Adaptive Power Panel of Cloud Computing ...

Adaptive Power Panel of Cloud Computing Controlling Cloud Power Consumption Nour M. Azmy

Islam A.M. El-Maddah

Hoda K. Mohamed

Ain Shams University Cairo, Egypt

(9580, islam.elmadah, hoda.korashy)@eng.asu.edu.eg ABSTRACT Cloud computing had created a new era of network design, where end-users can get their required services without having to purchase expensive infrastructure or even to care about troubleshooting. Power consumption is a challenge facing the Cloud Providers to operate their Datacenters. One solution to overcome this is the Virtual Machine (VM) migration, which is a technique used to switch under-utilized hosts to sleep mode in order to save power, and to avoid over-utilized hosts from Service Level Agreement (SLA) violation. But still the problem is that the Cloud Service Provider apply a single policy on all nodes. Our proposed solution is an adaptive power panel where different policies can be applied based on both of the nature of the tasks running on hosts, and the Cloud Provider decision.

Keywords Green Computing, Adaptive; Virtual Machine; Allocation; Placement; Selection; Migration; Cloudsim.

1. INTRODUCTION Cloud computing uses the technology of virtualization in order to manage the physical resources of the datacenter nodes [1], which is beneficial for each virtual machine (VM), as give it the needed resources based on the workload, the customer requirements, and also for better utilization of the physical machines resources. The pay-per-use model is based on the VMs as many users can share the same physical machine with or without sharing the resource depending on the cloud provider Service Level Agreement, where the VMs can be Time shared or Space shared. The agreement between the cloud provider and the user or broker is called SLA that is negotiated between them to specify the QOS requirements of the user or broker with the required MIPS, RAM, storage, bandwidth, response time, etc. in which the cloud provider should obey in order to get the rent, customer satisfaction, and avoid penalties in the case of the SLA violation. Cloud is presented in three different types of services which are Infrastructure As A Service (IAAS), Platform As A Service (PAAS), and Software As A Service (SAAS) where every service got its own permissions and limitation for the user or broker that will use these services [2], so every user, broker, or organization can specify which service best fit their working environment; Figure 1 [3] Describes the different types of cloud services and the market motivation for each service.

Figure 1. Cloud Computing Service Model Hierarchy There are many different types of applications where every type needs its own deployment scenarios and the cloud provider should be flexible enough to handle all kinds of customer requirements [4], e.g. there are types of applications that need very high response time so the deployment may need more Processing elements and RAMs while there are types of applications need more storage so the deployment with more storage capacity will be a good decision [5] and so on, so the vitalization technology allow different VMs deployment with different resource distribution. A key factor of the cloud is its scalability feature. In addition, Datacenter expansion became an essential task for the cloud providers. This entails the need for a considerable high power. The power consumption became a challenge that should be considered not only for the electricity bill but also for the heat pollution, and the carbon dioxide emission. Many techniques had studied decreasing the power consumption like Dynamic Voltage and Frequency Scaling [6] denoted later in this paper as DVFS, and VM Migration from the low utilized nodes to another nodes in order to turn some nodes to sleep mode which will save a lot of power, but here another challenge appears which is the power VS performance tradeoffs, as if the VMs are migrated to highly utilized nodes there will be SLA violation that will result in penalties. In this paper we survey VM Migration by discussing VM Allocation, Selection, and Placement policies to use them in making an adaptive power panel configured by the cloud owner or admin. In section II we will discuss briefly the related works. VM allocation, selection, and placement policies are discussed in details in section III. Section IV introduces our contribution in adaptive power panel. After that we will show our Results in section V. And finally conclusion and future direction are presented in section VI.

2. RELATED WORK In [6], Hai Li et al. introduced the variability in supplying voltage to the processors in order to reduce their power consumption. Their idea was to reduce the voltage, hence reduce the frequency, which will result in dynamic voltage frequency scaling –DVFS, during a decade of time where the power consumption of the processors decreased hence

