## 5 Results and Discussions  
Figure 2 comprises the details of all the configurations of the first test. In this result section, performance difference of configurations can be seen clearly. In configuration 1, a single virtual machine or container is assigned with higher number of CPU cores and executes the HEPSPEC06 benchmark. In the result, it can be seen that performance loss of VMs is up to 17% while containers are up to 13%, when only comparing with results of bare metal. Our proposed techniques demonstrate that the overall efficiency of scientific jobs can be maximized when we minimize the number of virtual cores and increase the number of VMs or containers. In Figure 2, configuration 5 can minimize the performance impact as it is the result of 16 virtual machines/container (assigned a single core to each virtual machine/container). As seen, it minimizes the performance lose impact on VMs from 17% to 5% and on containers from 13% to 2%. The other configurations, 2, 3, and 4, can also reduce the overall impact of performance loss. It clearly shows the difference of performance between VMs and containers configuration; containers perform much better than the virtual machine configuration, which is much closer to bare-metal performance. However, it is observed that by using configuration 5, it is possible to achieve near-real-time performance with containers in our cloud environment. Figure 2: HEPSPEC06 benchmark results for five configurations of test-1. Figure 3 shows the result of five configurations over test-2. The primary goal of this test was to enhance the overall throughput of the physical system. Since the hyperthreading doubles the number of cores, we often allowed HT on a physical system with 16 cores; it doubles the cores up to 32. As illustrated in Figure 3, the efficiency of HEPSPEC06 on the physical host (bare metal) is improved by up to 16%. This result is compared to the physical host of test-1 having 16 physical cores. For better performance and efficiency, this study used a similar technique of assigning cores per virtual machine or container of the first test case (i.e., test-1) but the total number of cores doubled for each virtual machine or container. As shown in the results, using the HEPSPEC06 benchmark, the performance loss of VM is 18% and containers is up to 14% of configuration 1 compared to the physical machine. With configuration 5, however, we increased the number of virtual machines or containers with decreased numbers of cores, which improved overall performance of VM up to 12% and container up to 10%, while comparing test-2 with configuration 1. The size of the virtual machine or container can vary according to scientific workload so that throughput and performance can be balanced according to requirements. In test-3, to improve the performance of the VM or container, we applied the pinning and isolation techniques that are implemented for both test cases and consist of five configurations. Figure 4 depicts that the HEPSPEC06 benchmark minimized the overall performance losses; if the number of cores per virtual machine or container decreased and proper pinning and isolation applied, then it further reduces the impact of virtualization from 18% to 6% and of containerization from 14% to 1.5%. It clearly shows that the containers can give much better and near-real-time performance in a cloud environment. Figure 3: HEPSPEC06 benchmark results for five configurations of test-2. The results of the HEPSPEC06 benchmark shows the results of test-4. As illustrated in Figure 5, these results are evaluated after pinning and isolation of HT-enabled VMs/containers and vCPUs for the second test case. The results of test-2 show the identical numbers given by using configuration 5, then configuration 1, and others. We can achieve near-real-time performance of the virtual machine with the difference of only 6% in comparison to bare metal (physical machine).