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Cloud Computing: Architectural and Policy Implications
Christopher S. Yoo*
Cloud computing has emerged as perhaps the hottest recent development in information technology. Some observers believe that that cloud computing represents a breakthrough development that has the potential to fundamentally transform the nature of computing. Others are more skeptical, arguing that it is nothing more than overhyped repackaging of already extant technologies. Notwithstanding the divergence of opinions regarding its future prospects, a new cadre of companies has emerged specializing in cloud computing solutions, including GoGrid, Iland, Rackspace, Saavis, and Sungard. Established computer industry players, such as Amazon, Google, Hewlett Packard, IBM, Microsoft, and Sun, have entered the fray, as have traditional telecommunications companies, such as AT&T, Comcast, NTT, and Verizon. Cloud computing’s growing salience is forcing every industry participant and enterprise customer to grips with this emerging phenomenon.
Despite all of the attention garnered by cloud computing, many know little about its basic principles or economic foundations. The scant analyses that already exist focus almost exclusively on the issues surrounding data privacy. Most importantly, an analysis of the architectural and policy implications has yet to appear in the literature.
This Article would like to take the first exploratory step in that direction. It begins by providing an overview of the key concepts and basic economics underlying cloud computing. It then assesses cloud computing’s architectural implications for both access and core networking functions. It closes with by offering some observations about cloud computing’s impact on market structure and network regulation. As is inevitably the case with any new technology, the novelty and dynamic nature of the subject matter means that any initial take is inevitably incomplete and vulnerable to immediate obsolescence. That said, I hope that my analysis of cloud computing’s architectural and policy implications will prove helpful in furthering academic scholarship, business decision making, and policy debates.
As is the case with many new architectures, a precise definition of cloud computing’s key characteristics has proven remarkably elusive.[1] That said, there is broad agreement that cloud computing is centered around certain core concepts. Some observers have noted that cloud computing has both an outward looking and an inward looking face (Birman et al., 2008). From the outward looking perspective of an end user looking at the cloud, it shifts functions that used to be performed by computers located at the network’s edge (such as hosing software and data) into data centers residing in the network’s core. From the inward looking perspective of how individual cloud computing elements interact with other cloud computing elements, the focus is on the ability to coordinate and integrate applications and data operating on multiple machines through mechanisms into a seamless whole.
Beginning first with the outward looking view, the dominant vision of computing today involves an application running through software and accessing data that are both stored locally. The data and applications employed by cloud applications, in contrast, reside in a data center rather than at the end user’s premises. For example, email has typically been accessed through a software client (such as Microsoft Outlook) that resides on the hard disk of a desktop or laptop computer and stores data on that same hard disk. Someone accessing email through a web-based email service (such as Google’s Gmail or Microsoft’s Hotmail) does not need to run an email client or store messages locally. Instead, both the application and the underlying data are hosted in Google’s data center. A similar comparison can be drawn between an end user running a traditional word processing application (such as Microsoft Word or Word Perfect) and another end user using a cloud based application (such as Google Docs or Microsoft Office Live).
Rather than regarding software and computing power as physical products that consumers purchase in lump sums in advance, cloud computing reconceptualizes software and computing power as services purchased incrementally on an as-needed basis. Unlike previous iterations of distributed computing (such as grid computing), which required end users to master deployment details and to perform numerous management functions in order to gain access to the shared resources, cloud computing is easily configurable on demand by end users.
Transferring applications and data into to data centers drastically simplifies the functions that must be performed by the machines owned and operated by end users. Instead, end users only need a thin client capable of providing nothing more than network connectivity and enough computing power to run a web browser or some other simple interface. The classic example is the netbook, which offers less computing power than the typical laptop or personal computer. The result is a device that is much simpler and costs less.
