environment composed of cloudlets and public cloud instances. We introduce novel approaches based on machine learning and software-defined networking techniques for handling these challenges. In addition, we present details of our offloading system and the experiments conducted to assess the proposed approaches.

Mobile Cloud Computing

2015 ◽  
pp. 1-23
Author(s):  
Jyoti Grover ◽  
Gaurav Kheterpal
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Applications ◽  
Large Scale ◽  
Mobile Cloud Computing ◽  
Research Area ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Mobile Environment

Mobile Cloud Computing (MCC) has become an important research area due to rapid growth of mobile applications and emergence of cloud computing. MCC refers to integration of cloud computing into a mobile environment. Cloud providers (e.g. Google, Amazon, and Salesforce) support mobile users by providing the required infrastructure (e.g. servers, networks, and storage), platforms, and software. Mobile devices are rapidly becoming a fundamental part of human lives and these enable users to access various mobile applications through remote servers using wireless networks. Traditional mobile device-based computing, data storage, and large-scale information processing is transferred to “cloud,” and therefore, requirement of mobile devices with high computing capability and resources are reduced. This chapter provides a survey of MCC including its definition, architecture, and applications. The authors discuss the issues in MCC, existing solutions, and approaches. They also touch upon the computation offloading mechanism for MCC.

scholarly journals Mobile Cloud Computing as Competence Intensifier

2019 ◽  
Vol 8 (06) ◽  
pp. 24693-24697
Author(s):  
Neeta P. Sarode ◽  
Dr. J.W. Bakal
Keyword(s):  
Cloud Computing ◽  
Ubiquitous Computing ◽  
Mobile Devices ◽  
Mobile Learning ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Personal Digital Assistants ◽  
Mobile Cloud ◽  
Advantages And Disadvantages ◽  
Actual Existence

Since the arrival of mobile devices, such as Personal Digital Assistants (PDA’s), smartphones, tablets etc., and their amalgamation with cloud computing is bringing up and transforming ubiquitous computing into actual existence. This concept of ubiquitous computing straightens out the way to unusual and experimental applications, in which the mobile devices are integrated and provide assistance to the users. This paper discusses about the concept of mobile cloud computing, identify advantages and disadvantages of collaborating mobile applications with cloud and identify benefits of leveraging mobile learning services on cloud. Mobile cloud computing induces innumerable benefits and overcomes the technical limitations of mobile learning.

scholarly journals Resource Intensification for Mobile Devices Using the Approximate Computing Entities

2020 ◽  
Vol 2 (1) ◽  
pp. 26-36
Author(s):  
Dr. Suma V
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Approximate Computing ◽  
Local Resources ◽  
And Storage ◽  
High Storage

The mobile devices are termed to highly potential due to their capability of rendering services without being plugged to the electric grid. These device are becoming highly prominent due to their constant progress in computing as well as storing capacities and as they are very much closer to the users. Despites its advantages it still faces many problems due to the load balancing and energy consumption due to its limited battery limited and storage availability as some applications or the video downloading requires high storage facilities consuming majority of the energy in turn reducing the performance of the mobile devices. So as to improve the performance and the capability of the mobile devices the mobile cloud computing that integrates the mobile devices with the cloud paradigm has emerged as a promising paradigm. This enables the augmentation of the local resources for the mobile devices to enhance its capabilities in order to improve its functioning. This is basically done by proper offloading and resource allocation. The proposed method in the paper utilizes the optimal offloading strategy (Single and double strand offloading) and follows an Ant colony optimization based resource allocation for improving the functioning the mobile devices in terms of energy consumption and storage.

A Survey of Mobile Cloud Computing for Extending Energy of Mobile Devices

2014 ◽  
Vol 573 ◽  
pp. 549-555
Author(s):  
P. Thanapal ◽  
M.A. Saleem Durai
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Mobile Devices ◽  
Present State ◽  
Mobile Device ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Mobile Cloud

Mobile cloud computing will wear down gaining quality among users, the researchers predicts these troubles by execution of mobile applications on application suppliers external to the mobile device. During this paper, we have a tendency to gift a wide survey of mobile cloud computing, whereas prominence the particular considerations in mobile cloud computing square measure as follows. (a) Highlights the present state in Application of cloud computing usage in real time world. (b) Identifies the problems in testing bandwidth and (c) provides a optimizing of the offloading that saves energy

scholarly journals Mobile Edge Computing

2019 ◽  
pp. 29-31
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M. Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Battery Power ◽  
And Storage

Mobile applications are becoming increasingly computational intensive, while many mobile devices still have limited battery power and cannot support computational intensive tasks. Mobile edge computing (MEC) computing is an extension of edge computing, and it refers to computing at the edge of a network. In mobile edge computing, computing and storage nodes are placed at the Internet's edge near mobile devices. It places the edge clouds at the candidate locations. This paper presents a brief introduction to MEC.

