DESIGN SCENARIO-BASED POWER SAVING SCHEME OF
VIRTUAL ENVIRONMENT IN CLOUD COMPUTING
Shin-Jer Yang
1*and
Sung-Shun Weng
2 1Department of Computer Science and Information Management
Soochow University
Taipei (100), Taiwan
2
Department of Information and Finance Management
National Taipei University of Technology
Taipei (106), Taiwan
ABSTRACT
A data center is equipped with a large quantity of servers, storage devices, and networking facilities. Aided by virtualization technology, the population of a data center can be decreased, thus enabling a very significant reduction in power consumption, enhancing a corporation’s competitive advantage, while concurrently lowering operational costs. Hence, built upon the virtualization technology and resource distribution principle, this paper will apply the pre-defined scenario model on the virtual environment to propose the SPS scheme for reducing power consumption in cloud servers. Also, this paper integrates the Linux platform to design the management module for SPS, called SPSM. Subsequently, the SPS can automatically optimize the power distribution based on the pre-defined scenario model via SPSM. When the monitor program discovers an increase in the VM’s workload, the wake-up function of the SPS can activate the server and then perform live migration among VMs by utilizing the automatic workload balancing in the virtual environment. By analyzing the experimental results, the applications of the SPS scheme can obtain higher CPU and memory utilizations of servers to achieve a 21% reduction in power consumption. Finally, the purposes of the proposed SPS are to attain better processing performance and obtain higher resource utilizations of VMs in cloud computing.
Keywords: Virtualization, SPS, VM, Live Migration, Standby Mode
1. INTRODUCTION
1
The data centers located in the United States, for example, have an annual power consumption equivalent to 4.5 billion US dollars. The world’s leading information technology research and advisory company Gartner, Inc. estimated that the budgets spent by the data centers of a majority of companies on power and heat-radiating systems in the next five years will be equal to the expenses on the hardware infrastructure. Aided by virtualization technology, the number of hardware servers in the data centers can be dramatically reduced. As a result, a drastic reduction in power consumption and lower operational costs can be achieved. Also, a company’s competitiveness can be enhanced at the same time. Upon being virtualized, virtualization allows you to run multiple operating systems as virtual machines on a single computer. For instance, the Windows, Linux, and
*
Corresponding author: [email protected]
Solaris operating systems can run simultaneously and enable to effectively share the resources, such as CPU, memory, network device, and storage, on the same physical server. Hence, various resources on the machine can be more effectively utilized; the management of the servers will become much simpler and more convenient. The implication of the virtualization program is quite broad, including the virtualization technologies of input/output (I/O) devices, storage devices, and other components. However, the general public’s focus is concentrated on the server virtualization, which is the competency of executing multiple operating systems on a physical server. Even though such technology is explicitly characterized by a long history, the virtualization of the x86 platform can be traced back to the VMware software released in late 1998 for initiating the virtualization of the x86 System.
In recent years, the virtualization of the x86 machines has emerged as a trend with different factors involved. The first factor is the imminent server replacement lifecycle. A larger number of
mature virtualization management tools are available around the world, and the computer servers are empowered with some new functional support. The symmetrical multi-processing (SMP) function enables more than two processors to be connected to a memory unit. The market has also gradually realized the optimal operation of the existing virtualization technologies. Furthermore, the virtualization of the data center also reduced costs of utilizing the hardware, power supply, and space due to simple resource distribution. A higher degree of flexibility has been instilled in the IT infrastructure. By utilizing the server virtualization technology, the addition of a server only required the administrator to create a new virtual machine on the existing physical server and set the resource distribution in compliance with the real needs. In addition, the virtualization can enable the central management of multiple servers on a single hardware, ensure the server’s continuing operational competency such as disaster recovery and high availability and ultimately reduce the software’s compatibility testing costs. Before server virtualization, the operating system is directly installed on the hardware and it requires specific device drivers to support specific hardware. On this kind of framework, the server’s resources cannot be used efficiently. Upon being virtualized, multiple operating systems can run on a single physical server. All of resources on the server are then thoroughly utilized. Virtualization has prevailed as the classical model of one application program corresponding to one server. After the virtual data center was constructed, the utilization efficiency of the hardware resources and the energy saving could be enhanced, as attributed to the decreasing in the number of physical servers.
