Are Free Cloud Services Productive? A Performance Study on End User Computing
Indika PERERA
Department of Computer Science and Engineering,
University of Moratuwa, Sri Lanka
Abstract-With the introduction of Cloud based services,
today’s computing has gained a new paradigm shift towards global, massive scale computing platforms, available for both macro and micro computing requirements. Although, there have been quite a few large scale Grid computing facilities for commercial and research computing purposes, Cloud computing services have extended their services to the individual end users with limited computing requirements. This paper is focused on how Cloud services impact on individual end users’ computing needs in the perspective of system performance productivity. The paper covers the Cloud computing conceptual models with present services available. Importantly, the experiment is based on free Cloud services dedicated for individual end users with limited computing requirements, to evaluate the computing productivity. The research results clearly indicate productivity bottlenecks on average computing users’ usage experiences, with Cloud services; which open an appealing dialogue for researchers to be indecisive on what they believe on Cloud service benefits, and the way it should be used.
Keywords: Cloud Computing, System Performance, Cloud Productivity, End User Experience, Web OS
I. INTRODUCTION
Computing performance and service productivity have been major concerns throughout the history of modern computing. Users are now experiencing more sophisticated and efficient handheld or wearable computing devices with unbelievable processing and storage capabilities, which have been evolved from early Mainframes, Personal Computers, and Servers. Despite the modern computing hardware efficiencies, there is one limitation still could not overcome with existing computing models; the computing and storage localization of the isolated hardware, unless forced to work otherwise [1]. For an example, an end user who wishes to perform a word processing or spreadsheet task, has to work with his or her computing device (Laptop, PC, or Desktop) throughout. In fact, there have been services to data backup or server hosting in remote computing hardware, but they were more or less in passive nature when compared with the present Cloud service concept. In general Cloud Computing can be considered as an extension of Grid infrastructure through web services. Whether it’s called Cloud computing or on-demand computing, software as a service, or the Internet as a platform, the common element is a shift in the geography of
computation [2]. The average and non expert computing user community prefers to have, usable interfacing for their work; GUI based operating systems and applications are becoming more popular over console modes. As a result an emulated virtual desktop environment has become a mandatory requirement for such free Cloud service to become popular among its target audience. However, these virtual desktop computing environments are entirely based upon web browser compatible computations, making a substantial impact to end user’s computer resources, when the service is being accessed. This research was done to analyze this performance overhead and to scale the significance respect to user’s resources.
The layout of this paper is as follows. Section II includes the background literature along with the section III specific to Cloud services. Section IV describes the experiments, results with analytical discussion. Thereafter, the conclusion and reference will complete the paper.
II. BACKGROUND
High Performance Computing (HPC) and Grid based computing were the initial approaches to have advanced, distributed computing powers for specific requirements. Later researches were focused on Grid technologies whilst more recently the trend has been changed towards the new concept, Cloud computing.
A. Cloud Computing Definition
Developing a precise definition for Cloud computing is still a concern for the researchers, due to the reasons such as being a growing area of interest, vagueness of conceptual boundaries, and lack of strategic view on functionality and potential. Hwang indicated the variety of technologies in the Cloud makes the overall picture confusing [3]. Furthermore, many are exaggerating due to their over enthusiasm on this new technology, creating a fuzzy and vague idea [4]. Some have argued that defining Cloud services is not important but implementing a complete suite of Cloud technology impacts more for the users. However, the misunderstanding on Cloud computing has destroyed the unbiased view and potential benefits, to some extent.
McFedries has defined the Cloud computing based on the Massive Data Scalability [5] concept, as the collection of
Data center based huge clusters with enormous computing power and storage through spare resources [6]. More recently, many researchers [7, 8, 9] have proposed different definitions for the Cloud computing. Creeger has come up with a definition for Cloud services, summarizing some of the industry experts’ views [10]. A similar effort has been made by Geelan with the support of 21 industry experts to develop a comprehensive definition [11].
Vaquero and others have incorporated rich conceptualization with precise definitions including key concepts of Virtualization, Scalability and the on demand utility pay models for their definition.
“Clouds are a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). These resources can be dynamically reconfigured to adjust to a variable load (scale), allowing also for an optimum resource utilization. This pool of resources is typically exploited by a pay-per-use model in which guarantees are offered by the Infrastructure Provider by means of customized Service Level Agreements” [12].