scholarly journals Constraints Based Heuristic Approach for Task Offloading In Mobile Cloud Computing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Raj Kumari, Sakshi Kaushal
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Computation Time ◽  
Mobile Cloud ◽  
Delay Sensitive ◽  
Communication Time ◽  
Delay Tolerant ◽  
Task Offloading

Mobile devices are supporting a wide range of applications irrespective of their configuration. There is a need to make the mobile applications executable on mobile devices without concern of battery life. For optimizing mobile applications computational offloading is highly preferred. It helps to overcome the severity of scarce resources constraint mobile devices. In offloading, which part of the application to be offloaded, on which processor and what is available bandwidth rate are the main crucial issues. As subtasks of mobile applications are interdependent, efficient execution of application requires research of favorable wireless network conditions before to take the offloading decision. Broadly in mobile cloud computing the applications is either delay sensitive or delay tolerant. For delay sensitive applications completion time has the highest priority whereas for delay tolerant type of applications depending on the network conditions decision of offloading can be taken. Sometimes, computation time on a cloud server is less but it consumes high communication time which ultimately gives inefficient offloading results. To address this issue, we have proposed a heuristic based level wise task offloading (HTLO). It includes computation time, communication time and maximum energy available on the mobile device to take the decision of offloading. For simulation study, a mobile application is considered as a directed graph and all the tasks are executed on the basis of their levels. The overall results of the proposed heuristic approach are compared with state-of-the-art K-M LARAC algorithm and results show the improvement in execution time, communication time, mobile device energy consumption and total energy consumption.

scholarly journals Enhanced Adaptive Cloudlet Placement Approach for Mobile Application on Spark

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yiwen Zhang ◽  
Kaibin Wang ◽  
Yuanyuan Zhou ◽  
Qiang He
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile Application ◽  
Completion Time ◽  
Communication Delays ◽  
Effectiveness And Efficiency ◽  
Computationally Intensive ◽  
Traditional Approaches ◽  
And Storage ◽  
Geographical Locations

The applications of mobile devices are increasingly becoming computationally intensive while the computing capability of the user’s mobile device is limited. Traditional approaches offload the tasks of mobile applications to the remote cloud. However, the rapid growth of mobile devices has made it a challenge for the remote cloud to provide computing and storage capacities with low communication delays due to the fact that the remote cloud is geographically far away from mobile devices. Reducing the completion time of applications in mobile devices through the technical expending mobile cloudlets which are moving collocated with Access Points (APs) is necessary. To address the above issues, this paper proposes EACP-CA (Enhanced Adaptive Cloudlets Placement approach based on Covering Algorithm), an enhanced adaptive cloudlet placement approach for mobile applications in a given network area. We apply the CA (Covering Algorithm) to adaptively cluster the mobile devices based on their geographical locations, the aggregation regions of the mobile devices are identified, and the cloudlet destination locations are also confirmed according to the clustering centers. In addition, we can also obtain the traces between the original and destination locations of these mobile cloudlets. To increase the efficiency, we parallelize CA on Spark. Extensive experiments show that the proposed approach outperforms the existing approach in both effectiveness and efficiency.

scholarly journals A Novel Encryption and Decryption of Data using Mobile Cloud Computing Platform

2021 ◽  
Vol 2 (3) ◽  
pp. 118-122
Author(s):  
Dr. Jennifer S. Raj
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Data Sharing ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Security Level ◽  
Computing Platform ◽  
Fine Grain ◽  
Security Proof ◽  
Cloud Servers

As the need for super-fast mobile devices incorporating cloud computing technology continues to be the need of the hour, Mobile Cloud Computing (MCC) serves as the platform for mobile users to share data with others, store information on the cloud and also compute using the data. Over the years, the most widely preferred encryption that has proven to be reliable is Attribute Based Encryption (ABE). However, this encryption methodology requires expensive pairing operation which makes it unsuitable for MCC. As a result of this, MCC remains slow in reaching the crowd due to the challenge of resource-constrained mobile devices. To tackle this resource-constraint we propose a novel method of outsourcing operations to resource-rich cloud servers so that the constraint on resources does not hinder proper functioning of the mobile device. There are a number of advantages when data sharing is incorporated with lightweight fine-grain data sharing methodology. This method has a number of advantages such as CCA security level, resisting decryption key exposure and supporting verifiable outsourced decryption. Simulation results indicate that the performance analysis and concrete security proof is apt for MCC environment.

Sign in / Sign up

Export Citation Format

Share Document