A Study on Task Scheduling in Mobile Cloud Computing (original) (raw)
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A REVIEW ON TASK SCHEDULING IN MOBILE CLOUD COMPUTING ENVIRONMENT
IJCSMC, 2018
Cloud computing is a recent and upcoming technology which includes various areas. Due to some inherent defects of mobile devices, such as limited battery energy, insufficient storage space, mobile applications are confronted with many challenges in mobility management, quality of service (QoS) insurance, energy management and security issues, which has stimulated the emergence of many computing paradigms, such as Mobile Cloud Computing (MCC), Fog Computing, etc. Mostly one network application can be decomposed into fine-grained tasks which consist of sequential tasks and parallel tasks. With the assistance of mobile cloud computing, some tasks could be offloaded to the cloud for speeding up executions and saving energy. Maintaining energy conservation the efficiency of energy has become a major problem with increased usage of devices consuming more energy due to MCC paradigms allow to offload some tasks to the cloud for execution. To manage this problem task are schedule in both at the mobile device and in the mobile cloud. Task scheduling is taken as the factor to reduce consumption of energy. Tasks can be assigned and scheduled based on the algorithms and so energy can be conserved.
A New Approach for Task Scheduling Optimization in Mobile Cloud Computing
Lecture Notes in Electrical Engineering, 2014
Mobile cloud computing is growing rapidly because its device (i.e., smart phone) is becoming one of the main processing devices for users nowadays. However, there are still some negative impacts that affect cloud access, especially when access to cloud becomes expensive but recent studies are not yet efficient in eliminating these. In this paper, we present an effective task scheduling by collaborating thick-thin clients and cloud to guarantee a better accessibility to cloud network and boost up the processing time in the mobile cloud platform while considering the network bandwidth and cost for cloud service usage. Intensive simulation proves that our method can improve the task scheduling efficiency and is better cost-effective than other works.
An Optimal Task Scheduling Mechanism for Mobile Cloud Computing
International Journal of Computer Applications, 2017
Mobile devices limited storage and computation capabilities are largely affected by the compute intensive, resource intensive or energy drain applications. These limitations of the mobile devices can be eliminated with the help of mobile cloud computing by delivering the energy drain or computing intensive parts of the task to more resourceful resources and receiving the result from the resources. This process (a.k.a code offloading) helps the mobile device to increase performance and reduce energy consumption. We have proposed an optimal task scheduling code offloading mechanism which optimally identify the remote executable tasks and also identify the remote VM to execute the task. We have also proposed some greedy algorithms to evaluate the result of our proposed task scheduling algorithm. Herein, the local execution time, maximum allowable time and communication latency information and scheduling a task based on the remote VM execution time and queuing time etc. information are served to the master cloud. In our experimentation, we have used an image processing application to validate the proposed system. From our evaluation we show that tasks executed on high capacity VM improve the overall execution time comparing with local mobile device
Mobile cloud computing is a developing field in parallel processing and distributed computing region. Mobile cloud computing familiarity is exponentially greater because of its characteristics like on-request benefit, versatility, adaptability, and security. Cloud encourages both computational and storage service to its clients. This decreases maintenance and deployment cost support for any organization. Therefore, cloud computing has expanded significantly. To be specific, cloud service providers (CSP) necessities the resource utilization an ideal way. To make use of resource effectively, scheduling taskplays a significant role. Scheduling helps in allocating the tasks in the cloud environment. The task scheduler orchestrates tasks in queue for accessible associated assets. Furthermore, the created portable information movement has been violently developing and has turned into a serve load on versatile system administrators. To address such a confront in versatile systems, a successful approach is to managing data traffic by utilizing advanced technologies (e.g., Wi-Fi network, small cell network, so on) to accomplish portable data offloading This course of action benefits cloud service providers to accomplish most extreme execution in cost effective way. Here, a broad investigation of some scheduling algorithm that plans to diminish the energy consumption, while assigning different tasks in mobile cloud condition is finished. The merits and demerits of these existing algorithms are further identified.
Tasks Oriented Round Robin Scheduling Model for Mobile Cloud Computing
2019
Data offloading helps to send computation intensive part of a mobile application tasks to the cloud, a resource rich environment, for execution and after the processing, the result is sent back to the mobile devices thereby minimizing the execution time and computational cost. A lot of these tasks having different requirements and nature compete for resources in the cloud; therefore, effective and clever scheduling method is required. There have been a lot of scheduling research works in Mobile Cloud Computing (MCC) but most have been to minimize execution time and energy consumption, little consideration has been given to how more sensitive and important are some tasks over others. This often leads to application failure of some critical mission and delay sensitive mobile applications; this endangers vital processes even lives. In this work, we developed a model to bridge this gap by giving cognizance to how more important and delay sensitive some applications’ tasks are and at the...