decrease the heat of the chip where the cooling point of that processor may also work in low power mode, but reducing the frequency will affect the performance of these processors, the concept behind that idea is that the pipeline processor make some misses in the computations, so it halts and wait for the required data to continue the computation, during that waiting time they could take advantage by reducing the frequency of the processor and then increase it again to support the maximum performance again, but the fact that idle processor consumes 0.7 of its maximum energy [7], makes the need for VM migration over the DVFS in order to switch the processor to sleep mode or even switch it off. In [8], Ziming Zhang et al. developed an adaptive power management framework where the authors estimated the power consumption from the VM utilization and configuration instead of estimating the power from a physical machine as many researchers used to estimate the power consumption based on a specific server with specific components. In their architecture, each physical node gets its own Virtual Machine Monitor (VMM) regardless the physical node. These VMMs communicate with their Power Estimation Model (PEM) and provide it with their current utilization and configuration. They also communicate with the Cloud Resource Manager (CRM) and provide it with their current performance and again the configuration. The output of the PEM is the estimated power consumption based on the input parameters and hence send that result to the CRM. Based on all the inputs to the Cloud, it will make a feedback decision with the new configuration. In [9], H.A Selmy et al. discussed the Dynamic VM Migration policies using the live online migration. Then the authors introduce many approaches to VM Selection Policy based on Neural Networks, Self Organizing MAP, and KClustering, but based on the results, the SLA Violation of these techniques are high, also the power consumption of the SOM and K-Clustering are relatively high. In [10], S.R. Hussein et al. discussed the performance parameters that the authors study, i.e. CPU utilization, SLA percentage, Average SLA percentage, VM migration number and other parameters; Moreover, they discussed the Cloud operation messages flow between the Datacenter, Cloud Information Service, and Brokers in order to submit a specific Cloudlet. Then the authors lists the VM placement, allocation, and selection algorithm to compare this algorithm with their fuzzy-based contribution in VM placement and allocation policies. But the authors didn’t guarantee to place the VM in the best power aware node as in the FRBS algorithm the output of the allocated host is the first host that can handle the placement request, which may result after a while in reallocation of that VM, and Execution time. VM reallocation mean and SLA violation are very high compared to the other techniques. CloudSim [11] is a framework toolkit Java project for modeling and simulating different cloud computing scenarios, infrastructure, configuration, etc. This toolkit enables modeling the behavior of the Datacenters, physical nodes, VMs [12], and even the brokers and the tasks needed to be submitted to the cloud which called cloudlets. Researchers and even enterprises can implement their configuration scenario, topology, and even the hardware specs of the server nodes which can help them to test their ideas and compare between them in order to simulate it before the real implementation. Also, the CloudSim can simulate many VM allocation policies, VM selection policies, resources scheduling policies for both VMs and Cloudlets and many power related attributes [13].

3. FOUNDATION 3.1. Performance Analysis 3.1.1. Power Equation The power consumption of the cloud is affected by many factors, so in order to operate our Datacenter, the power consumed by the components of the physical nodes is not the only source of the consumption, but also the cooling requirements consume a huge amount of energy. The most noticeable factor is the CPU as it has the greatest share of the node’s consumption percentage, but this does not imply neglecting other components e.g. RAM, HDD, Network Adaptors, and FANs. Many studies aims to reduce the CPU power by applying the DVFS technique; However, based on a study in [7], the idle CPU consumes 0.7 of its maximum energy as shown in equation (1), makes the direction to turn unutilized idle node off [14, 15], as the power consumption of a datacenter decreases as to the number of nodes that had shutdown increases, as it depend on another factors other than the CPU [16] as shown in equation (2). ( )

(

)

)

((

)

(1) (2)

Where Pmax is the maximum power if the CPU utilization is 100%, U is the current utilization, P(U) is the power at a specific CPU utilization U, and constant K = 0.7. But the CPU utilization is time dependent not constant, so the factor time (t) should be added to our previous equation as shown in equation (3). ( ( ))

(

)

)

((

( ))

(3)

Moreover, the Energy consumed in a time interval t1- t0 is shown in equation (4).

∫

( ( ))

(4)

3.1.1. SLA Violation The SLA is the agreement and the contract between the cloud provider and the broker, then the broker can provide another service to an end user, which require another SLA between the Broker and the end user. All negotiate on the lease time interval, the price, Moreover the needed resources, i.e. MIPS, RAM, Storage, Response time, etc., which should be allocated to that broker in order to submit some tasks (cloudlets); The Cloud Service Provider should guarantee the availability as well as the performance of their service [17]. The violation of the SLA will be translated into financial penalty on the cloud provider side, so the percentage of the SLA violation is the amount of resources the is requested but not allocated over the total requested resources ,and resources are replaced by MIPS in the case of PaaS and SaaS as shown in equation (5). ∑

(∑ (∑

) )

(5)

Over utilized hosts result in SLA Violation, So VM Migration from over utilized hosts to less utilized hosts is one of the cloud advantages.

