A recent review and a taxonomy for multimedia application in Mobile cloud computing based energy efficient transmission

Parajuli, Nitesh; Alsadoon, Abeer; Prasad, P. W. C.; Ali, Rasha S.; Alsadoon, Omar Hisham

doi:10.1007/s11042-020-09516-y

Cited by 10 publications

(6 citation statements)

References 82 publications

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“…The scheduling experiments were conducted on Core i5-CPU/3.0 GHz/8 GB RAM-running PCs and MATLAB R2018b. We design a user-friendly and creative environment using MATLAB to build our experimental model [ 1 ]. The experimental model is designed to achieve the reliable and efficient behavior of IoT devices.…”

Section: Resultsmentioning

confidence: 99%

“…Internet of Things (IoT) and mobile cloud computing (MCC) are names given to the emerging concept of establishing a meaningful relationship between the actual objects around us to interrelated things that collectively change our traditional lifestyle with progressive and forward-looking ideas [ 1 ]. Smart homes, smart cities, and intelligent transportation systems are practical examples of these ideas, where devices and man’s real-time performance integrate to obtain intelligent access to physical changes in our surroundings [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

Ali

Iqbal

Jamil

et al. 2021

Sensors

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With the increasing number of mobile devices and IoT devices across a wide range of real-life applications, our mobile cloud computing devices will not cope with this growing number of audiences soon, which implies and demands the need to shift to fog computing. Task scheduling is one of the most demanding scopes after the trust computation inside the trustable nodes. The mobile devices and IoT devices transfer the resource-intensive tasks towards mobile cloud computing. Some tasks are resource-intensive and not trustable to allocate to the mobile cloud computing resources. This consequently gives rise to trust evaluation and data sync-up of devices joining and leaving the network. The resources are more intensive for cloud computing and mobile cloud computing. Time, energy, and resources are wasted due to the nontrustable nodes. This research article proposes a multilevel trust enhancement approach for efficient task scheduling in mobile cloud environments. We first calculate the trustable tasks needed to offload towards the mobile cloud computing. Then, an efficient and dynamic scheduler is added to enhance the task scheduling after trust computation using social and environmental trust computation techniques. To improve the time and energy efficiency of IoT and mobile devices using the proposed technique, the energy computation and time request computation are compared with the existing methods from literature, which identified improvements in the results. Our proposed approach is centralized to tackle constant SyncUPs of incoming devices’ trust values with mobile cloud computing. With the benefits of mobile cloud computing, the centralized data distribution method is a positive approach.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

Ali

Iqbal

Jamil

et al. 2021

Sensors

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“…According to the National Institute of Standards and Technology (NIST), cloud computing is defined as a kind of computing resource (including networks, servers, storage, applications, and services) that can be obtained through the Internet in a convenient and on-demand manner. These resources come from a configurable and shared resource pool [7], and they only need very little management effort from the users and enable users to quickly configure and release related computing resources without interacting with cloud providers [8]. In summary, cloud computing can also be called grid computing; it can complete the processing of tens of thousands of items of data in a short period of time (a few seconds), so as to achieve powerful network services, and through a system composed of multiple servers, it can process and analyze these small programs to get the results and return them to the user.…”

Section: Research Methods Of Workflow Schedulingmentioning

confidence: 99%

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Gong

2021

Wireless Communications and Mobile Computing

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In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

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“…ese techniques ensured that each connected node in the topology had been associated with a single leader. e approaches in [22,[54][55][56] have highly relied on a temporarily ordered routing technique called TORA.…”

Section: Related Workmentioning

confidence: 99%

Head Node Selection Algorithm in Cloud Computing Data Center

Kanwal

Iqbal

Irtaza

et al. 2021

Mathematical Problems in Engineering

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Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption.

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A recent review and a taxonomy for multimedia application in Mobile cloud computing based energy efficient transmission

Cited by 10 publications

References 82 publications

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

Multilevel Central Trust Management Approach for Task Scheduling on IoT-Based Mobile Cloud Computing

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Head Node Selection Algorithm in Cloud Computing Data Center

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