Cost-effective Cloud Services for HPC in the Cloud: The IaaS or
The HaaS?
Ifeanyi P. Egwutuoha
1, Shiping Chen
2, David Levy
1, and Rafael Calvo
1 1School of Electrical & Information Engineering, The University of Sydney, Australia
Email: {ifeanyi.egwutuoha, david.levy, rafael.calvo}@sydney.edu.au
2Information Engineering Laboratory, CSIRO ICT Centre, Australia
Email: shiping.chen@csiro.au
Abstract— In the scientific research domain, traditional High Performance Computing (HPC) refers to the use of supercomputers, grid environments and/or clusters of com-puters to solve computation-intensive and/or data-intensive problems. The traditional HPC systems are expensive and sometimes require huge start-up investment, technical and administrative support and job queuing. With the benefits of cloud computing, cloud services such as Infrastructure as a Service (IaaS) and Hardware as a Service (HaaS), enables scientists and researchers to run their HPC applications in the cloud without upfront investment associated with the traditional HPC infrastructure. In this paper we analyze the computational performance and dollar cost of running HPC applications in the cloud when IaaS or HaaS is leased. We find that HaaS significantly reduces the cost of running HPC application in the cloud by 20% compare to IaaS without significant impact to application’s performance. We also found that there is a substantial improvement in computational performance in HaaS compare to IaaS. Keywords:HPC, cloud computing, HaaS, computation-intensive applications, computational performance
1. Introduction
In the scientific research domain, traditional High Per-formance Computing (HPC) refers to the use of supercom-puters, grid environments and/or clusters of computers to solve computation-intensive problems. Some common uses of HPC systems include weather forecasting, aircraft crash simulations, computational fluid dynamics for aerodynamics studies and many other computation-intensive applications [14], [20]. Today, HPC systems also offer new opportunities in business. For example, in financial institutions HPC systems are used in real time modelling to make informed investment decisions. The most powerful HPC systems are ranked on top500 [1]. Huge capital is needed to acquire the HPC systems, this makes it difficult for research communi-ties. Until recently, HPC systems would have been out of reach for most research communities.
With the recent advancement in computing technologies, computation-intensive applications are not only executed in the traditional HPC systems but also in HPC system
in the cloud. Cloud computing [2], [3], [8] is a revolu-tionary computing paradigm for storing data and running applications, including computation-intensive applications. It promises numerous benefits, which includes, no upfront investments. Cloud computing also reduces development time, staff (e.g., administrators), and hardware, resulting in better service and significant cost saving. It is expected that more computation-intensive applications will be deployed and run in HPC systems in the cloud [5], [3]. Furthermore, the Amazon Elastic Compute Cloud (Amazon EC2) cluster recently appeared in TOP500 list [1], which shows that there is a great future for HPC systems in the cloud.
With Cloud computing pay-as-you-go pricing model, sci-entists and researchers can lease cloud services such as Infrastructure as a Service (IaaS) and Hardware as a Ser-vice (HaaS) for computation-intensive applications. These services are relinquished when not in use. This avoids the job queuing, which is a common phenomenon in traditional HPC system. The price model is also attractive when compared to traditional HPC systems that require huge investments capital, administrative issues and allocation policies.
However, the cost of running HPC application on the cloud may be high if the cloud services are not well understood and the cost-effective cloud services chosen. If the dollar cost of running HPC applications in the cloud is high comparing to traditional HPC system, then the benefits of running computation-intensive application on the cloud may have been defected. HPC research communities are concerned about the cost and computational performance of different cloud services.
In this paper we analyze the computational performance and dollar cost of running computation-intensive application in HPC systems in the cloud when IaaS and HaaS are leased. We find that the cost of executing computation-intensive application when HaaS is leased is significantly lower compared to the IaaS model. We show that there is significant improvement in computational performance of the application on HaaS if the computation-intensive application is not a network intensive application. Our ex-perimental setup uses the Message Passing Interface (MPI) implementation [4]. We provide our test results, but do not reveal the identify of the cloud providers, to avoid any
head-to-head comparisons. However, we do include the relevant technical details of the cloud instances.
In Section 2 we present the overview of Cloud services for HPC systems in the clouds, while Section 3 presents the experimental setup. MPI applications and benchmark are presented in Section 4. Experimental results and Cost analysis are presented in Section 5 and 6 respectively, while Section 7 discusses related work. Finally, some conclusions are presented in Section 8.
