scholarly journals A Persuasive Resource-Aware Allocation Scheduler for Enhancing Task Scheduling

2019 ◽  
Vol 8 (6) ◽  
pp. 2808-2811
Keyword(s):  
Resource Allocation ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Map Reduce ◽  
Total System ◽  
Master Node ◽  
Resource Aware

The deployment of Map Reduce has been built to grant enhancements to total system objectives such as job throughput. Hence, the support for user-specific objectives and resource allocation management has been least regarded and addressed. Schedulers enable users to assign jobs to queues that fulfil shared of specific resource. Existing work mainly focus on scheduling glitch occurring on the master’s side where the scheduler on the master node tries to allocate same work across all the worker nodes. The proposed scheduler focus on enhancing resource allocation when various kinds of workloads execute on the clusters. In order to evaluate the performance on the proposed scheduler which enhances resource utilization, an accomplishing time goal with each job is created.

scholarly journals Resource-Aware Min-Min (RAMM) Algorithm for Resource Allocation in Cloud Computing Environment

2019 ◽  
Vol 8 (3) ◽  
pp. 1863-1870 ◽  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Cloud Resource ◽  
Tasks Scheduling ◽  
Minimum Completion Time ◽  
Resource Aware ◽  
Time Resource

Resource allocation (RA) is a significant aspect of Cloud Computing. The Cloud resource manager is responsible to assign available resources to the tasks for execution in an effective way that improves system performance, reduce response time, lessen makespan and utilize resources efficiently. To fulfil these objectives, an effective Tasks Scheduling algorithm is required. The standard Max-Min and Min-Min Task Scheduling algorithms are not able to produce better makespan and effective resource utilization. In this paper, a Resource-Aware Min-Min (RAMM) Algorithm is proposed based on basic Min-Min algorithm. The proposed RAMM Algorithm selects shortest execution time task and assigns it to the resource which takes shortest completion time. If minimum completion time resource is busy, then the RAMM Algorithm selects next minimum completion time resource to reduce waiting time of the task and improve resource utilization. The experiment results show that the proposed RAMM Algorithm produces better makespan and load balance than Max-Min, Min-Min and improved Max-Min Algorithms.

scholarly journals An Efficient Grouped Task Scheduling and Resource Allocation in Cloud Computing Environments

2019 ◽  
Vol 8 (4) ◽  
pp. 12203-12206
Keyword(s):  
Resource Allocation ◽  
Resource Utilization ◽  
Task Scheduling ◽  
Waiting Time ◽  
Execution Time ◽  
High Capacity ◽  
Task Type ◽  
Average Waiting Time ◽  
Computing Environments ◽  
Time Average

Task scheduling in cloud is the allocation of resources to a task at a particular time. In cloud, scheduling strategy is defined or adapted by a scheduler according to the changing environment. Allocation of resource with poor capacity to a task may lead to increase in execution time of the task. Problem of resource under utilization may also occur when a resource with high capacity is allocated to a task that requires a resource with lesser capacity. In this paper we proposed an Efficient Grouped Task Scheduling (EGTS) and resource allocation to minimize average waiting time, average execution time and increase resource utilization. EGTS classify Tasks into two groups of similar task type, and sort the tasks in the order of their respective deadlines. Task in each group is allocated Virtual Machine with capacity equal to the average capacity required by tasks in that group. An experiment was conducted using CloudSim to exhibit EGTS and the result shows minimal average execution time, average waiting time and a higher resource utilization compared to Min-Min and Max-Min

scholarly journals Transmission Strategy Design and Resource Allocation in D2D Multicast Cooperative Communications with SWIPT

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Chenfan Weng ◽  
Dingcheng Yang ◽  
Jun Wan ◽  
Lin Xiao ◽  
Chuanqi Zhu
Keyword(s):  
Resource Allocation ◽  
Cooperative Communications ◽  
Communication Systems ◽  
Spectrum Efficiency ◽  
Second Step ◽  
Total System ◽  
Power Splitting ◽  
Transmission Strategy ◽  
Multiple Receivers ◽  
Strategy Design

This paper proposes a new transmission strategy for device-to-device (D2D) multicast cooperative communication systems based on Simultaneous Wireless Information and Power Transfer (SWIPT) technology. The transmission block is divided into two slots. In the first slot, the source user transmits the information and energy to the help user by SWIPT. In the second slot, the help user uses the cellular spectrum and forwards the information to multiple receivers by using harvested energy. In this paper, we aim to maximize the total system rate, and to tackle the problem, we propose a two-step scheme: In the first step, the resource allocation problem is solved by linear programming. In the second step, the power-splitting coefficient value is obtained by taking the benefit of help user into account. Numerical results show that the proposed strategy not only effectively improves the overall throughput and spectrum efficiency but also motivates the cooperation.

Optimal Energy Management for a Smart Grid using Resource-Aware Utility Maximization

2016 ◽  
Vol 17 (3) ◽  
pp. 251-266
Author(s):  
Brook W. Abegaz ◽  
Satish M. Mahajan ◽  
Ebisa O. Negeri
Keyword(s):  
Smart Grid ◽  
Energy Management ◽  
Resource Utilization ◽  
Utility Maximization ◽  
Energy Resource ◽  
Optimal Energy ◽  
Central Controller ◽  
Optimal Energy Management ◽  
Energy Community ◽  
Resource Aware

Abstract Heterogeneous energy prosumers are aggregated to form a smart grid based energy community managed by a central controller which could maximize their collective energy resource utilization. Using the central controller and distributed energy management systems, various mechanisms that harness the power profile of the energy community are developed for optimal, multi-objective energy management. The proposed mechanisms include resource-aware, multi-variable energy utility maximization objectives, namely: (1) maximizing the net green energy utilization, (2) maximizing the prosumers’ level of comfortable, high quality power usage, and (3) maximizing the economic dispatch of energy storage units that minimize the net energy cost of the energy community. Moreover, an optimal energy management solution that combines the three objectives has been implemented by developing novel techniques of optimally flexible (un)certainty projection and appliance based pricing decomposition in an IBM ILOG CPLEX studio. A real-world, per-minute data from an energy community consisting of forty prosumers in Amsterdam, Netherlands is used. Results show that each of the proposed mechanisms yields significant increases in the aggregate energy resource utilization and welfare of prosumers as compared to traditional peak-power reduction methods. Furthermore, the multi-objective, resource-aware utility maximization approach leads to an optimal energy equilibrium and provides a sustainable energy management solution as verified by the Lagrangian method. The proposed resource-aware mechanisms could directly benefit emerging energy communities in the world to attain their energy resource utilization targets.

Resource Allocation Policies in Cloud Computing Environment

2017 ◽  
pp. 115-132 ◽  
Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Resource Utilization ◽  
Service Level Agreement ◽  
Service Level ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
On Demand ◽  
Different Types ◽  
Cloud Datacenter

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.

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