First developed by IBM during the 1960s to better utilize mainframe computing, virtualization allows the computing power of a single machine to be subdivided into a number of smaller virtual machines by permitting a single piece of hardware to run multiple operating systems or multiple sessions of the same operating system. This allows end users to share the same machine while giving the appearance that end user’s application was running on a separate, dedicated machine. The ability to run multiple instances on the same machine permits finer granularity in provisioning computing services and allows computing resources to be utilized more efficiently.
In addition to subdividing a single machine into multiple smaller virtual machines, modern virtualization techniques also allow cloud computing environments to shift virtual machines from one server to another. Thus, should one cloud computing application need more computing power than initially allocated to it, the software that manages virtualization (called a hypervisor) can seamlessly transfer the instance to another server that has more space. The ability to bring in additional storage and computing power as needed greatly enhances the flexibility and scalability of computing operations. Unlike previous forms of distributed computing, such as grid computing, organizing such virtual resources is done through automatic processes that give the impression that each application is running on a different piece of hardware dedicated to a single end user.
Cloud computing providers can offer services at different layers of the resource stack, simulating the functions performed by applications, operating systems, or physical hardware. Although some commentators categorize cloud computing services somewhat differently, the most common approach segregates services into a three-part taxonomy (see, for example, Foster et al., 2008; Vaquero et al., 2009; Mell & Grance, 2009).
· Software as a Service (SaaS) offers finished applications that end users can access through a thin client (typically, but not necessarily, a web browser). Prominent examples of SaaS include Gmail, Google Docs, and Salesforce.com. The end user does not exercise any control over the design of the application (aside from some minor customization and configuration options), servers, networking, and storage infrastructure.
· Platform as a Service (PaaS) offers an operating system as well as suites of programming languages and software development tools that customers can use to develop their own applications. Prominent examples include Microsoft Windows Azure and Google App Engine. PaaS gives end users control over application design, but does not give them control over the physical infrastructure.
· Infrastructure as a Service (IaaS) offers end users direct access to processing, storage, and other computing resources and allows them to configure those resources and run operating systems and software on them as they see fit. Examples of IaaS include Amazon Elastic Compute Cloud (EC2), Rackspace, and IBM Computing on Demand.
The literature distinguishes among at least three different deployment models of cloud computing. In the case of private clouds, all of these services are deployed through a privately owned data center used exclusively by the organization that builds it. These private clouds may deploy proprietary technologies inaccessible to other users of cloud services. In contrast, public clouds are provided by third parties offering their services to a wide range of interested customers. As such, they come the closest to the vision of utility computing that some have advanced since the 1960s. The key difference is that public clouds are more competitive, do not bear a duty to serve, and typically offer a wider range of quality of service and pricing than traditional public utilities.
Rather than commit to one strategy or the other, many enterprise customers employ what have become known as hybrid clouds, which focus primarily on proprietary data centers, but rely on public cloud resources to provide the computing and storage needed to protect against unexpected or infrequent increases in demand for computing resources. One classic case is disaster recovery, in which customers make arrangements to mirror their data in a cloud-based data center and to access the data and computing power to run the applications should the enterprise’s internal network or servers fail. The scalability of cloud computing also makes it well suited to provide overflow capacity to provide insurance against unanticipated spikes in demand (sometimes called cloud bursting). Even if such surges are anticipated, cloud computing may nonetheless be a logical strategy if the increase in traffic is sufficiently short lived to render impractical provisioning the necessary resources in house, such as occurs during the increasingly popular post-Thanksgiving online shopping event known as Cyber Monday (see, Weinman, 2009, for an interesting analysis of the relevant tradeoffs).
The basic cloud computing concepts described above have important implications for the economics of cloud computing and for understanding the potential benefits associated with this new approach to computing.[2] A closer analysis reveals that some of the considerations that are often cited as supporting cloud computing (such as the scale economies resulting from the amortization of fixed costs and converting capital expenditures into operating expenditures) are more ambiguous as they initially seem. Instead, the primary advantages are the result from the benefits of aggregating demand.