Currently, power consumption of the data centers has already been drastically affecting the environment. Researchers have been striving to seek effective solutions to reduce the data centers’ power consumption while maintaining the expected service quality or service level. The virtualization technology has been widely applied in the data center, which is mainly attributed to its reliability, flexibility, and ease of management. With the virtualization technology, the number of physical servers in the data center can be dramatically reduced, thus significantly decreasing power consumption, lowering operational cost, and increasing corporate competitiveness. Is there any newer technology that can further reduce the virtual data center’s power consumption beyond the above technology? Built with the virtualization technology and the resource distribution policy, the research project, therefore, would apply the pre-defined scenario model to propose the scenario-based power saving technology in the virtual environment. At the same time, this research also integrates the Linux platform to develop the management module for the
SPS. Furthermore, the virtual infrastructure of the open source hypervisor can be installed as the standard to verify the model’s accuracy and to practicality examine the user interface’s feasibility.
The main purpose of this paper is to propose the Scenario-based Power Saving (called SPS) scheme in the virtual environment. Practically, the cloud servers in the virtual environment are not performing all the time even though the new generation data centers, which have already reduced an impressive amount of power consumption, as compared to the traditional data centers, after virtualized the servers. Therefore, we hope to design an advanced, scenario-based power saving mechanism. With the resource-monitoring mechanism, the utilization of all host servers could then be collected and analyzed. When the average utilization is relatively low, the power saving mechanism would then be initiated for achieving a higher level of power saving. In addition, to ease management and operation, we develop the Scenario-based Power Saving Manager (SPSM) for the SPS in this paper.
The remainder of this paper is organized as follows. Section 2 surveys the previous literatures and examines the related studies about SPS. Section 3 proposes and illustrates the system framework and design issues of the SPS. Section 4 explains the simulation environment and procedures, also conducts the performance evaluation and analysis based on the simulation results. Finally, we make the conclusions and indicate future research directions in Section 5.
2. RELATED WORKS
In a large-scale data center, the myriad of servers renders both power management and task performance significantly important. Nowadays, many management strategies have been developed; the common practice was the conversion of the hardware components into the low-voltage operation state for effectively reducing the server’s power consumption. However, it may be directly applied by the data centers relying on the virtualization technology because the virtual machines run simultaneously on the same physical server. If the state of a certain component was modified, it may affect the performance of all virtual machines [8, 16]. As a result, some studies have proposed the double-layered monitor structure based on the complete control theory. It should be noted that its main monitoring circuit adopted the multi-input-multi-output control method to maintain the load balancing of the virtual machine. The secondary performance monitoring circuit can subsequently manipulate the CPU frequency to achieve the effective power utilization [5].
Furthermore, for the power management of the event-driven processor, the time-driven mode was converted into the event-driven mode after considering the driver systems of operating system scheduling and variable frequency. By applying the Linux kernel’s power management function, it was estimated to save approximately 10% power, and conform to performance requirements at the same time [7, 11].
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources [12]. In other words, the cloud can be regarded as a large computer resource pool to be entrusted with various kinds of loads. When necessary, a cloud computing platform could dynamically deploy, assign, reinstall, and terminate the allocation of the servers [8, 11]. In a data center for cloud computing, power consumption is enormous. Fortunately, modern computers are equipped with variable frequency competency; different power consumptions are accomplished based on the variable frequencies. In order to optimize power consumption, there is an existing operation where we adopt the appropriate CPU frequency for the corresponding application program. However, this application program’s performance still needs to conform to the SLA requirements [14, 16]. The main challenge to the supplier of cloud computing is that the automatic management of the virtual server must take into consideration both the advanced QoS requests and the resource management costs [10]. Regarding cost management, a typical application was the reduction in the number of physical servers for reducing power expense costs. For resource management, the objectives of an automatic resource management system are to satisfy the following three requirements: (a) dynamic deployment and placement of virtual machines [12]; (b) support for heterogeneous application programs and workloads, including the online application program’s QoS requests and the CPU-intensive application programs; and (c) support for any application topology, such as the n-tier, single cluster, monolithic and scalable machines [15].
