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.

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 An Efficient Dynamic-Decision Based Task Scheduler for Task Offloading Optimization and Energy Management in Mobile Cloud Computing

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4527
Author(s):  
Abid Ali ◽  
Muhammad Munawar Iqbal ◽  
Harun Jamil ◽  
Faiza Qayyum ◽  
Sohail Jabbar ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Task Scheduling ◽  
Time Management ◽  
Mobile Cloud Computing ◽  
Fog Computing ◽  
Mobile Cloud ◽  
Computational Performance ◽  
Task Offloading

Restricted abilities of mobile devices in terms of storage, computation, time, energy supply, and transmission causes issues related to energy optimization and time management while processing tasks on mobile phones. This issue pertains to multifarious mobile device-related dimensions, including mobile cloud computing, fog computing, and edge computing. On the contrary, mobile devices’ dearth of storage and processing power originates several issues for optimal energy and time management. These problems intensify the process of task retaining and offloading on mobile devices. This paper presents a novel task scheduling algorithm that addresses energy consumption and time execution by proposing an energy-efficient dynamic decision-based method. The proposed model quickly adapts to the cloud computing tasks and energy and time computation of mobile devices. Furthermore, we present a novel task scheduling server that performs the offloading computation process on the cloud, enhancing the mobile device’s decision-making ability and computational performance during task offloading. The process of task scheduling harnesses the proposed empirical algorithm. The outcomes of this study enable effective task scheduling wherein energy consumption and task scheduling reduces significantly.

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 cloud is the infrastructure that facilitates the offloading of storage and computing resources of mobile devices pertaining mobile applications to cloud computing. Mobile devices can run expensive applications using mobile cloud as they can outsour

2018 ◽  
Vol 1 (22) ◽  
pp. 759-772
Author(s):  
Riyadh R. Nuiaa
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Pervasive Computing ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Cloud Infrastructure ◽  
Security Issues ◽  
Secure Channels ◽  
Cloud Users

Mobile cloud is the infrastructure that facilitates the offloading of storage and computing resources of mobile devices pertaining mobile applications to cloud computing. Mobile devices can run expensive applications using mobile cloud as they can outsource services to cloud while providing interface for mobile users. Emerging mobile applications that are expensive can overcome the inherent problems of hand held devices through the concept of mobile cloud computing. The offloading process provide mobiles a rich platform for pervasive computing with on-demand services linked to cloud computing through mobile cloud infrastructure. Thus the mobile cloud computing is an inevitable phenomenon which bring about plethora of pros besides the mobility. The mobile cloud users can perform their resource intensive operations on the fly without time and geographical restrictions. In spite of the advantages it bestows mobile cloud computing has its own security issues. This paper throws light into the security issues and solutions in terms of secure channels transmission in mobile cloud computing. In this paper, we present state-of-the-art of mobile cloud computing besides its security aspects that are to be taken care of for successful mobile cloud computing.

scholarly journals An Efficient Agent Based Offloading Decision Maker For Mobile Cloud Computing

2021 ◽  
Author(s):  
SHANTHI THANGAM MANUKUMAR ◽  
Vijayalakshmi Muthuswamy ◽  
Bushra H
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Decision Maker ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Cost Effective ◽  
Vital Role ◽  
Mobile Cloud ◽  
Agent Based ◽  
Data Intensive

Abstract The usages of mobile devices are drastically increasing every day with high end support to the users. The high end configurations mobile devices such as smart phones, laptops, tablets, etc., computations are complex in these devices. Computation intensive and data intensive are plays a vital role in the mobile devices. The main challenges in the mobile devices are handling the mobile applications in the devices with high computation and high storage. The above mentioned challenges can be overcome by using mobile cloud computing. The limitations while handling the mobile cloud computing is offloading decision making, which part of computation should offload and which should execute in the mobile side. The proposed work provides the solution to the limitations and challenges mentioned earlier by providing agent based offloading decision maker for mobile cloud. The decision maker should decide which computation part is executed in the mobile side and the cloud side. The evaluation shows the mobile applications having high complexity get benefited over other high applications. The proposed system achieves the better response time, low latency, cost-effective and minimizes the energy consumed by data-intensive and computational-intensive mobile applications.

scholarly journals Enabling Efficient Decentralized and Privacy Preserving Data Sharing in Mobile Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jiawei Zhang ◽  
Ning Lu ◽  
Teng Li ◽  
Jianfeng Ma
Keyword(s):  
Cloud Computing ◽  
Access Control ◽  
Mobile Devices ◽  
Data Sharing ◽  
Mobile Cloud Computing ◽  
High Efficiency ◽  
Mobile Cloud ◽  
User Privacy ◽  
Access Policy ◽  
Fine Grained

Mobile cloud computing (MCC) is embracing rapid development these days and able to provide data outsourcing and sharing services for cloud users with pervasively smart mobile devices. Although these services bring various conveniences, many security concerns such as illegally access and user privacy leakage are inflicted. Aiming to protect the security of cloud data sharing against unauthorized accesses, many studies have been conducted for fine-grained access control using ciphertext-policy attribute-based encryption (CP-ABE). However, a practical and secure data sharing scheme that simultaneously supports fine-grained access control, large university, key escrow free, and privacy protection in MCC with expressive access policy, high efficiency, verifiability, and exculpability on resource-limited mobile devices has not been fully explored yet. Therefore, we investigate the challenge and propose an Efficient and Multiauthority Large Universe Policy-Hiding Data Sharing (EMA-LUPHDS) scheme. In this scheme, we employ fully hidden policy to preserve the user privacy in access policy. To adapt to large scale and distributed MCC environment, we optimize multiauthority CP-ABE to be compatible with large attribute universe. Meanwhile, for the efficiency purpose, online/offline and verifiable outsourced decryption techniques with exculpability are leveraged in our scheme. In the end, we demonstrate the flexibility and high efficiency of our proposal for data sharing in MCC by extensive performance evaluation.

Accelerating Mobile-Cloud Computing

2015 ◽  
pp. 1933-1955
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

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