Perhaps the most frequently cited benefit of cloud computing is its ability to reduce cost. End users who shift applications that used to reside on their desktops into the cloud face considerably lower maintenance costs, as they no longer need to bear the expense of ensuring that the applications run properly. Running software in the cloud also relieves end users of the responsibility for making sure that the software is the most recent version and ensures that all such updates are deployed quickly and easily.[3]
On the data center side, people often argue that cloud computing facilitates the realization of scale economies by allowing greater amortization of fixed costs. Sharing machines with other users can allow small and medium sized firms whose operations are too small by themselves to support a single data center to combine with others to achieve the minimum efficient scale needed to run data centers in a cost effective manner. Cloud computing also allows the expertise needed to run data centers and to update applications to be amortized over a larger number of customers. One must be careful not to overstate the extent to which cloud computing permits firms to realize scale economies. Larger enterprises whose computing needs are already large enough to support a single data center are typically able to realize these efficiencies even in the absence of cloud computing.
Those comparing the cost of cloud computing to the cost of creating a proprietary data center must bear in mind one key difference between the cost structures. The costs associated with creating a proprietary data center must be incurred up front and must be sunk regardless of whether the capacity is actually used. As a result, when demand is uncertain, a simple comparison of projected unit cost of a proprietary data center with the incremental cost of cloud computing services can be misleading should the projected levels of demand fail to materialize. Indeed, cloud computing solutions become more attractive as flows become increasingly volatile.
Another often touted benefit of cloud computing is its ability to turn capital expenses (CapEx) into operating expenses (OpEx). Although this argument bears considerable intuitive appeal, whether it makes sense in any particular case depends on each firm’s individual characteristics (Weinman 2008). For example, avoiding CapEx improves companies’ cash flow positions by reducing their need to make up-front investments in assets that will not generate compensating revenues until later years. Although reductions in the need for cash are generally beneficial, they are less so for companies whose ongoing operations already generate enough free cash flow to cover these capital investments.
Conversely, capitalizing expenses can make an income statement look more attractive by allowing the expenses paid during the current year to be transferred into future years (depending on how quickly the applicable depreciation schedule calls for the invested capital to be expensed). Moreover, whether a particular investment will meet a company’s internal hurdle for return on invested capital depends in no small part on that company’s current cost of capital. The higher the cost of capital, the more attractive transforming capital expenses into operating expenses becomes.
These considerations can make the benefits from turning CapEx into OpEx less compelling than may appear at first glance. Whether avoiding such investments is ultimately beneficial depends not only on the cost savings, but also on any particular company’s current financial position.
More compelling than cost reduction and transforming capital expenditures into operating expenditures are the potential benefits from aggregating demand. As a general matter, enabling multiple end users to share equipment allows higher utilization of the underlying hardware. It also reduces the amount of aggregate excess data center capacity that must be maintained, since, in the words of one discerning industry observer, “The peak of the sum is never greater than the sum of the peaks” (Weinman, 2008).
The rationale underlying this insight is straightforward. A basic principle of statistics is that the sum of the variance of two numbers is always greater than or equal to the variance of the sum of those numbers. Stated slightly more formally, (σ1+2)2 = (σ1)2 + (σ2)2 + 2ρσ1σ2, where (σ1+2)2 represents the variance of a bundle of goods 1 and 2, and (σ1)2 and (σ2)2 represent the variance of each component. Since (σ1 + σ2)2 = (σ1)2 + (σ2)2 + 2σ1σ2, this implies that σ1+2 ≤ σ1 + σ2 for all ρ ≤ 1. So long as the demands for the components are not perfectly correlated, the standard deviation of the bundle will be less than the sum of the standard deviations of the components (Schmalensee, 1984).
The reduction in the variability that results from aggregating demand helps cloud computing firms achieve higher utilization rates than individual companies can achieve on their own. The fact that firms in different industries systematically see their traffic peak at different times makes it possible for combinations of firms to increase the efficiency of hardware utilization than would be possible on their own. Moreover, the scalability of cloud computing allows end users to guard against the possibilities that demand might either be unexpectedly high or unexpectedly low.