This comprehensive definition on whole spectrum of Cloud Computing has provided a better basis for this research as well.
B. Cloud Services over Grid Computing
Many tend to believe that Cloud services are just another facet of Grid computing. One of the major reasons for this misconception is that Cloud and Grid computing share many common characteristics. However, with a close look on both concepts, it is easier to understand on similarities and differences between the two. Researchers, who studied this aspect, have come up with a set of features on both computing paradigms to clearly define the service differences and similarities.
The development and deployment of Grid-based applications face the challenges of working over large sets of heterogeneous hardware and software located on and owned by different institutions with different usage policies [13]. The interfaces present in the Grid infrastructure are too low level; they expose too many details that increase the complexity and time of development [14]. These are mainly limitations of Grid services, which have been successfully addressed by the Cloud concept. More precisely, the main concern of Grid computing is how to manage different resources and present them to the users and not the collaboration between them [15]. This indicates the relative rigidity of Grid services compared with new Cloud services. However, technically, Cloud services can provide different levels of abstraction on top of Grid services, where users can customize the abstraction as their computing need [16]. In fact, Clouds and Grids can be implemented together in a properly arranged system environment if required.
The Grid architecture has significant limitations with Quality of Service (QoS) [17]. It is experienced that QoS of a
Grid service is a more or less probable and dependant scenario with other factors. This is one of the significant limitations, which has been addressed by the Cloud services taking it as one of the most important attributes. Grid services have addressed the specific segment of computing needs such as scientific computing and High Performance Computing, but in contrast, Clouds try to reach all segments of the computing market from Fortune 500 to average users [1]. Palankar et. Al have indicated that Grid computing users can benefit from combining Cloud and Grid infrastructure in a way by performing costly data operations on the Grid resources while utilizing the data availability provided by the Cloud services [18]. All these facts show that Clouds and Grids complement each other; none of the services are significantly better at present situation. The best possible approach one could take is to develop Cloud services on top of the low level Grid infrastructure. However, it is an interesting question that whether an average computing service user would ever need such advanced solution framework. Nevertheless, if required Cloud services can extend more services with the support of existing Grid technology.
III.CLOUD MODELS AND SERVICES
Service consumers can enjoy the Cloud computing services in three major types according to the present service scenarios; i.e. Infrastructure as a Service (IaaS) [19], Platform as a Service (PaaS) [20], and Software as a Service (SaaS) [21]. In IaaS, service providers manage the infrastructure, and they allocate resources through virtualization for computation and storage according to the dynamic user demands. IaaS is the main form of Cloud computing. PaaS behaves as an additional layer to the virtualization of hardware infrastructure and allows users to consider their service as a transparent platform for applications to be run on [1]. Google’s App Engine is an example for PaaS. The major rationale with SaaS is that there are utility applications for the Cloud users to run through the basic Cloud Services such as personal managing software, Spreadsheets, Documentation tools. These are not essential to use Cloud services; however, these are more important to individual users with average computing requirements for their daily works.
A. Present Cloud Services
Cloud computing services are still at developing stage; only few industrial giants have been able to provide successful services yet. Amazon Elastic Computing Cloud (EC2) [22] and Simple Storage Services (S3) [23] have become more popular and hold competitive market share at present context. Amazon Web Service (AWS) is indeed the only large scale Cloud service which provides both computation and storage service. There are number of successful use cases with AWS being revealed, recently. Nevertheless, Google’s App Engine [24] which focus mainly on the web based services, Microsoft’s Live Mesh [25], which mainly provides storage
services, and Sun Microsystems’s Sun Grid [26] with major focus on computation, are other global solutions available for commercial Cloud service requirements.
The major problem with these main Cloud service providers is that they do not provide a reasonable service to individual average computing end users to a reasonable price or simply at zero costs. One may think that why should a service to be free, especially if it requires expensive storage and/or processing resources. On the other hand, many of the present available services in different domains, such as e-mail, social networking, Multi User Virtual Environments, etc., have a free option for non commercial, low resource consuming user requirements, whilst premium options for commercial or highly resource required scenarios. Even though, it is not considered at this moment, as the industry grows and becomes competitive, it is more certain that there will be free a Cloud service option for everybody.