Energy Efficient Task Scheduling in Mobile Cloud Computing
Lecture Notes in Computer Science, 2013
Cloud computing can enhance the computing capability of mobile systems by offloading. However, the communication between the mobile device and the cloud is not free. Transmitting large data to cloud consumes much more energy than processing data in mobile device, especially in a low bandwidth condition. Further, some processing tasks can avoid transmitting large data between mobile device and server. Those processing tasks (encoding, rendering) are as the compress algorithm, which can reduce the size of raw data before it is sent to server. In this paper, we present an energy efficient task scheduling strategy (EETS) to determine what kind of task with certain amount of data should be chosen to be offloaded under different environment. We have evaluated the scheduler by using an Android smartphone. The results show that our strategy can achieve 99% of accuracy to choose the right action in order to minimize the system energy usage.
An Adaptive Procedure for Task Scheduling Optimization in Mobile Cloud Computing
Mathematical Problems in Engineering, 2015
Nowadays, mobile cloud computing (MCC) has emerged as a new paradigm which enables offloading computation-intensive, resource-consuming tasks up to a powerful computing platform in cloud, leaving only simple jobs to the capacity-limited thin client devices such as smartphones, tablets, Apple’s iWatch, and Google Glass. However, it still faces many challenges due to inherent problems of thin clients, especially the slow processing and low network connectivity. So far, a number of research studies have been carried out, trying to eliminate these problems, yet few have been found efficient. In this paper, we present an enhanced architecture, taking advantage of collaboration of thin clients and conventional desktop or laptop computers, known as thick clients, particularly aiming at improving cloud access. Additionally, we introduce an innovative genetic approach for task scheduling such that the processing time is minimized, while considering network contention and cloud cost. Our simu...
IEEE Access
The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloudbased frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%. INDEX TERMS Cloud computing, mobile cloud computing, task offloading, task sequencing, task scheduling, microservices.
A Survey on Task Distribution Approach for Mobile Cloud Computing Application
2014
Recently mobile devices, such as smartphones and tablets have made many pervasive computing dreams which come true. Still such mobile applications do not perform well due to the shortage of resources for computation data storage, network bandwidth, and battery backup. Along with the rapid growth of various cloud services and network technologies and increasing number of mobile devices use cloud storage services to enlarge their capacity and share data in our daily lives. We generally use cloud service client-side software in a serial fashion. However when much devices and users participate in various services, the difficulty of managing these services efficiently and conveniently increases. With worldwide shipments of smartphones 487.7million exceeding PCs 414.6million including tablets in 2011, and in the US alone, more users predicted to access the Internet from mobile devices than from PCs clearly there is a desire to be able to use mobile devices and networks like we use PCs and...
Work Allocating Strategy Using a Powerful Prioritized Tasks in Mobile Cloud Computing Atmosphere
IJRASET, 2021
In recent times, users necessitate and expect more demanding criteria to perform computational in-depth applications on their mobile devices. Based on the mobile device limitations such as processing power and battery life, Mobile Cloud Computing (MCC) is turned to be a more attractive choice to influence these drawbacks as a mobile computation can be provided to the cloud, which is coined as Mobile computation deceive. Prevailing researches on mobile computation offloading determines offloading mobile computation to single cloud. Moreover, in real time environment, computation service can be offered by multiple clouds for every computation services. Therefore, a novel and an interesting research crisis in mobile computation offloading begins with, how to choose a computation service for every tasks of mobile computation like computation time, energy consumption and cost of using these computation services. This is also termed as multi-site computation offloading in mobile cloud computation. In this examination deceive computation to diverse cloudlets/data centres with respect to task scheduling is formulated for examination. So, a Searching algorithm known as Accelerated Cuckoo Search Algorithm based job splittingis designed to attain higher data transmission rate in the MCC. The results of the certain method outperform the prevailing methods in terms of effectual job splitting; transmission speed, Bandwidth used, execution time of a job, transmission value, through put value, buffering overhead and reduced waiting time. The simulation was carried out in Clouds environment for good output.