Although the benefits from aggregating demand are theoretically inexhaustible, they are subject to the principle of diminishing marginal returns. Given the risk premium that cloud computing firms must charge, it is quite possible that large firms may find their own traffic large enough by itself to achieve the necessary reduction in variability of the peak. That said, the fact that one set of flows generated by a single firm is likely to be highly correlated with the other flows it is generating slows the speed with which individual firms to realize the reduction in variability associated with aggregating demand. In any event, firms that fall below the size necessary to reach the relevant point of diminishing marginal returns will find that cloud computing firms are in a better position to bear the risk inherent in the uncertainty of traffic flows than they are. This is particularly important because of the indivisibility and long lead-times associated with expansions of data center capacity.
The manner in which virtualization permits the same data to be hosted at multiple data centers greatly enhances the reliability of public cloud solutions. A single data center with reliability r faces a failure rate of (1 – r). If that same data is simultaneously hosted across n data centers, the failure rate drops to (1 – r)n. To use a specific example, if individual data centers have reliability of two nines (99%), storing that same data across two data centers increases reliability to four nines (99.99%). Mirroring the data in a third data center increases reliability to six nines (99.9999%) (Weinman, 2008). The benefits of increased reliability from operating multiple data centers may not fully materialize if the risk of failure is correlated across data centers or if data centers exhibit interdependencies.
Aggregating traffic with other firms also provides firms with a cost effective way to reduce latency. Consider the simplest case, in which latency is entirely a function of proximity to a data center. The agility of modern cloud computing can reduce latency by continuously migrating data to the data center located closest to where it is currently being demanded.
Sharing data center capacity with other end users also makes it easier for cloud computing customers to reduce latency by increasing the number of data centers. In a one dimensional space, one needs only double the number of data centers in order to halve the distance between them. In a two dimensional space, however, significantly larger numbers of additional data centers are required to reduce distances by one half. If the data centers are currently laid out in a square grid, halving the distances would be require doubling the number of data centers along both axes. In effect, cutting the latency in half would require quadrupling the number of data centers. Indeed, this analysis can be generalized to conclude that a k dimensional space would require 2k more data centers in order to halve the distance between them. The precise numbers change somewhat depending on the precise configuration, but the basic intuitions remain (Weinman 2008, 2009).
The nonlinear relationship between distance and the number of locations represents another source of economies of scale that further increase the benefits of cloud based solutions. This conclusion is, of course, subject to a number of caveats. In many cases, network latency is more the result of queues in the routers than the distances between them. That said, in an increasingly global world in which traffic often travels long distances, industry participants have found that locating data centers closer to end users plays a key role in reducing latencies and in improving the economics of negotiating low-cost interconnection agreements (Yoo 2010).
Cloud computing provides numerous other benefits that bear mentioning. Enabling individual end users to access data from any Internet-enable location facilitates remote and mobile access and makes it easier for end users to remain productive. It also facilitates collaboration between multiple end users. Storing data in locations other than laptops and desktop PCs protects against certain types of security and privacy threats (although as we shall see, it raises other types of concerns at the same time).
Relying on cloud services may yield management benefits as well. Aggregating demand across multiple companies may allow data centers to realize the benefits of learning by doing more quickly. Seeing data from multiple sources may allow data centers to recognize patterns in data in ways that will allow them to identify threats and improve the performance of certain applications. It also provides greater leverage over the overhead costs of keeping abreast of the latest technological developments. Lastly, by reducing the need for corporate management to devote time and energy to overseeing the company’s IT resources, cloud computing can conserve on one of the dearest resources in any organization: management focus.
For all of cloud computing’s potential advantages, it possesses several potential disadvantages as well. Whether the advantages outweigh the disadvantages depend largely on the configuration of the access and core networking functions on which cloud computing depends. The underlying architecture can be disaggregated into two separate components. The first is with respect to the access network through which end users connect to data centers. The second is with respect to the links that connect data centers to one another.