As mentioned above, the main question this research tries to investigate is the productivity norms of free Cloud services with respect to end user’s computing resources. To investigate that, the first issue which affected the research for a while, was to find such free, meaningful Cloud service available for general public. During the literature survey and initial studies, three free services were identified, and examined for the suitability for this research. These services are Google Apps, I-Cloud service [27] and Global Hosted Operating System (G.ho.st) [28]. Each of these services shared common system characteristics; hence the following abstract system architecture was developed for this research consideration.
B. System View – End User Cloud Computing
As mentioned above, Cloud services can act as a platform for computation, storage services, or utility software solutions for specific requirements. The basic idea to have these services is as a remote service through the internet. Simplest possible way to achieve affective serviceability is using web services to deploy Cloud service through the internet. In the premium service category, Cloud services allow remote computation for its users making their computing happening inside another location. Same principle applies to storage requirements as well. Hence, technically, a Cloud service consumer should not require advanced computing resources at client end, where only few services calls are to be used; mainly through the web browser or similar service. Even a much resource constrained mobile computing device or handheld device can effectively be the service interface to provide necessary requirements to perform required computations within the Cloud. However, yet free Cloud services do not provide many service facilities when compared with premium counterparts. Whether, free or paid services, all Cloud service providers follow the strategy that clients are allowed to consume services through a generic service interface; the web browser. Should the users have to use a specific application interface, instead of generic web browser, will determine the success of that particular Cloud
service. It is well standardized that Hyper Text Transfer Protocol (HTTP) based web interfacing should be facilitated with any basic computing device, which has the connectivity to internet. Therefore, HTTP based service consumption for Cloud clients is a simple yet comprehensive solution, without making clients to worry on their heterogeneous platforms. To make it more effective Cloud based operating systems (Web OS) use Extensible Markup Language (XML) and Asynchronous JavaScript and XML (AJAX) based web services to implant their services.
Having said so, following abstract architecture on free Cloud services (Fig. 1) is a better representation for the analysis of this research. As the system architecture explains, basic free Cloud service can be consisted of application services, storage facilities, simple infrastructure facilities or a combination of these. Out of the three services identified for this research, Google Apps is more or less a web portal based simple application management environment including basic services like (Google calendar, Google mail, Google documents, Google talk, etc.). These are basically, appeared as standard web components in a web solution; making them lightweight enough to access with low resource consumption. Furthermore, there are only few effective service offers from storage and computing aspects, other than simple service types, for basic free users. On the other hand, users do not feel that they are accessing a remote computing service, but a simple web solution through their computer. In fact, there is a growing concern for having a desktop like interactive environment for Cloud services to make them more appealing to its users, for a better perception on what facilities they have been using as a service. For this emulation of virtual desktop, Cloud service providers have been using, Flash technology with vector graphics, AJAX, and XML; hence they could simply provide an emulated browser based virtual desktop.
The other two services which have been selected for further analysis, are offering a personalized virtual desktop environment as described above, for registered users, freely. This makes the appearance of the Cloud service, more user friendly and convincing, since users can visualize their remote computing desktop through the web browser. Furthermore, these services offer varying storage capacities of few Gigabytes and similar applications services that one could experience with their standard operating system and application installations in a personal computer or laptop. Nevertheless, not like Google Apps type of simple and less attractive service, these two services require more computing resources even at the client end to deliver the service.
The following experiment and analysis were performed to evaluate on this resource consumption impact to the end users.
IV. EXPERIMENT DATA AND ANALYSIS
For the experiment, 10 computers were used mainly in two different configuration settings. Specifically, a low end, and average computing resourceful systems were selected, with the assumption that a general average computing user’s system is not composed with high end resources. Furthermore, if such a user has high end resources, there are not many requirements to use free Cloud services, since he has a better computing environment than what those services offer. The Table I shows the notations that have been used for this discussion.
The first group – G1 system configuration: Genuine Intel®
CPU T1350 – 1.86 GHz processor, 512 MB memory, 80 GB hard disc, Windows XP home operating system, and connected to campus broadband through 100 Mbps Local Area Network interface.
The second group – G2 system configuration: Genuine Intel®
Pentium Dual Core Mobile T3200 – 2.0 GHz processor, 1024 MB Memory, 160 GB hard disk, Windows Vista Business operating system, and connected to campus broadband through 100 Mbps Local Area Network interface.
