# Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers
## 2 Related Work  
Many papers deal with task allocation and scheduling in a cloud environment, which successfully led to a reduction in the total energy consumption.  
In [12], the authors proposed a Dynamic Voltage and Frequency Scaling (DVFS) enabled energy-efficient workflow task-scheduling algorithm (DEWTS). The proposed algorithm balances the performance of scheduling by dividing the parallel tasks in workflows, and then executes them at an appropriate time to reduce the power consumption. Evaluation tests indicated that the algorithm succeeded to drop the power consumption by 46.5% in the execution of parallel tasks.  
The priority algorithm from [13] is a green energy-efficient scheduler that efficiently uses the DVFS technique to allocate proper resources to jobs according to the requirements of jobs under the Service Level Agreement (SLA). This technique improved the resource utilization and reduced the power consumption of servers by 23% in comparison to the dynamic voltage scaling technique [14]. An offline scheduling strategy for data intensive applications was proposed in [15] to reduce the energy consumption by degrading the SLA rate. The proposed scheduling strategy decreased network traffic and improved the utilization of servers to reduce the energy consumption in the cloud.  
In [16], the authors introduced an energy aware scheduling strategy with DVFS integration in multicore processors, which works for batch and online mode tasks. The scheduler improved the energy consumption by 27% for batch mode applications, and 70% for the online mode applications. The proposed new virtual machine scheduler in [17] was used to schedule the virtual machines by selecting first the highest performance power virtual machine (VM) in the deadline constraint. This scheduling algorithm increased the processing capacity by 8% and saved up to 20% of the energy. [PERSON] and [PERSON] [18] proposed an energy-efficient resource-provisioning framework with dynamic virtual machine placement using an energy aware load balancer in the cloud. The presented technique obtains measurable improvements in resource utilization by minimizing the power consumption, cost, and execution time of the cloud computing environment.  
The proposed scheduling scheme in [19] utilized the resources and saved energy consumption by optimizing the VM allocation and using a consolidation policy. The results showed energy consumption improvement compared with two benchmarks--DVFS and an Energy-aware Scheduling algorithm using Workload-aware Consolidation Technique (ESWCT). The proposed efficient server-first scheduling in [20] used the response time with respect to the number of active servers to reduce the energy consumption in the data center. About 70 times the energy was saved in comparison of the random selection-based strategy.  
The previously mentioned task-scheduling algorithms considered one or two parameters of task scheduling, while the algorithm must provide fairness to users when providing services and deliver the ultimate performance with the best reductions in cost and power consumption. Intel's Cloud Computing 2015 Vision stressed the need for dynamic resource scheduling approaches to increase the power efficiency of data centers by shutting down idle servers [21] and using a good task scheduler that can reduce both the power consumption of the utilized resources and the processing time of an application [22].  
Following from the limitations of the above studies, this paper focuses on designing a novel scheduler in order to minimize the total processing and communication costs for a set of tasks aiming to reduce the overall energy consumption.