As an initial matter, cloud computing will place considerably more onerous demands on the access networks through which end users will gain access to the cloud. The access networks’ ability to meet these demands will go a long way in determining cloud computing’s attractiveness as an option.
As an initial matter, cloud computing requires a higher degree of ubiquity than traditional computing solutions. When the software and the data needed to run a particular application reside on the end user’s hard disk, the unavailability of a network connection may inconvenience the end user and reduce the application’s functionality, but it does not necessarily stop that end user from being productive. When the software and data reside in the cloud, however, the absence of a network connection has more serious consequences, effectively preventing end users form running the application at all. As a result, cloud computing customers regard ubiquitous access to network connections as critical.
The fact that cloud computing envisions that processing will now occur in data centers rather than locally necessarily requires that a great deal of information that was previously handled locally pass through the access network. As an initial matter, new cloud computing customers must have some means for uploading their data to the data centers when setting up new applications. At this point, however, the access network does not have sufficient bandwidth to support this level of utilization. Because datasets in the terabyte range would take weeks to upload, cloud computing providers currently recommend that customers download their data onto a physical storage medium and to send it via an overnight mail service (Brodkin, 2010). Even after data has been provisioned to a new cloud computing instance, the fact that processing that used to occur locally is now being performed in the data center typically means that more information must pass to and from the client being operated by the end user.
A related concern is access network reliability. The availability of an access network connection is meaningless if it is not functioning properly. Even when the application and the data reside on the end user’s hard disk, the absence of a network connection can severely limit the end user’s ability to perform productive work. Network failure becomes an even more insuperable obstacle when these elements are hosted in the cloud. Indeed, Gmail, Salesforce.com, and Amazon’s S3 (Simple Storage Service) and EC2 (Elastic Compute Cloud) have suffered from well publicized service outages that had severely adverse effects on their customers. The higher stakes mean that some customers are likely to demand that access networks offer higher levels of guaranteed uptime.
End users’ willingness to offload services that used to be provided locally within a single machine into the cloud depends in no small part on how quickly the cloud is able to perform those functions. As a result, cloud computing customers are likely to insist on service level agreements guaranteeing them certain levels of quality of service. These demands will likely vary from company to company. For example, financial services companies typically require perfect transactions with latency guarantees measured in microseconds. In addition, these companies will require the cloud provider to verify the delivery time of each and every transaction after the fact.
One way that cloud computing systems can improve the quality of service of network services is by taking advantage of the presence of multiple connections between two points. The Internet currently relies on the Border Gateway Protocol (BGP) to determine the route that any particular stream of packets may take. BGP deals with the presence of multiple paths only clumsily, instead tending to route all traffic along a single route instead of balancing traffic across both paths. BGP, moreover, is controlled by the core routers rather than end users. A new architecture for cloud computing could improve network performance by providing greater ability to allocate traffic across multiple paths. It could also increase functionality by giving end users control over the particular routes taken by their traffic.
Cloud computing necessarily requires large amounts of data that previously did not leave a company’s internal network to be transported via the access network. The fact that this data must pass outside the company’s firewall and through the access network renders it vulnerable to attack vectors that are different from those plaguing corporate campuses. Moreover, the law holds all institutions maintaining health and educational records responsible for maintaining their privacy. The fact that such records are now housed in the cloud does not obviate those responsibilities.
As a result, cloud-based solutions must be able to assure these institutions that their data is being handled in a way that preserves confidentiality by giving end users greater ability to control the manner in which their traffic passes through access networks. In addition, cloud computing may require an architecture that permits the exact routes that particular traffic takes to be auditable and verifiable after the fact.
The advent of cloud computing will change the nature of demand place on data centers. In addition to heightening data centers’ seemingly unquenchable thirst for electric power, cloud computing is placing ever increasing demands on the links that connect data centers to one another.