A. Experiment Results
An initial study was done to examine the web browser impact to this experiment; hence, Microsoft® Internet Explorer (version 7) and Mozilla Firefox (version 3.5.3) were considered on pre experimental basis. Results did not convince for a significant difference between the two products. As a result, the idea of an experiment between web browsers for resource consumptions was abandoned and made the assumption that different client end browsers do not significantly differ on resource requirements to use these Cloud services. Furthermore, extensive XML virtual machine based web desktop services have known incompatibility issues with other browsers; hence, it was decided to consider only these two browsers for the experiment.
TABLE I
NOTATIONS USED FOR THE DISCUSSION AND ANALYSIS
Notation Description
Ci ith computer of the experiment G1 Group 1 of computers {C1,C2,C3,C4,C5} G2 Group 2 of computers {C6,C7,C8,C9,C10}
IC I-Cloud service
Go G.ho.st Cloud service
µCPU (T) Mean Total CPU usage
µCPU (A) Mean CPU usage of the application process µMem (T) Mean Total Memory used
µMem (A) Mean Memory use of the application process Xi (J)
Population Mean values for res respective groups {G1, G1}; where, i= {CPU,Mem} and J={A,T}
The first experiment activity was to perform basic file handling operations (creating and modifying) on documents and spreadsheet applications on the services. The same operations were done for 10 times on each machine and mean resource consumption are mentioned in the Table II and Table III, for the groups G1 and G2, respectively. The set of
activities were consisted with uploading data files, creating document and spreadsheet files, editing data content in created files, running application widgets, and consuming selected application services (a game play, web browser activity, and scheduler/calendar management). These activities are selected to represent generic tasks that an average user would follow with the available Cloud service options. The computer specific mean values {µCPU (T), µCPU (A), µMem (T), and µMem (A)} were considered as integers after rounding off, since the decimal fractions do not significantly affect to the population mean, in the scale that measures were taken. However, the populations’ mean values were considered with required precision, for accurate analysis.
Population mean values were derived using standard mean models, irrespective of service types (i.e. both IC and Go). For the G1 data set XCPU-G1 (T) = 60.1%, XCPU-G1 (A) = 44.1%,
XMem-G1 (T) = 708.0MB, XMem-G1 (A) = 240.8MB. Similarly,
for the G2 data set, XCPU-G2 (T) = 55.9%, XCPU-G2 (A) = 40.0%,
XMem-G2 (T) = 779.6MB, XMem-G2 (A) = 225.1MB.
When individual service performances were considered for both groups, there was not any significant difference either in CPU or Memory usage. For G1, the CPU usage difference for
applications was 1% where as for G2, it was 0.8%. Similarly,
for the mean difference Memory usage by the applications was for G1, 4.0MB and for G2, 2.6MB.
TABLE II
GROUP 1 MEAN VALUES FOR RESPECTIVE MEASURES
C1 C2 C3 C4 C5 IC Go IC Go IC Go IC Go IC Go µCPU (T) % 52 49 67 65 62 55 62 67 59 63 µMem (T) MB 683 688 712 701 729 698 706 730 717 726 µCPU (A) % 34 38 51 49 50 45 38 48 45 43 µMem (A) MB 234 221 246 237 251 249 239 246 244 241 TABLE III
GROUP 2 MEAN VALUES FOR RESPECTIVE MEASURES
C6 C7 C8 C9 C10 IC Go IC Go IC Go IC Go IC Go µCPU(T) % 47 53 55 59 61 57 58 60 57 52 µMem (T) MB 759 756 790 785 767 758 781 793 809 798 µCPU(A) % 37 39 42 45 48 41 40 41 35 32 µMem (A) MB 213 205 227 231 219 215 232 229 241 239
Hence, it can be concluded that there is no significant performance difference between services. Furthermore, it confirms the fact that overheads are due to the client end browser emulations, irrespective of server side performances. Hence the above derived population means can be used for further analysis in general; as a percentage these application services use 72.41% of total CPU time used, and 31.3% of Memory of Total Memory used. Combined group means on total CPU and Memory usages are meaningless due to different hardware and OS versions, hence not considered for individual analysis.