2. Overview of Cloud Services for HPC
Systems in the cloud
With the advent of cloud computing infrastructures, cloud services providers such as Salesforce.com, Amazon [5], Rackspace, Baremetalcloud [6], Microsoft Azure, SoftLayer [7], Google, IBM offer different cloud services to cloud users. Some of these services offered are Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS), Hardware as a Service (HaaS), Network as a Service (NaaS) and Storage as a Service (STaaS). Based on the capability provided by the cloud service provider, cloud computing services fall into four major competing categories [8], [9]. Application as a Service, Platform as a Service, Infrastructure as a Service and Hardware as a Service. Figure 1 shows the architecture of the cloud computing services.
Software-as-a-Service (SaaS) Platform-as-a-Service (PaaS) (Developers implementing cloud applications)
Infrastructure-as-a-Service (IaaS) [(Virtualization, Storage Network) as-a-Service]
Hardware as a Service (HaaS)
Fig. 1: Cloud layered architecture [8]
Software as a Service (SaaS) is the highest abstraction level in the cloud. It offers cloud users ready-to-use on-line applications that are already deployed in the cloud. This layer is hidden from the users and managed by the SaaS providers. The users do not know where or how these applications are deployed, but simply use them. SaaS cloud applications can be accessed via the internet with any internet-ready device such as a laptops, smart phones, or iPads. This enables relatively dumb clients to perform complex tasks, by shifting the real work, transparently to the user, into the cloud. A good example of SaaS is the commonly used Gmail (email services) provided by Google. Platform as a Service (PaaS) provides cloud users with a fully configured and managed computing platform, ready to run custom software developed by the users. Each PaaS platform is targeted to software developed in a specific
programming language or software framework (e.g., Java EE) and ready to execute corresponding builds. PaaS cloud users deploy and run their software, without setting up servers and software stacks, without thinking about scala-bility or clustering, and often even without knowing how many computers or CPUs their application will run on.
Infrastructure as a Service (IaaS) is similar to HaaS, but virtual machines are rented out instead of real hardware. IaaS cloud users have to install, configure, and maintain the virtual machines they rent and are free to choose the operating system and software stack they install in their VMs. Often IaaS users make use of a pre-installed and preconfigured VM image supplied by their provider as base installation. Users do not have root access to the hardware. A good example of cloud provider that offers IaaS for HPC applications is Amazon [5]. The Amazon Elastic Compute Cloud (Amazon EC2) offers cluster compute instances for HPC applications.
Hardware as a Service (HaaS); in this case, the cloud provider basically rents out ‘bare-metal’ hardware (e.g., server/host and storage). Notable examples of cloud providers that offer HaaS are Baremetalcloud [6] and Soft-Layer [7]. Cloud users connect to HaaS via the Internet, install and configure (e.g., VMs) the server they leased. Cloud users choose HaaS, because it gives them full control of the server, operating system, and software/hardware stack, as well as the number of VMs they execute on it. Research communities do lease HaaS for computation-intensive and/or data-intensive applications and configure HPC systems ac-cording to their needs [8], [9]. Consequently, computation-intensive applications that were traditionally run on HPC systems can now be executed in the cloud. Figure 2 shows the HaaS architecture and access level of the provider and user. Dom0 Drivers Back end Xen hypervisor Hardware [Disk, NIC, CPU, and RAM]
DomU1 MPI applications Front end DomU... MPI applications Front end DomU... MPI applications Front end DomUn MPI applications Front end Virtual CPU and RAM
Users (eg., Scientist)
Provider (e.g., baremetalcloud)
Fig. 2: An example of HaaS architecture with level of involvement of key players
3. Experimental setup
We setup experimental environments to evaluate the computational performance and dollar cost of running computation-intensive application on IaaS and HaaS. Our experimental setup includes two services we have leased
from two cloud service providers; for the purpose of avoid-ing head-to-head comparison of the two cloud providers, we call them Cloud-A and Cloud-B. Cloud-A offers IaaS in different kind of cluster instances for HPC applications: for example, cluster compute instances. Cloud-B offers HaaS which can be configured to run HPC applications.
3.1 Cluster Compute Instances from Cloud-A
(IaaS)
Cloud-A is one of the major cloud service providers. They offer IaaS in different instances for HPC applications. Table 1 shows a sample of cluster compute instances with price details of on-demand instances from cloud providers. The clusters compute instances are available with commonly used Operating System (OS) (Windows and Linux) in 32-bit and 64-32-bit platforms. For our experiments, we choose the Ref-C virtual instance in the Table 1 because it is widely used for HPC applications. The instances use Xen full virtualization. The I/O network communication between the cluster instance is 10 Gigabit Ethernet.