The need to augment computing resources on demand requires the ability to move large amounts of data between data centers very quickly. These demands are heightened still further by the needs of virtualization, which depends on the ability to knit together instances operating on several different machines into a current whole. As a result, cloud computing will require that all data centers be linked by high capacity connections. In addition, these connections are likely to employ technologies that are able to guarantee a higher level of quality of service than that enabled by the Internet’s current best efforts architecture.
Just as cloud computing requires greater reliability from the access network, hosting the software and the data needed to run application in the cloud also requires greater reliability in the data centers. As a result, data centers operations will need a high level of redundancy, both in terms of computing power and in terms of the interconnections between servers. In addition, because cloud computing often requires that a particular application be run in parallel processes operating on multiple servers, the system must be “self healing” in that it be able to tolerate and recover from the failure of one of those servers.
As noted earlier, cloud computing necessarily requires large amounts of data that used to reside on an end user’s hard disk or behind a corporate customer’s firewall to reside instead on a server in a data center. Moreover, virtualization envisions that this data will often be located on the same servers as other companies’ data. As a result, the hardware located in these data centers and the networks interconnecting them require guarantees that other companies will not gain access to their data.
In addition, customers are likely to require assurance that failure of one virtual machine operating on a server will not compromise other processes operating on the same server. Industry participants are also often very protective of information about the volume and pattern of their transactions. They are thus likely to impose stringent requirements on what data can be collected about their operations and how that data is used.
Most cloud computing providers need greater control over the paths taken by traffic passing between data centers. As a result, many rely on MPLS or some other protocol to exercise control over the precise paths taken by particular traffic. Such control mechanisms are essential to ensuring that flows between data centers maintain the required levels of quality of service, protect network security, and maintain the privacy of end users’ data.
The fact that data may be shifted from one data center to another also potentially makes that data subject to another jurisdiction’s privacy laws. Because customers are ultimately responsible for any such violations, they are likely to insist on a significant degree of control over where their data reside at any particular moment.
The approach discussed so far implicitly presumes that all of the virtualization of particular cloud computing application will be performed by a single provider. There is no reason that this has to be the case as a theoretical matter. It is quite possible that multiple cloud computing companies could work together to provide a single cloud computing application. For providers to be able to interoperate with one another, the industry would have to develop standards under which different cloud computing providers can exchange traffic and jointly interact with data as well as protocols for joint coordination and control.
In addition to allowing multiple providers to provision a single application, the emergence of standards could also permit the integration different applications provided by different providers. Instead of relying entirely on a single application, cloud computing could integrate multiple applications provided by multiple sources and integrate them on a dynamic basis. Such cross-application coordination would depend on the availability of standards to govern the interactions among these applications.
One of the primary advantages of cloud computing is that it permits the provisioning of computing resources on demand on a pay-as-you-go basis. As a result, cloud computing requires some means for metering resource usage. In so doing, cloud computing inherently supports commercial deployment to an extent that grid computing was never able to achieve. Move over, the fact that different customers require different levels of quality of service means that such prices are likely to vary widely. Any cloud computing system must provide the basis for setting prices, monitoring and accounting for activity levels, and obtaining the appropriate amount of monetary compensation.
The future success of cloud computing thus depends on the development of new architectural features. These architectural features also have policy implications, both in terms of the likely industry structure as well as its interaction with regulation.
The likely evolutionary path for cloud computing has important implications for industry structure. As an initial matter, the high fixed costs and the need to access the rights of way associated with last-mile networking makes it unlikely that cloud computing providers will construct their own access networks. Instead, they are likely to rely on incumbent broadband providers to provide the necessary end user connectivity. These same structural features suggest that the market for access networking is unlikely to support no more than a handful of players. Even if the government devotes more spectrum to wireless broadband, the market will likely remain quite concentrated
The economic factors discussed above may also limit the competitiveness of cloud computing providers operating the underlying data centers. As noted earlier, data center operations may require certain levels of scale if they are to bear risk efficiently, aggregate the information necessary to optimize applications, and support the optimal number of data centers to support reliability and to minimize latency. In addition, data centers may incorporate proprietary technologies that may further concentrate the market.