3.2. VM Allocation Policy The first step is to find the over utilized hosts in order to migrate one or more VMs to another host, so upper threshold

can determine either a host is utilized or not; The following Policies are already implemented in CloudSim.

3.2.4. Local Regression, Regression

3.2.1. Static Threshold

Both LR and LRR methods try to fit a curve over a given values, but the LRR is more robust and not vulnerable to outliers [18].

Upper threshold is assumed based on previous knowledge or trial and error; This policy is different from the other policies which will be discussed next as it eliminates the threshold calculation step, but it also got drawbacks e.g. it’s not suitable with all workloads.

3.2.2. Median Absolute Deviation (MAD) A Stable statistical estimator, MAD is not affected by the small changes as it is median based measurement as shown in equation (6) which don’t depend on mean which is affected a lot by large weighted values and neglect small values.

(|

(

)|)

(6)

Where X is a set of the nodes’ utilization percentage, so the MAD can get the deviation of the CPU utilization as the more the CPU utilization is deviated, the more likely the CPU utilization will be high [18], then we use the MAD method to get the threshold as shown in equation (7).

(

)

(7)

Where Tu is the upper threshold, and s is a safety parameter which made the cloud admin control the weight of the power over the performance, as small s results in high upper threshold that means only very high utilized host will be considered, so the workload will operate in small number of hosts and the rest of the hosts will be in sleep mode, which implies less energy consumption but high SLA violation.

3.2.3. Interquartile Range (IQR) Again another method that use the median in its calculation to avoid the mean limitation with the dominating of large numbers over smaller numbers. The IQR idea is to find the median of a set in order to divide that set into two parts and get the median of each set, then subtract both medians as shown in equation (8).

(8) Where Q3 and Q1 are the medians of the two sets. So we can use (7) again but replace the MAD by the IQR to estimate the threshold as shown in equation (9).

(

)

(9)

Robust

Local

3.3. VM Selection Policy After the VM Allocation stage, which marks the over utilized hosts if they hit the threshold; One or more VMs will be selected for migration to other less utilized host.

3.3.1. Maximum Correlation (MC) Policy The VM that got the highest correlated value to the node’s CPU among all the other VMs is most likely that it will be the VM that consumes the most resources and makes that host over utilized, so it will be migrated.

3.3.2. Minimum Migration Time (MMT) Policy Fast response on SLA violation is needed to avoid big penalties, so this policy will chose the VM that needs minimum time to be migrated from over utilized host to another host. Finding the VM that needs the migration time is simply by dividing the currently RAM usage of the VM over the B.W. of the over utilized host, based on an assumption that all the VMs are sharing the same bandwidth, so the VM that will be chosen to be migrated is the VM that allocates the least amount of RAM as shown in equation (10). (10) Where RAMVMi is the least amount of RAM consumed among all the VMs, NET. B.W is the available Bandwidth for the host.

3.3.3. Random Selection (RS) Policy Randomly select a VM based on a random uniform distribution policy.

3.3.4. Fuzzy Logic Policy Using the Rule-Based Fuzzy algorithm, a combination of the VM attributes will result to select appropriate VM [10], i.e. “Fuzzifying the VM attributes, then Applying IF-THEN rules, then defuzzifying the fuzzy output to get a crisp value, which will affect the selection decision”

Figure 2. Proposed Power Panel Flow Chart

3.3.5. Other Artificial Intelligent Polices

Figure 3. Proposed Power Panel Modes

Based on VMs’ attributes as inputs i.e. “current CPU utilization, CPU utilization history mean, MIPS, RAM” [9], Neural Networks, Self Organizing Map, and K-Means can classify those VMs into different clusters as outputs, so the selection of the VM to be migrated is based the VM of a specific cluster.

3.4. VM Placement Policy The final step is to choose which host to place the selected VM in. Similarly Bin-packing [19] problem, but to adapt that technique, [7] had modify the original best fitting decreasing (BFD) to be power aware BFD (PABFD), which is to arrange the VMs in descending order based on their CPU utilization as it’s the most factor that affects the power then chose the most power efficient node first to place the VMs.