In order to compare the computational performance and dollar cost of running HPC applications when IaaS and HaaS services are leased. We leased a cluster compute instance with a total of 16 processors. The details of the leased cluster compute instance are shown in Table 2. We installed OpenMPI 1.6 [23] on the node. OpenMPI is an open source implementation of the Message Passage Interface (MPI).
Table 1: Virtual and HaaS Instances from Cloud-A and Cloud-B
Instance type Memory CPU Disk
Cost of in-stance for Linux Cost of in-stance Win-dow Ref-A Virtual instance 30 GB 2x2.0 GHz (sixteen-core) 500 GB $1.600 per hour $1.800 per hour Ref-B Virtual instance 244 GB 2 x Intel Xeon E5-2670 (eight-core) 240 GB $3.500 per hour $3.831 per hour Ref-C Virtual instance 22 GB 2 x Intel Xeon X5570 (quad-core) 1690 GB $2.100 per hour $2.600 per hour Ref-D Virtual instance 23.00 GB 2 x Intel Xeon X5570 (quad-core) 1690 GB $1.30 per hour $1.610 per hour Ref-E Hardware Instance 96 GB DDR3-1333 2x2.13 GHz E5606 (eight-core) 1000.0GB, 7200RPM 0.99 per hour $1.19 per hour Ref-F Hardware Instance 48 GB DDR3-1066 2x2.66 GHz X5650 (twelve-core) 300GB, 10000RPM 0.73 per hour $0.93 per hour Ref-G Hardware Instance 64 GB 2x2.0 GHz E5-2650-OctoCore (sixteen-core) 500.0GB 1.54 per hour $1.59 per hour Ref-H Hardware Instance 32 GB 2x2.0 GHz (eight-core) 250.0GB 1.25 per hour $1.3 per hour
3.2 HPC system on HaaS in the cloud
As explained in Section II, HaaS allows users to have full control of the system and control environment for mea-suring system performance and other available experiments. This enables users to determine the number of VMs to be deployed for HPC applications. We have leased an HaaS instance (Ref-G) with 64GB RAM from Cloud-B. Table 1 shows some of the cloud services that the HaaS providers offer that are similar to cluster compute instances that Cloud-A offers. The table also gives a summary of HaaS and price of the service leased. The communication network between each HaaS is a 1 Gigabit Ethernet.
The summary of the VM we provisioned on the HaaS is shown in Table 2. We installed Xen hypervisor [11] on the host. Xen hypervisor is an open source, industry standard virtualization technology. Linux Operating System (Ubuntu 12.4 64-bit) runs on top of the Xen hypervisor. We imported our pre-configured para-virtualised guest OS (Ubuntu 12.4 64-bit) on the HaaS instance. The pre-configured para-virtualised guest reduces the time to setup the HPC system on the HaaS instance. A para-virtualized OS uses a modified kernel, and reduces the size of the image. The VM is configured to have 16 processors with 60GB memory and 200GB hard drive. We installed OpenMPI on the node. This setup is almost equivalent to the cluster compute instances we leased from Cloud-A. The setup also allow us to have a good comparison environment for IaaS and HaaS in terms of computational performance and dollar cost. Table 2 shows both the IaaS and HaaS environments we used.
Table 2: Computational environment for IaaS and HaaS
Cloud&A,)VM)of)IaaS Cloud&B,)VM)of)HaaS RAM:%24%GB% RAM:%60%GB% Architecture:%%%%%%%%%%x86_64 Architecture:%%%%%%%%%%x86_64 CPU%op;mode(s):%%%%%%%%32;bit,%64;bit CPU%op;mode(s):%%%%%%%%64;bit CPU(s):%%%%%%%%%%%%%%%%16 CPU(s):%%%%%%%%%%%%%%%%16 On;line%CPU(s)%list:%%%0;15 Thread(s)%per%core:%%%%16 Thread(s)%per%core:%%%%2 Core(s)%per%socket:%%%%1 Core(s)%per%socket:%%%%4 CPU%socket(s):%%%%%%%%%1 NUMA%node(s):%%%%%%%%%%1 NUMA%node(s):%%%%%%%1 Vendor%ID:%%%%%%%%%%%%%GenuineIntel Vendor%ID:%%%%%%%%%%%%%GenuineIntel CPU%family:%%%%%%%%%%%%6 CPU%family:%%%%%%%%%%%6 Model:%%%%%%%%%%%%%%%%%%%26 Model:%%%%%%%%%%%%%%%%%26 Stepping:%%%%%%%%%%%%%%%%5 Stepping:%%%%%%%%%%%%%%5 CPU%MHz:%%%%%%%%%%%%%%%2933.440 CPU%MHz:%%%%%%%%%%%%%%%2266.796 Hypervisor%vendor:%%%%%Xen Hypervisor%vendor:%%%%%Xen Virtualization%type:%%%full Virtualization%type:%%%para
4. MPI applications and benchmark
We used a commonly used HPC benchmark and real HPC application to analyze and evaluate the MPI applications
running on IaaS and HaaS services. The benchmark was the High Performance Linpack (HPL) benchmark [12] and the application was ClustalW-MPI [14]. We desribe them below. HPL [12] is a benchmark that is commonly used to evaluate the computational performance of HPC systems for example, top500 [1]. It measures the floating execution rate of linear equations based on the problem size. We executed the HPL benchmark with five different problem sizes of 2,000, 4,000, 6,000, 8,000 and 10,000 on the both cloud services on VMs from IaaS and on HaaS. The execution of each the problem sizes was carried twice and the average execution time calculated. The five different problem sizes enable us to obtain different wall clock execution times of HPL. We recorded the wall clock execution time for each problem size. We used the wall clock execution time to analyse the dollar cost and computational performance of the both platforms. Figure 3 show the results obtained on computational-performance.
