Virtualization enables flexible resource provisioning and improves energy efficiency by consolidating virtualized servers into a smaller number of physical servers than that of the virtualized servers. Currently, virtualized environments including cloud computing systems charge users for the amount of their processor time, or the number of their virtual machine instances. This paper proposes an estimation model to consider the energy consumption of each virtual machine without any dedicated measurement hardware.
Our estimation model estimates the power consumption of a virtual machine based on CPU events generated by the virtual machine. Detecting the number of active cores at a precise time granularity using per-core field variables.
Introduction
Therefore, it is difficult to identify what proportion each virtual machine contributes to the system power. Existing research output has already approached the calculation of energy consumption at the virtual machine granularity by calculating and analyzing the amount of resources that each virtual machine uses. However, these models assumed that the energy consumption of each virtual machine is simply proportional to the length of allocated processor time or the number of network packets processed.
The conventional virtual machine schedulers only consider processor time when it comes to the scheduling decision. Both the proposed estimation model and the scheduler are implemented in the Xen virtual machine monitor.
Motivation and Related Works
Motivation
Consequently, in order to identify the energy consumption of each virtual machine, which is constantly and rapidly changing, the hypervisor must equip a resource accounting scheme that can estimate the energy consumption of the virtual machine by observing its various activities and the effects of other virtual machines are necessary. In addition to energy metering schemes at the virtual machine level, energy-aware provisioning schemes are needed to adjust billing systems based on the amount of energy consumption, so that users can limit the energy consumption of their virtual machines to stay within their energy budgets. Most conventional virtual machine schedulers strive to provide CPU time to each VM fairly and in proportion to its priority, because CPU time is one of the main billing criteria for conventional computing systems.
To use the energy-aware scheduling scheme, each virtual machine must provide its The energy budget of a virtual machine is the amount of energy the virtual machine can use during its fiscal time interval. If a virtual machine uses its energy budget in a fiscal interval, the virtual machine will be suspended until the current tax interval ends and the next interval begins.
The fiscal interval of a virtual machine is determined by the owner of the virtual machine based on its purpose and characteristics and can vary from a few tens of milliseconds. The fiscal interval should be chosen carefully because it can affect the throughput and responsiveness of the virtual machines. Conversely, if the interval is too long, the downtime of the virtual machine may become too long and consequently may harm the responsiveness of the services provided by the virtual machine.
For example, as shown in Figure 2-1, when two virtual machines dedicated to a web service or a data backup service are running together on a physical server at the same time, it is beneficial for the web service virtual machine to have a short-term plan such as is 300 mWh/0.5 s. To realize this conceptual model, we propose an estimation model that estimates the energy consumption of each virtual machine in a multi-core server. In addition, we propose an energy-aware virtual machine scheduler that schedules virtual machines according to their energy budgets.
Related Works
The second model, the improved model, uses an integrated power measurement device to collect power consumption patterns of the entire system. When a new virtual machine is created, the system observes changes in power consumption patterns by measuring the first 200 seconds. This observation gives the power consumption characteristics of the new virtual machine after statistical processing such as linear regression.
Using the obtained characteristics of the virtual machine's power consumption pattern, the second model estimates the power consumption of the virtual. Because this model is based on the assumption that the behavior of the virtual machine remains the same as that of its first 200 sec. The processor power consumption model estimates the power consumption of a processor by monitoring its internal activities such as memory accesses, integer or floating point arithmetic operations and so on.
Other power estimation models based on the observation of the interval activities have been proposed and demonstrated their accuracy. Sinha and Chandrakasan, 2001, Tiwari et al., 1996). Based on this approach, some system simulators also estimate the power and energy requirements of applications.
Energy Accounting
Observation
Thus, to establish the evaluation model, we measure the changes in energy consumption depending on the performance counter values every 300 ms. Therefore, we believe that the number of retired micro-operations is likely to reflect the activities within a processor more accurately than the number of retired instructions. Even within the same workloads, the number of retired micro-operations during a time interval varies greatly over time, and this trend results in fluctuations in energy consumption patterns.
In general, the average energy consumption in the time interval, which can be directly interpreted as the energy consumption of the interval, appears to be linearly proportional to the number of micro-operations. However, compared to the workloads with infrequent memory accesses, more energy is consumed to perform the same number of operations as the workloads with frequent memory. Despite the same number of operations, the memory-intensive workloads tend to consume more energy than the non-memory-intensive workloads.
Therefore, we expect a processor to require less energy when instructions are distributed and executed across multiple cores than when the same number of instructions are executed on a single core or a smaller number of cores. The dots in the graph are grouped around four small areas depending on the number of active nuclei. The difference in power consumption between one and two, two and three, or three and four cores is smaller than the power consumption of just one active core.
Also, using more cores results in better power efficiency due to less tendency of power consumption compared to that of a single core. Contrary to popular belief, the difference in power consumption between floating-point operations and integer operations at the micro-operational level does not show significant differences, despite the large difference in complexity at the macro-operational level. This is because floating-point instructions tend to translate into a larger number of micro-operations than floating-point instructions.
Profiling and Estimation
The obtained coefficients are used to estimate the energy consumption of a virtual machine interval by substituting , and in Equation 3 for the measured performance counter values. At the end of each predefined time interval, usually 300 ms., the master core collects the ec_priv data for all cores and reconstructs the core utilization time series as illustrated in Figure 3-6. This approach collects the data for the linear regression and obtains the coefficients.
When calculating CPU power consumption on a per-core basis, we must take into account the power consumed by these shared components. The microoperation count model shows better accuracy for the memory-intensive workloads than for the CPU-intensive workloads. However, this is because the underestimation tendency of the memory-intensive workloads outweighs the overestimation property of the microoperation count model.
We measured the total energy consumption and compared it to the estimated total energy consumption using our estimation model for several sets of criteria. Obviously, there is no physical power meter that can measure VM power consumption. However, we can expect that the sum should agree with the measured total power consumption if all the power consumptions of each VM are correctly estimated.
To the right of the benchmark names, the estimated energy consumption of each VM is displayed, and in the last column, the estimated total energy consumption. The red line represents the measured power consumption and the blue dotted line represents the estimated power consumption. As in the graph, our model estimates energy consumption with low errors in most cases despite the existence of drastic changes in energy consumption.
Energy-Aware Scheduling
Energy-Aware Scheduler
Evaluation
To evaluate the power supply accuracy, we use the workload configurations from Table 4-1. One to four virtual machines run simultaneously and each of them runs a different workload with a different power budget. Although a virtual machine is free to choose its own fiscal interval, all virtual machines in our experiment are set to 1 second.
Similar to the evaluation of the estimation model, we compare the aggregated value of the indicated energy consumption of all virtual machines with the measured energy consumption of the entire physical machine. The two rightmost columns in Table 4-1 show the measured energy consumption per second and the energy supply error, respectively. According to the experiment results, the error rates are less than 2% of the total energy consumption.
If the energy budget must be strictly guaranteed, the algorithm must be improved to be proactive. Time series of set energy consumption rate and actual energy consumption rate when the energy budget of four virtual machines. However, on a short time scale, such as 5 seconds, the difference between the specified power supply and the actual usage varied significantly.
Despite the short-term inaccuracy, in the long-term our scheme provides the energy according to the energy budget.
Conclusion
Euiseong Seo for his guidance, kindness and especially his wholehearted support so that I could only focus on my works.
