# Sharing with Live Migration Energy Optimization Scheduler for Cloud Computing Data Centers
## 4 SLM Scheduler Algorithm
The SLM scheduler allows multiple tasks processing at the same time only if the task's type is'read' or 'download' (not if the current task type under processing is either 'write' or 'upload', due to the overwriting problem). Hence, the SLM scheduler will provide concurrency control. * QoS and SLA control: The proposed SLM simulation model added a continuous observation class to the simulation to detect changes of the QoS and the SLA conditions, and prevent any violation to those agreements. ## 6 Experimental Results

In this section, we analyze the performance of our algorithm based on the experimental results. We created a simulation named SLM to further verify the performance of the proposed SLM scheduler. To test the response time performance of our proposed SLM scheduler, we benchmarked it against a scenario named basic work [30]. The basic work model is a greedy data center that assigns tasks to the hosts with sufficient resources to handle the tasks using a FIFO (First In First Out) queuing system and no concurrency control, where two tasks cannot access the same file simultaneously at the same time. Therefore, at high loads, the waiting and response time will eventually increase the makespan. Two parameters have been used to measure the performance of the task scheduling algorithms: energy consumption and makespan, where makespan is the overall completion time needed to assign all of the tasks to the available resources i.e., VMs. Hence, the SLM model will provide task admission control for all of the incoming tasks, and it will provide a parallel processing feature with migration. Compared with the basic work algorithm, the proposed algorithm can significantly reduce the system energy consumption and makespan, as shown in Table 1.

The simulation results of the SLM model in Table 1 show the improvement in reducing energy consumption. If we take an example of a batch of 100 tasks in the cloud data center, those tasks are processed using 0.16 kWh and 1098 s of makespan in a basic work environment, while in the SLM model, they require only 0.07 kWh and 450 s of makespan. Figures 2 and 3 display the improvements. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Tasks**} & \multicolumn{2}{c}{**Basic Work Model**} & \multicolumn{2}{c}{**Energy Consumption in the SLM Model**} \\ \cline{2-5}  & **Energy Consumption (kwh)** & **Makespan (s)** & **Energy Consumption (kwh)** & **Makespan (s)** \\ \hline
100 & 0.16 & 1098 & 0.07 & 450 \\
200 & 0.24 & 1590 & 0.11 & 730.1 \\
300 & 0.3 & 2178 & 0.17 & 1260 \\
400 & 0.32 & 2290 & 0.21 & 1350.1 \\
500 & 0.39 & 2518 & 0.27 & 1920 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The SLM and the basic work simulation energy and makespan results. ## 7 Conclusions

The ultimate goal of energy-efficient scheduling in cloud computing is to reduce the costs and computing infrastructure. This paper introduced a novel scheduling algorithm, named SLM, which aims to reduce the overall energy consumption of a data center by combining sharing and live migration policies. A set of simulation tests demonstrated that the method leads to a more balanced workload for each machine. Compared to a basic work, the SLM algorithm has a shorter processing time and an overall optimized performance. The results show good performance in energy utilization and makespan. We expect that the real-world cloud platforms will apply the proposed algorithm, aiming at network load minimization and reducing the energy costs for cloud data centers. In the future, we plan to extend the work for 500 to 10,000 tasks in order to evaluate energy consumption. Conceptualization and Methodology, S.A., B.G., N.C.; Investigation, S.A. and B.G. ; Software, Validation, Formal Analysis, X.X. ; Writing-Original Draft Preparation, S.A.; Writing-Review & Editing, S.A., B.G., N.C.; Supervision, B.G., N.C. The authors declare no conflict of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON] A survey on cloud computing security: Issues, threats, and solutions. _J. Netw. Comput.