An Optimal Task Scheduling Mechanism for Mobile Cloud Computing (original) (raw)
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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.
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.
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.
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 Study on Task Scheduling in Mobile Cloud Computing
International Journal of Scientific Research in Science and Technology, 2018
In mobile device, the resources such as computation, storage, power are limited. Quality of Experience (QoE) of user in these limited resource mobile device is not satisfied. Mobile cloud computing is a new computation paradigm to increase Quality of Service (QoS) of mobile applications by scheduling the offloaded tasks into the cloud. The scheduling of tasks is done in four architectures of mobile cloud computing. Two types of scheduling are done with lot of constraints such as data transmission, task dependency and cost etc. Different scheduling techniques are developed to improve the QoE of mobile users.
An Improved Scheduling Algorithm for Task Offloading on Cloud
Nowadays mobile devices have become one of the basic necessities of our daily lives. These mobile devices resolve our numerous tasks which earlier used to take a lot of time without such devices. This paper is used to describe some of the methodologies using which we can manage such tasks more efficiently. The basic disadvantage of mobile devices is its battery consumption, due to which we need to use our devices in a much optimized way. To support this idea, we have proposed an organizational level application through which we can segregate between tasks which have much higher battery and resource utilization and tasks which require minimum resources. The former tasks are dispatched or rather offloaded to the cloud servers and the generated output is sent back to the device which reduces the battery consumption of the device. To distinguish between the tasks we use some scheduling algorithms at both levels.
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...
Energy-aware dynamic task offloading and collective task execution in mobile cloud computing
International Journal of Communication Systems, 2019
There is a good opportunity for enlightening the services of the mobile devices by introducing computational offloading using cloud technology. Offloading is a process for managing the complexity of the mobile environment by migrating computational load to the cloud. The mobile devices oblige the quick response for the offloading requests; it is dependent on network connectivity. The cloud services take long setup time irrespective of network connectivity. In this paper, new system architecture for the dynamic task offloading in the mobile cloud environment is proposed. The architecture includes the offloading algorithm that concentrates on energy consumption of the tasks both in the local and remote environment. The proposed algorithm formulates a collective task execution model for minimizing the energy consumption. The architecture concentrates on the network model by considering the task completion time in three different network scenarios. The experimental results show the efficiency of the suggested architecture in reducing the energy consumption and completion time of the tasks.
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 Review of Computational Task Offloading Approaches in Mobile Computing
International Journal of Scientific Research in Science, Engineering and Technology, 2019
Mobile cloud computing permits the execution of calculation escalated uses of cell phones in computational clouds, and this procedure of executing in cloud by sending the application VM/Components is called application/code/part offloading. Offloading is a successful strategy to spare the execution time and vitality utilization of cell phones. In this way it amplifies the battery life of cell phones. Applications are first apportioned into offloadable and on-offloadable segments, which are then exchanged to remote server for execution. We concentrate the booking of computational assignments on one nearby processor and one remote processor with correspondence delay. This issue has vital application in cloud computing. In spite of the fact that the correspondence time to transmit an errand can be induced from the known information size of the assignment and the transmission data transfer capacity, the preparing time of the undertaking is for the most part obscure until it is handled to completion. The target of this paper is to investigate the distinctive systems of offloading and application dividing techniques. These strategies are completely surveyed in this paper. This paper likewise highlights the examination of various systems on the premise of their commitment, benefits, negative marks and furthermore on the premise of change in execution time, vitality utilization, correspondence time.