The computations of many applications must be performed in cloud [10]. After conducting the research, power management of the IDC (Internet Data Center) can be divided into four categories. The first category is the power management of the objectives and constraints, which deals with the tradeoff between the processing performance and the energy saving, such as whether the short-term power expenses exceed the budget allowed and whether there were any added efficiency limits [17]. The second one can be viewed as the solution concerned with the scope and granularity. If we compared the variations deployed in the hardware management policies, we may notice that even though these
resolutions were limited at the lower level, it will be more convenient to save and retrieve the system components. The third category is specified by the approaches utilized, such as the local server method, distributed scheduling or virtual machine integration [9, 11]. The last one solution is ways to use the power management solutions, including the DVFS (Dynamic Voltage/Frequency Scaling), system component activation and deactivation, sleeping, and other methods [5, 14]. Presently, the data centers’ power consumptions have impacted our living environment tremendously. Researchers are searching for new and effective solutions to reduce the data centers’ power consumption and meet with the expected service quality or SLO. In fact, virtualization technology has been widely applied to the data center environment mainly due to its reliability, flexibility, and ease of management. Therefore, some studies have proposed a green cloud structure to achieve the objective of reducing power consumption while ensuring service efficiency. The structure employed diverse online monitoring methods to acquire more comprehensive and complete system information. It also encompassed the strategies in live migration and optimized placement of virtual machines. In summary, this structure is also the important reference literature for this paper.
3. DESIGN ISSUES IN
SCENARIO-BASED POWER
SAVING
3.1 Design Issues of SPS Scheme
The primary technology of this paper constructs the scenario based on the pre-defined model. The Scenario-based Power Saving (SPS) scheme can automatically conduct the power distribution optimization in accordance with the pre-defined scenario. This technology consolidates the low resource used by virtual machines to a few specific host servers through live migration and also puts the unused host servers into standby mode for reducing the server’s power consumption. When resource requirements of workloads are increasing, it can then power on host servers backing to use virtual machines. Virtual machines are not affected by disruption or downtime. In addition, the power management technologies of different servers varied from one another. Therefore, more in-depth studies shall be conducted specifically for different power management technologies in order to meet various needs of the realistic environment.
For the predictable system positioned in a specific low utilization and time interval, the scheduling can automatically perform the live migration of the virtual machine and trigger the
standby mode action by using the pre-defined scenario model before the event takes place while achieving the performance and power saving objectives. Furthermore, the pre-defined scenario model also could be modified and created manually. Based on the actual operations in the virtual data center, define the virtual machine’s resource utilization threshold. When the resource utilization is under the threshold, the administrator would be automatically notified to apply the corresponding power saving measure(s).
The SPS workflow includes the following: a) confirm the virtual machine’s workload, select a host server with relatively sufficient resources, and perform the live migration of the virtual machines; b) notify the host server’s power management system to enter the standby mode; c) when resource requirements of the virtual machine are increasing, SPS can activate the host servers to use other virtual machines. Hence, the full operation of SPS as described above is illustrated in Figure 1.
Figure 1: The operational flow of SPS
3.2 SPS Algorithm Design
According to the operations of SPS, we first design the SPS algorithm and then propose the management module for SPS, called SPSM. SPSM is based on the RedHat Virtual Machine Manager (VMM), and its main function is to manage the operations of virtual machines (VMs). However, the resource allocation for the host server is relatively lacking, so we can make this feature on the basis of a set of VM management tools to be combined with automatic resource allocation scheme. The built-in
SLA (Service Level Agreement) provides alarm to help automatic monitoring the virtual machines and host servers’ resource utilization. With the real-time monitoring of each VM’s resource utilization by the VMM, the administrator can be notified by the e-mail in accordance with the reporting system of SLA, when the VM’s utilization is lower than the threshold set by the administrator. The SPSM can then be applied to perform the live migration of the VMs for ensuring all of the services will not be interrupted. While completing the live migration, none of the workload for VM is present on this host server. We are effectively putting the host server into standby mode to reduce power consumption. When resource requirements of workloads are increasing, the SPSM can power on the host server backing to use the virtual machine. It can utilize power management protocols to bring the host server out of standby mode. In this paper, we implement Wake-on-LAN as the power management protocol. Then, the live migration can then be activated to relocate some virtual machines backing to the server. As a result, each virtual machine’s performance can be guaranteed to meet the SLA requirements.
Based on the SPS workflow as depicted in Figure 1, the pseudo-code of the SPS algorithm can be designed as follows:
Algorithm SPS() BEGIN
Input:
//Users are to pre-define resource usage. UserDef.Low, UserDef.High as integer Output:
When resources usage gets lower for specific time interval
VMs will perform Live Migration to consolidate workload
Host will be put into standby mode
When resources usage gets higher for specific time interval
Host will bring out of standby mode
VMs will perform Live Migration to this Host Method:
Host.CPU.UsageRate as integer VM.CPU.UsageRate as interger Get UserDef.Low, UserDef.High while
// Execute SLA Resources Monitor in Virtual Machine Manager
if VM.CPU.UsageRate <= UserDef.Low or VM.CPU.UsageRate >= UserDef.High
// Event Controller switch action.type
case “1” : send notification email case “2” : send SNMP trap end switch
// SPS Manger (SPSM) switch event.type
case standby
if Host.CPU.UsageRate <= UserDef.Low Migrate VM to other Host
Place host in standby mode endif
case wakeup
if Host.CPU.UsageRate >= UserDef.High and any standby Host
Wake up Host
Migrate VMs to this Host endif end switch endif loop End END SPS.
