A Study for VM Placement Schemes in Cloud

2017 ◽  
Vol 7 (8) ◽  
pp. 208
Author(s):  
Ramandeep Kaur ◽  
Navpreet Kaur
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Task Scheduling ◽  
Specific Time ◽  
Due Date ◽  
Vm Placement ◽  
Time Task ◽  
Wide Range ◽  
The Web

The cloud computing can be essentially expressed as aconveyance of computing condition where distinctive assets are conveyed as a support of the client or different occupants over the web. The task scheduling basically concentrates on improving the productive use of assets and henceforth decrease in task fruition time. Task scheduling is utilized to allot certain tasks to specific assets at a specific time occurrence. A wide range of systems has been exhibited to take care of the issues of scheduling of various tasks. Task scheduling enhances the productive use of asset and yields less reaction time with the goal that the execution of submitted tasks happens inside a conceivable least time. This paper talks about the investigation of need, length and due date based task scheduling calculations utilized as a part of cloud computing.

scholarly journals Efficient Task Scheduling for Quality of Service in Cloud Computing Network

2020 ◽  
Vol 8 (5) ◽  
pp. 3193-3196
Keyword(s):  
Cloud Computing ◽  
Quality Of Service ◽  
Response Time ◽  
Task Scheduling ◽  
Waiting Time ◽  
Threshold Values ◽  
Specific Time ◽  
Required Quality ◽  
Cloud Resources

Task scheduling in cloud is the process of allocating a resource to a task at specific time. The allocation of limited cloud resources to large number of tasks to satisfy the required quality of service is the key challenge in cloud. Allocation of a resource with less capability to a task increases the response time, makespan of the task and waiting time of the entire tasks in the waiting queue. This problem will result to an unsatisfied Quality of Service. In this paper we proposed an efficient task scheduling that uses three threshold values to specify the resource to be allocated to a task at a given time. This method ensures that a capable resource is allocated to task such that the response time and makespan of the all task are minimized. The proposed method was simulated using CloudSim and the result shows a better response time and makespan than the well known Min-Min and Max-Min Method.

scholarly journals Cost Effective Ant Colony Optimization in Cloud Computing

2019 ◽  
Vol 8 (10) ◽  
pp. 3016-3020
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Direct Interaction ◽  
Cost Effective ◽  
Cloud Services ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Wide Range ◽  
Scheduling Method

Cloud computing is a term for a wide range of developments possibilities. It is rapidly growing paradigm in software technology that offers different services. Cloud computing has come of age, since Amazon's rollouted the first of its kind of cloud services in 2006. It stores the tremendous amount of data that are being processed every day. Cloud computing is a reliable computing base for data-intensive jobs. Cloud computing provide computing resources as a service. It is on-demand availability of computing resources without direct interaction of user. A major focus area of cloud computing is task scheduling. Task scheduling is one among the many important issues to be dealt with. It means to optimize overall system capabilities and to allocate the right resources. Task scheduling referred to NP-hard problem. The proposed algorithm is Cost Effective ACO for task scheduling, which calculates execution cost of CPU, bandwidth, memory etc. The suggested algorithm is compared with CloudSim with the presented Basic Cost ACO algorithm-based task scheduling method and outcomes clearly shows that the CEACO based task scheduling method clearly outperforms the others techniques which are in use into considerations. The task is allotted to the number of VMs based on the priorities (highest to lowest) given by user. The simulation consequences demonstrate that the suggested scheduling algorithm performs faster than previous Ant Colony Optimization algorithm in reference to the cost. It reduces the overall cost as compare to existing algorithm.

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

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 108
Author(s):  
Abid Ali ◽  
Muhammad Munawar Iqbal ◽  
Harun Jamil ◽  
Habib Akbar ◽  
Ammar Muthanna ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
Task Scheduling ◽  
Mobile Cloud Computing ◽  
Computing Time ◽  
Management Approach ◽  
Mobile Cloud ◽  
Distribution Method ◽  
Wide Range ◽  
Iot Devices

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.

scholarly journals DELAY DIMINISHED EFFICIENT TASK SCHEDULING AND ALLOCATION FOR HETEROGENEOUS CLOUD ENVIRONMENT

2019 ◽  
Vol 01 (01) ◽  
pp. 51-62 ◽  
Author(s):  
Bhalaji N
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Quality Of Service ◽  
Task Scheduling ◽  
Cloud Environment ◽  
Wide Range ◽  
Heterogeneous Cloud ◽  
The Way

Cloud computing being a promising paradigm has become very prominent among a wide range of applications due to their timely service rendering capability. Attracted to the type of servicing and the way of servicing lots and lots of users, adapt to the cloud computing. This makes the time servicing of the cloud computing a tedious job. So in order to effectively handle the tasks the scheduling approach is entailed in the cloud computing. The paper proposes an efficient task scheduling for the heterogeneous cloud to render service at a minimized delay utilizing the genetic algorithm. The proposed method is validated through the, cloud simulator to understand the efficiency of the same in terms of delay and the quality of service.

scholarly journals v-Mapper: An Application-Aware Resource Consolidation Scheme for Cloud Data Centers

2018 ◽  
Vol 10 (9) ◽  
pp. 90 ◽  
Author(s):  
Aaqif Abbasi ◽  
Hai Jin
Keyword(s):  
Cloud Computing ◽  
Resource Management ◽  
Ease Of Use ◽  
Vm Placement ◽  
Network Resource ◽  
Computing Systems ◽  
Time Service ◽  
Management Concepts ◽  
Wide Range ◽  
Resource Consolidation

Cloud computing systems are popular in computing industry for their ease of use and wide range of applications. These systems offer services that can be used over the Internet. Due to their wide popularity and usage, cloud computing systems and their services often face issues resource management related challenges. In this paper, we present v-Mapper, a resource consolidation scheme which implements network resource management concepts through software-defined networking (SDN) control features. The paper makes three major contributions: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a cloud service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. We tested our scheme with other techniques in terms of average task processing time, service delay and bandwidth usage. Our results demonstrate that v-Mapper outperforms other techniques and delivers significant improvement in system’s performance.

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