ClustalW-MPI [14] is a parallel implementation of ClustalW [15] which is based on MPI. ClustalW is a tool that is widely used in bioinformatics for multiple alignments of nucleic acid and protein sequences. It uses three align-ments steps: pairwise alignment, guide-tree generation and progressive alignment. We ran a sample of ‘A full multiple sequence alignment’, ‘A guide tree only seqence alignment’, and ‘A multiple sequence alignment out of an existing’ on nodes from IaaS and from HaaS. We recorded the execution time of the three alignment steps to compare time to finish executions with both IaaS and HaaS. The results are shown in figure 4.
5. Results and Discussion
One of the major attractions to the Cloud-A cluster com-pute instance is that it is relatively easy to set up the clusters compared to setting up a cluster in HaaS. However, some level of technical knowledge is required to setup cluster on Cloud-A that will run HPC applications due to varying needs of HPC applications. In order to reduce the time to set up an HPC system on HaaS instances in the cloud, we uploaded our pre-configured para-virtualized image to the cloud. There are also similar VM images which can be downloaded from different sites. We estimated that this technique reduces the set up time by up to 80%. We did not compare the time to setup HPC system in Cloud-A (IaaS) cloud and in Cloud-B (HaaS) because setup time varies with individuals technical experiences.
From the computational performance result of the HPL benchmark shown in figure 3, we can see that the wall clock execution time of HPL benchmark on a provisioned instance on HaaS is shorter when compared to IaaS provided by Cloud-A. We achieved this because the memory of the virtual instances deployed on HaaS is 60GB. We chose to allocate this amount of memory to our virtual instance because we can predict the memory needed. This option is
not available for the IaaS instance (users cannot change the memory of the virtual instance chosen). We also have full control of the Hardware instance and virtual instances.
0 50 100 150 200 250 300 350 400 450 500 2,000 4,000 6,000 8,000 10,000 W al l c lo ck e xe cu 4 on 4 m e in s ec on ds
HPL problem sizes on 16 processors Cloud-‐A (VM of IaaS)
Cloud-‐B (VM of HaaS)
Fig. 3: Computational performance of High Performance Linpack on 1 node with 16 processors
As shown in Figure 3, executing the HPL on 1 node with 16 processor eliminates the bandwidth inequality on both providers. The virtual instances HaaS out performs IaaS. This is because we have full control of the applications running of our HaaS instance and we allocated higher memory to VM on HaaS. On IaaS, other VM instances may have been hosted on the hardware which may have affected the performance of the application running on our lease IaaS instance. As shown in [19], high resource allocations on infrastructure affect applications running on VMs.
The ClustalW-MPI results is shown in Figure 4. Cloud-A IaaS uses 10 Gigabit Ethernet network, whereas HaaS we leased uses a 1 gigabit Ethernet network. We could have benchmarks with the same bandwidths, however the two major providers of HaaS do not have 10 gigabit Ethernet network. The results in Figure 4 show that there is no significant impact on application running on IaaS and on virtual instances on HaaS.
6. Cost Analysis
At the time of writing, Cloud-A offers different price models to their cluster compute instance customers; The primary price model which is widely used is called ’on-demand instances’. The on-’on-demand instances price model allows users to pay hourly without contract while other price models may require up front payments and/or contracts.