4. EXPERIMENTS SETUP AND
PERFORMANCE EVALUATION
4.1 Setup of Experiments Environments
The virtualization platform of this paper is to be actually constructed based on the Xen hypervisor. The Xen hypervisor is the fastest and most secure virtualization infrastructure resolution. The Xen hypervisor creates the virtualization layer between the server hardware and the virtual machine. It is tasked with CPU scheduling and allocation of the memory units, which are used by all of the virtual machines. The construction of this virtualization layer has enabled each physical server to run more than one VM simultaneously. In this experimental environment, there are three physical servers (IBM x3650); two Servers A and B are installed with the Xen hypervisor. Another one is installed as the iSCSI target server for storing the virtual machine’s files. Two virtual machines are implemented on each Xen hypervisor server; each virtual machine is installed with the Windows Server 2010 and the cpubusy.vbs script will create a high processor load, resulting in contention for CPU cycles, as illustrated in Figure 2.
Figure 2: Simulations environment architecture The purpose of our experiments is to simulate the efficiency and effectiveness of power saving. When the workload of virtual machines is lower, the
virtual machines can be consolidated to a few servers. The replacement of these virtual machines utilizes the live migration technology, so each relocated virtual machine is able to continue the services without downtime. There are two scenarios of simulation experiments as follows:
Scenario 1: Assume that the virtual machine does not execute the cpubusy.vbs script and the virtual machine’s workload is lighter than at present, then the system automatically sends the notification via e-mail according to the SPSM mechanism. Upon receiving the notification from SPSM, the administrator can perform two tasks: (a) Live migration of the virtual machine by relocating the virtual machines operating on the Server A to the Server B and (b) Power-off Server A to put the host into standby mode for reducing power consumption.
Scenario 2: All of the virtual machines operating on the Server B can execute cpubusy.vbs for generating high CPU loads. When a drastic increasing in the server’s workload is realized, the SPS can perform two tasks: (a) Power on the standby server by the wake-on-LAN power management protocol and (b) the Balancing Server’s workload is done by migrating virtual machines to this server for providing the virtual machines with sufficient system resources and ensuring the good performance of virtual machines to meet the SLA.
4.2 Experimental Results and Performance Analysis
Assuming that the average power consumption during the operation of the server is Pavg, power consumption for the standby mode is Psm, and the average time duration for the server to enter into the standby mode is Tsm, the power that can be saved Psa is determined by the formula Equation (1).
Psa = (Pavg - Psm ) x Tsm (1)
In the simulation, the average power consumption measured for the server is 292W, and the measured power consumption for standby mode is 36W. Assuming that the host server’s standby time interval, Tsm is 6 hours, approximately 21% of the power consumption can be saved in the virtual environment with SPS mechanism activation. The overall benefits associated with the reduction of additional air-conditioning power consumption, which is attributed to the reduction in heat ventilation when the host server is in the standby mode, are not included. Moreover, once the workload of the virtual machines increases, this mechanism can also instantly power on the server and then perform live migration of virtual machines to balance host server’s workload and ensure virtual machines to acquire sufficient system resources to achieve the SLO requirements.
Then, we utilize the OS-owned performance monitoring tool to emulate the performance of these VMs and the host server’s resource utilization, while consolidating workloads to a few host servers and reduce power consumption. Therefore, we find that the VM’s performance is less affected compared with pre-migration performance, which is almost negligible. Before migration, we measured the global workload’s performance benchmark; the result is shown in Figure 3. The Server A will be put into standby mode as the system performing the power saving mechanism. All the virtual machines on Server A will be migrated to Server B. So there are four virtual machines running on Server B now. We measure virtual machine benchmark again and the result is shown in Figure 4.