4. PROPOSED ADAPTIVE PANEL Many studies [9, 10, 20, 21] proposed methods to improve the power consumption but with SLA violation percentage. The requirements of the brokers vary depending on the nature of their business. if the cloud provider chooses to apply only one policy on their datacenter, that will affect either the power consumption or the SLA violation percentage to whole of their system, as if the provider choose to apply VM Migration policies to save power with high preference, then high SLA violation takes place, while if the provider choose to apply different policies where high performance was their target, then power consumption will be high. Figure 2 shows our proposed solution for this challenge, in which the cloud provider divide the Datacenter hosts into clusters, where each cluster got its own setup based on the customer requirements, and based on these setups different policies will be applied, also each cloudlet will be applied to a specific cluster to share the same setting of its cluster; Moreover, each cluster can adjust its mode based on time and change its policy independently from the other clusters in which to adapt the power VS. performance tradeoff, so we propose an adaptive power panel for each cluster that can be manually or automatically setup based on SLA Violation percentage acceptance of a specific cluster. The proposed adaptive power panel will get three modes. Figure 3 describes awareness relativity of power and SLA violation of these modes. The first the power aware mode which will aim to achieve the most power efficient policies with a little aware to SLA violation percentage. The second mode will be a balanced mode which will compromise between the first two modes, in which in that mode our objective will be to adjust the tradeoffs between the power and the performance. The third mode is high-performance mode which will care about having the least SLA violation percentage. Moreover, figure 4 shows each of the three modes in which the cloud service provider can be applied by manipulating the safety parameter of the VM Allocation policies [18].

Figure 4. Safety Parameter Effect

4.1. Power Aware Mode The Power Aware mode targets to consume as less power as possible regardless the performance degradation that will affect the SLA violation percentage. That mode can be applied in many cases e.g. power shortage in specific clusters, so in order to increase the uptime of these clusters, power aware policies are applied, also if the customer or the broker accept a high percentage of SLA violation. That mode can be achieved by keeping highly utilized hosts without VM Migration unless the host is highly utilized, by applying the VM allocation policies with relatively low safety parameter.

4.2. High Performance Mode Unlike the Power Aware mode, the High Performance mode objective is to achieve the least SLA violation percentage regardless the power consumption percentage, this mode can be used in the clusters where the brokers need very high quality of service. That mode can be achieved by migrating VMs before the host is highly utilized, by applying the VM allocation policies with relatively high safety parameter.

4.3. Balanced Mode This mode aims to balance between the previous two modes by adjusting the tradeoffs between the power and the performance, we propose that mode to be the default mode for all the clusters, while the other two modes are adjustable by the cloud provider on demand, either to save power or to deliver high quality of service which results in low SLA violation percentage.

5. PERFORMANCE EVALUATION AND RESULTS 5.1. Simulation Configuration In our Testbed, we choose Cloudsim toolkit to simulate the proposed technique. Moreover we use a real data analysis of a project called CoMon, which is a part of CoDeeN project , that uses a huge Datacenter across the world called PlanetLab, where a monitor for real VM workload Analysis is provided. In our setup we use one datacenter that contains 50 hosts, with two types of predefined hosts as shown in Table 1. We

aim to operate 100 VMs whose characteristics are shown in Table 2.

Table 1. Hosts Characteristics

Table 2. VM Characteristics

of 0.8, the Balanced mode’s safety parameter with a value of 1, and finally the safety parameter of High Performance mode with a value of 1.2. Tables 3 ,4 presents our results, where in the best case with LRR as the selection policy, MC as the allocation policy, and with 1.2 safety parameter, the SLA Violation is 0.06%, but with our proposed technique combined with the same policies, the SLA of the balanced mode and the high performance mode achieve better results. Even that the accumulative power consumption had increased in that experiment, but in other setups where the power saving mode SLA contracts is more than the high performance, and balanced mode, the accumulative power consumption will be acceptable; As the SLA V.S. Power consumption tradeoffs will be adjusted. So that each broker request will be supplied with a suitable cluster.

6. CONCLUSION DIRECTION We will run the simulation four times, the first three time aims to apply the proposed three modes separately, then the fourth time is to apply all of the three modes combined on our testbed, by assuming that 30 VMs requires Power Saving mode, 30 VMs requires high performance mode, and 40 VMs requires balanced mode. We assume that the Power Saving, Balanced, and High performance clusters have 30, 40, and 30 Nodes respectively.

5.2.Results For the proof of concept, we apply two combination of Selection and Allocation policies. Local Regression (LR) and Robust Local Regression (LRR) with Minimum Migration Time (MMT) and Maximum Correlation (MC) respectively are applied. The safety parameter is the attribute that we propose to manipulate, in order to change our cluster mode, so for the Power Saving mode we use the safety parameter with a value

AND

FUTURE

Adapting the VM allocation, selection, and placement policies can reduce the energy consumption which is attractive to the cloud providers. On the other hand, it can reduce SLA violation percentage which is attractive to the brokers, and the users. Applying only one policy may not be suitable for all circumstances, so a power panel should control those policies in order to satisfy all the cases e.g. power aware, performance aware, or moderated clusters. For our future work, we will introduce automation for the cluster selection to our proposed solution. Secondly, a study on time based rules to change a cluster’s mode based on that cluster behavior history.