Given the natural oligopolistic tendencies of the cloud computing industry, ways to reduce entry barriers and switching costs are well worth thinking about. One of the greatest concerns raised by potential cloud computing customers is the lack of portability of data and applications between cloud computing systems. The emergence of a standard set of cloud computing application programming interfaces (APIs) would help ameliorate many of these concerns.
The development of the cloud computing industry is also being hamstrung by the fact that cloud computing providers face considerable regulatory risk. Regulators have traditionally classified services that combine computing power and storage with transmission as information services and have exempted them from most forms of regulation. Only if transmission is offered to the public as a standalone offering are technologies subject to the more intrusive regulatory regime applied to telecommunications services. During the summer of 2010, the Federal Communications Commission proposed deviating from this understanding and invited comment on whether broadband access services are telecommunications services even though they are not offered on a standalone basis. If this proposal is adopted, both private and public cloud computing providers may well be subject to stringent regulatory requirements. Indeed, there are some reports that concerns about being subject to telephone-style regulation has led some cloud computing providers to forego providing their own data center interconnections and instead to outsource those services.
In addition, cloud computing presumes that providers will provide guaranteed levels of quality of service and offer different levels of reliability and that people will pay for these services. To the extent that customers’ needs vary, one would expect some customers to pay a premium for higher levels of reliability and network service. Such pricing differentials threaten to run afoul of network neutrality regulation that has been proposed.
On a broader note, the analogy between cloud computing and public utilities should not be taken too far. History has shown that public utility regulation is best suited when the product being regulated is relatively uniform, the primary network elements have already been built out and thus do not require additional investment, and technologies and market shares are stable. As such, it is poorly suited to environments like cloud computing, in which the product varies widely, investment incentives play a critical role, and in which the underlying technology and market positions are in a state of dynamic flux.
Cloud computing thus holds considerable promise as a transformative technology that can change the very nature of computing. Assessing its relative merits requires a clear understanding of its key concepts and its underlying economics. What is perhaps most interesting is that the primary economic benefits of cloud computing do not come from amortizing fixed costs over large volumes. Instead, the primary benefits result from the scale economies resulting from the aggregation of demand.
Cloud computing also requires networks that are more ubiquitous, reliable, efficient, and secure. It also demands operational support for controlling and verifying the routes that particular traffic takes as well as for setting and charging prices. Satisfying these new demands will require major changes to the architecture, both in terms of access networking and in terms of data center connectivity. It also implicates important questions of public policy.
Only time will tell if cloud computing turns
out to be the transformative technology that many predict. In the meantime,
both the engineering and the policymaking community should take steps to ensure
that industry participants have the tools and the latitude to explore cloud
computing’s full potential.
References
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* Professor of Law, Communication, and Computer & Information Science and Founding Director of the Center for Technology, Innovation and Competition, University of Pennsylvania. I would like to thank Bill Boebel, Todd Broebsting, Daniel Burton, Robert Krauss; Bill Lehr; Don Norbeck; Jonathan Smith; and particularly Joe Weinman for their help in introducing me to many of the nuances of cloud computing. This work is partially supported by an event on “Antitrust and the Dynamics of High Technology Industries” sponsored by the Technology Policy Institute on October 22, 2010, as well as National Science Foundation Grant CNS-10-40672..
[1] For surveys of proposed definitions of cloud computing, see Geelan (2009); Vaquero et al. (2009); Weinhardt et al. (2009). For other examples, see Buyya et al. (2009); Foster et al. (2009).
[2] My views about the economics of cloud computing are heavily influenced by Weinman (2008) as well as subsequent conversations with him.
[3] Note that end users running PaaS or IaaS instead of SaaS will continue to have to bear these costs.