Figure 3: Virtual Machine’s global performance benchmark on server A
Figure 4: Virtual Machine’s global performance benchmark on server B
In addition, due to the fact that the virtual machines are consolidated to another host server for operation, the utilization for the host server’s CPU and memory are thus enhanced. The CPU utilization
improved from 15% to 30%, and the memory utilization also increased as the migrated virtual machine’s need. The reason is that after assigning the Xen virtual machine, for which its memory is monopolistic, can then be directly allotted by the hypervisor.
From above experiments, we find that the VM which employed ARAS can obtain the online computing resources via live migration to meet their computation requirements as they needed an amount of resources. In the beginning, our experiment uses only one vCPU on the VM. Then, we add another vCPU while running resource is in high contention. Hence, the CPU benchmark results as shown in Figure 5 and Figure 6 indicate that two vCPU can obtain about twice the performance of one vCPU.
Figure 5: One vCPU benchmark
Figure 6: Two vCPU Usage benchmark Due to the performance benchmark tool in RAM memory, we only perform simulations for increasing virtual memory from 512 MB to 1024 MB, instead of replacing the memory module in the physical machine. Hence, the memory benchmark results as shown in Figure 7 indicate that both memory sizes of 512 MB and 1024 MB can obtain almost the same performance. As we know form the experiments, the increasing memory size is beneficial to the execution of memory-intensive applications in performance improvement.
Figure 7: Memory usage benchmark
Based on our simulations on Disk I/O usage, we find that the disk usages are significantly lower as shown in Figure 8 on the VM of Server A while the VM of Server B initiates the iometer task. In this event, there exist some other VMs that compete for disk I/O resources. The SPS will take some actions such as on-line migration from one VM to the other VM or accessing the disk resource for improving the disk I/O performance while it detects many I/O-intensive VMs on the same physical server. To illustrate the Figure 8, the disk I/O performance on the VM of Server A will be lower while we activate the iometer task of Server B. Since there are some VMs that access the same common I/O resources, we adopt the migration from VM of Server B to other Server host to relieve the I/O resource competition. Hence, this event observes that the disk usage of Server A can resume to better situation. Consequently, the simulation results prove that the SPS utilizes the on-line migration of VMs effectively and efficiently against I/O resource competitions.
Figure 8: Disk usage benchmark
5. CONCLUSION
This paper expects to conduct the real-time monitoring of the virtual machine’s resource utilization and to set the alarm conditions. When the conditions are in place, it can trigger the system to perform two tasks including (a) live migration and
consolidating the virtual machines into specific servers; and (b) putting the host server that currently does not have any virtual machines to be powered into standby mode for reducing the host server’s power consumption. Also, we employed a previous Scenario Model Inducer for automatic resource management in the virtual environment to design the SPS [17]. By utilizing the setup of the scenario model and developing the schedule of power saving mode, the applications of the SPS scheme can achieve a 21% reduction in power consumption, and enhance CPU and memory utilizations of servers. Also, the SPS manager is enabled to prearrange the virtual machine’s placement and put the host server into standby mode.
In this paper, we develop the technology required for achieving power saving and balance the host server’s workload. Utilizing the SPS proposed by this paper, it can lower operational costs and achieve power saving. Finally, the purposes of the proposed SPS are to attain better processing performance and obtain higher resource utilizations for saving energy and reducing carbon emission. Overall, the expected accomplishments of this paper can provide a performance-warranted virtual environment, thus setup a green and power-saving data center for cloud computing.
ACKNOWLEDGEMENTS
The authors would like to thank their research assistant Mr. Hsi-Hui Tseng for his much effort in programming and simulation.REFERENCES
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ABOUT THE AUTHORS
Shin-Jer Yang is currently a full Professor in the
Department of Computer Science and Information Management, Soochow University, Taipei, Taiwan. Professor Yang is the author/coauthor of more than 96 refereed technical papers (Journals and Conferences) on Wired/Wireless Communications Networking, Cloud Computing and Applications, Web/Internet Applications Design, and Network Management and Security. Also, he takes in charge of more than 23 research projects. His research interests include Networking Technologies and Applications, Cloud Computing and its Applications, Network Management and Security, Secure Web Applications Design, and Information Management. Also, he is a senior member of IEEE, ACM, and ISCA, respectively.
Sung-Shun Weng is currently Professor and Dean in
the College of Management at National Taipei University of Technology, Taiwan. He received his Ph.D. degree in Computer Science at Northwestern University, USA, in 1995. His current research and teaching interests are in the area of Business Intelligence Management. In particular, he is interested in Data Mining, Cloud Computing, Big Data, Mobile Commerce and Customer Relationship Management.
(Received January 2014, revised February 2014, accepted March 2014)