7. REFERENCES [1]

N. da Fonseca, R. Boutaba. 2015. Virtualization in the cloud. Wiley-IEEE Press eBook Chapters, pp. 432.

Table 4. Results of Each mode separately and after applying proposed adaptive power panel

[2]

A. Anasuya. 2011. J. Cloud Infrastructure Service

Table 3. Results when applying proposed adaptive power panel in details

Management – A Review. unpublished. Website: http://www.teachmecloud.net/introduction.html#service s-cloud [4] R. Buyya, CS Yeo, S. Venugopal, J. Broberg, and I. Brandic. 2009 Cloud Computing and Emerging IT Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility, Future Generation Computer Systems. [5] A. Ghafarian, 2015 Foreniscs analysis of cloud computing [3]

[13]

[14]

services Science and Information Conference (SAI), , London, pp. 1335 - 1339. [6]

[7]

[8]

[9]

[10]

[11]

[12]

H. Li, C. Cher, T. Vijaykumar, and K. Roy. 2003. Vsv: L2-miss-driven variable supply-voltage scaling for low power. In Proceedings of the IEEE International Symposium on Microarchitecture (MICRO-36). A. Beloglazov, J. Abawajy, and R. Buyya. 2011. Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing Z. Zhang, Q. Guan, S. Fu. December 2012 “An adaptive power management framework for autonomin resource configuration in cloud computing infrastructure. Performance Computing and Communications Conference (IPCCC), 2012 IEEE 31st International. H.A. Selmy, Y. Alkabani, H.K. Mohamed. 2014. Energy efficient resource management for Cloud Computing Environment. Computer Engineering & Systems (ICCES), 2014 9th International Conference , pp.415 - 420 S.R. Hussein, Y. Alkabani, H.K. Mohamed. 2014. Green cloud computing: Datacenters power management policies and algorithms. Computer Engineering & Systems (ICCES), 2014 9th International Conference , pp. 421 - 426 Website: http://www.cloudbus.org/cloudsim/ A Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services,CloudSim project. By R. Buyya, R.N. Calheiros, N. Grozev. Buyya R, Ranjan R, Calheiros RN. 2009. Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities. Proceedings of the Conference on High Performance

[15]

[16]

[17]

[18]

[19]

[20]

[21]

Computing and Simulation (HPCS 2009), Leipzig, Germany. IEEE Press: New York, U.S.A., 21–24 June 2009;pp. 1–11. Calheiros, R. N., Ranjan, R., Beloglazov, A., De Rose C. A. F., Buyya, R. 2011. CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms. Software: Practice and Experience (SPE), Vol. 41 (Issue1), pp. 23-50. E. Elnozahy, M. Kistler, R. Rajamony. 2003. Energyefficient server clusters, Power-Aware Computer Systems. pp.179–197. E. Pinheiro, R. Bianchini, E.V. Carrera, T. Heath. 2001. Load balancing and unbalancing for power and performancee in cluster-based systems, in: Proceedings of the Workshop on Compilers and Operating Systems for Low Power. pp.182–195. A. Narayan, S.Rao. 2014 Power-Aware Cloud Metering. Services Computing, IEEE Transactions Volume: 7, Issue: 3,pp.440-451 S. Sakr, A. Liu. 2012. SLA-Based and ConsumerCentric Dynamic Provisioning for Cloud Databases, Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on. pp 360 - 367 A. Beloglazov, R. Buyya. 2011. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Wiley Online Library. Yue M. 1991 A simple proof of the inequality FFD(L)< 11/9 OPT (L)+ 1,for all l for the FFD bin-packing algorithm. ActaMathematicae Applicatae Sinica (English Series); 7(4): pp. 321–331. P. Dalapati, and G. Sahoo. 2013. Green Solution for Cloud Computing with Load Balancing and Power Consumption Management. M. Seddiki, R. Prado, J. Enrique, and S. Garcia. 2014. Fuzzy Rule-Based Systems for Optimizing Power Consumption in Data Centers. IEEE Image Processing and Communications Challenges 5: Advances in Intelligent Systems and Computing, vol. 233, pp 301308.