Cloud-B offers their customers a pay-as-you-go price model, which is similar to on-demand instance prices offered by Cloud-A. Therefore we use on-demand price instance
0 5 10 15 20 25
A full mul+ple
sequence alignment sequence alignment A guide tree only A mul+ple sequence alignment out of an exis+ng tree W al l c lo ck e xe cu + o n + m e in s ec o n d s
Cloud-‐A (VM of IaaS) Cloud-‐B (VM of HaaS)
Fig. 4: Performance of ClustalW-MPI application on 16 processors
to compare the cost of running computation-intensive ap-plications on both cloud services. In addition to the on-demand/pay-as-you-go instances prices, there are charges, which are charged for some cloud services such as network bandwidth and IP addresses which we do not consider to avoid complexity. We used the results obtained from HPL benchmarking to analyse the cost. As previously used in a similarly cost analysis [18], we assume that 1 second is equal to hourly rate which the both cloud providers offer. This also allows us to do the analysis without paying the for hours the experiment would have cost. We used the prices of the leased services as shown in Table 1 and 2. The cost analysis computation of IaaS and HaaS is shown in figure 5.
Based on the computational performance and cost analysis it appears that it is more cost effective to lease HaaS and configure the HPC systems. Cloud service users of HaaS have full control of the hardware as well as the VMs they provisioned. Application performance and other metrics can be easily measured. From the result, it seems that the cost of running HPC applications can be reduced by 20% when HaaS is leased.
7. Related work
Cloud computing is a revolutionary computing paradigm for storing data, running applications, including computation-intensive applications. Cloud computing promises numerous benefits, which includes no up front investments for HPC applications, which is attractive, compared to traditional HPC systems. Many studies have evaluated the suitability of HPC systems in the cloud and showed that it is expected that more computation-intensive HPC applications will be run in the cloud HPC than
0 100 200 300 400 500 600 2,000 4,000 6,000 8,000 10,000 D ol lar c os t $
HPL problem sizes on 16 processors Cloud-‐A (VM of IaaS) Cloud-‐B (VM of HaaS)
Fig. 5: The cost analysis VM of IaaS and VM of HaaS
traditional HPC systems [16]. Furthermore, the Amazon Elastic Compute Cloud (Amazon EC2) cluster recently appeared in TOP500 list [1] in year 2010, which shows that there is a viable future for HPC systems in the cloud.
Many past researches evaluating of HPC applications on HPC systems in the Cloud with emphasis on Amazon EC2 have been carried out. These investigations focus on the performance of Amazon EC2 and Traditional HPC systems [16], [17], [18], [19].
Carlyle et al. [17] studied the cost effective HPC System. They show that it is cost effective for institutions like Purdue University to operate a community/traditional cluster than to lease HPC resources from Amazon EC2. This study clearly shows that Amazon on-demand cluster compute instances prices are not cost effective for HPC applications for some institutions. Their work focuses on Amazon EC2 service IaaS and traditional HPC systems.
Deelman et al. [18] in their work on ’The Cost of Doing Science on the Cloud: The Montage examples’; show that the cost of cloud services could be significantly reduced without significant impact on application performance, if the right storage and compute resources are provisioned. However, they did not consider different platforms like HaaS. We extended their work, demonstrating that HaaS can significantly reduce the cost of running computation-intensive application on HPC in the cloud.
Ekanayake and Fox [19] compare HPC applications with different needs and showed the performance of applications with latency. However, they did not compare the cost of executing computation-intensive application on different ser-vices such as IaaS and HaaS.
Yao et al. [21] showed that optimal cost-performance ratio can be achieved with th appropriate cloud instance. However, they did not consider cost and computational performance
when IaaS and HaaS are leased.
To the best of our knowledge, our work is different from other work in that we study the computational performance and dollar cost of running computation-intensive application in HPC in the cloud when IaaS and HaaS are leased. We experimentally show that the dollar cost of running computation-intensive application can be reduced as much as 20% with HaaS without significant impact to performance.
8. Conclusions and Future Work
Due to the huge capital investment required to own a traditional HPC systems which typically involves job queuing, using an HPC system in the cloud is a good alternative. Cloud computing offers IaaS and HaaS for deployment of cluster instances, which can be used to run computation-intensive applications. IaaS provides almost ready to use clusters with minimal deployment installation tasks. With HaaS, virtual machines can be provisioned to run computation-intensive application. We have conducted experimental analysis to determine the performance and cost when cloud services IaaS and HaaS are leased to run computation-intensive application. We showed that the dollar cost of running computation-intensive application in the cloud can be reduced by as much as 20% when HaaS is leased. We showed that there is no significant impact in performance of the applications when executed on the leased HaaS.
Acknowledgment
The authors would like to thank Bran Selic for providing valuable comments and suggestions.
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