B. Result Analysis and Discussion
The results clearly show a significant performance overhead at the client end, from both services. In fact, it is not due to the server performance but the emulation of virtual desktop within the browser application. The population means of CPU usage and Memory usage for browsers show significant similarity for both services, thus it confirms the fact that overheads are due to the emulation of graphics within the browsers, not due to the server side computations. In fact, the server side computation is much generic and common to any service, which offers web desktop based applications. And any difference of computational performance or overheads at the server side may not reveal or effectively experienced by the client due to the resource latencies at the client end.
Fig. 2 shows a snapshot view of an arbitrary computer’s CPU profile. The emulating of a desktop operating system within the client’s browser has made this type of CPU usage continually over the period of Cloud service consumption. In fact, the CPU profile is more like a heavy computing activity in the client end; unfortunately, the tasks were accessing basic services offered, which should not require such processing profile at any instance if performed locally.
Despite the performance overheads at the client end, these free Cloud services offer some benefits to the client as well. More importantly, even though, the approach is heavily resource consuming, the service of a sharable data repository in a highly useable way makes non expert computing users to benefit significantly. Users can simply upload their data files, just as they work within their GUI based desktop operating system. Further it overwhelms the constraints with data transportation through physical media; it consumes the required bandwidth, alternatively, however.
0 10 20 30 40 50 60 70 80 90 100
Fig.2. A typical CPU profile (%) during the experiment
Also, simple widgets and applications to manage user’s basic computing requirements, such as word processing, spreadsheet, calendar, etc. are also helpful and meaningful services, which may provide rational to use them over their overheads.
It is interesting to discuss the meaning of some of the Cloud based services provided to the end users in these free Cloud services. Most of the free Cloud services with emulated virtual desktop targeting the individual users including the two services examined in this experiment, have tried to incorporate as many features and applications to make their offering closer to the users’ real operating system applications and features.
This approach has gone far beyond the Cloud service conceptual framework, where some of the service offers are meaningless in the context of the end user’s interface. For an example, how can one justify to use the free Cloud service’s web browser for web activities through your own browser? Anyone who wishes to access his free Cloud based web desktop, should have a web browser with internet connectivity. If that is the case, he can simply access his favorite web sites as he used to do; there is no valid reason to use a browser within a browser. In fact, this is a fundamental flaw of system implementation, which violates the basic rules of system performance enhancements. Not only the browser option, but also there are many applications (Games, e-mail clients, instant messengers, system accessories, etc.) which have no meaning to use through your browser, when there are ample options to have those directly. This also questions the commonsense of end users’ as well as service providers’.
Furthermore, the basic idea of having a Cloud based service to have a user’s desktop accessible from anywhere, through any supportive device, is to increase the availability and efficiency. Of course, almost all the users with average computing requirements would love to have GUI based service which is similar to their desktop. However, the question is when more overheads are there with service, how much powerful device is required to access the services. No one would be able to access such overhead services through their mobile phones or handheld devices, which are famous for resource constraints. Ultimately, unless addressed properly, an end user would need a more advanced desktop or laptop computer to use these web desktops, which has no productive resource meaning than a mere data repository to keep files.
V. CONCLUSION
The research outcomes strengthen the fact that Cloud based emulated free desktop services are merely a masking interface to one’s own computing resources from the perspective of system performance. In general, non expert average computing users can be easily misled, thinking that they are using Cloud resources to perform their task instead of their own. Ironically, what being used in such service consumption is simply the user’s own local computing resources.
Therefore, it is important that end users being aware on this situation, enabling them to take correct decisions on using these free Cloud services in a productive manner. At the same time, there are some growing concerns related to free Cloud service based Web OS and generally with Cloud computing other than performance. One of the more critical issues to address is the data security and privacy issues, which is another vital future research area in the context of free services. In conclusion, it is fair to state that, if these existing performance deficiencies at the client end are not addressed, users may not be able to experience a productive service for their requirements.
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Indika Perera, is a lecturer attached to the Dept. of Computer Science and Engineering at University of Moratuwa Sri Lanka. He is presently reading for his PhD, and has earned Bachelor of Science of Engineering (Hons), Master of Science (Computer Science), and Master of Business Studies degrees. His research interests mainly include, Virtual Education Methods, Software Engineering and Processes, Information Technology Management, Cloud Computing and its impact, IT and Enterprise, and Strategic IT Management. He has been appointed in some national level IT projects in various capacities including roles of project manger, consultant, and Technical Evaluation Committee member/chairman due to his expertise.
