2. Cloud Computing: An Old Idea Whose Time Has (Finally) Come
What is Cloud Computing, and how is it different from previous paradigm shifts such as Software as a Service(SaaS)?
Why is Cloud Computing poised to take off now, whereas previous attempts have foundered?
What does it take to become a Cloud Computing provider, and why would a company consider becoming one?
What new opportunities are either enabled by or potential drivers of Cloud Computing?
How might we classify current Cloud Computing offerings across a spectrum, and how do the technical and business challenges differ depending on where in the spectrum a particular offering lies?
What, if any, are the new economic models enabled by Cloud Computing, and can a service operator decide whether to move to the cloud or stay in a private datacenter?
What are the top 10 obstacles to the success of Cloud Computing---and the corresponding top 10 opportunities available for overcoming the obstacles?
What changes should be made to the design of future applications software, infrastructure software, and hardware to match the needs and opportunities of Cloud Computing?
3. What is Cloud Computing?
Cloud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the datacenters that provide those services. The services themselves have long been referred to as Software as a Service (SaaS). The datacenter hardware and software is what we will call a Cloud.
Factors might influence these companies to become Cloud Computing providers:
1. Make a lot of money.
2. Leverage existing investment.
3. Defend a franchise.
4. Attack an incumbent.
5. Leverage customer relationship.
6. Becomes a platform.
4. Cloud in a Perfect Storm: Why now, Not Then?
4.1 New Technology Trends and Business Models.
4.2 New Application Opportunities
Mobile interactive applications.
Parallel batch processing.
The rise of analytics.
Extension of compute-intensive desktop applications
"Earthbound" applications
5. Classes of Utility Computing
Azure is intermediate between complete application frameworks like Google AppEngine on the one hand, and hardware virtual machines like EC2 on the other.
Different tasks will result in demand for different classes of utility computing.
6. Cloud Computing Economics
6.1 Elasticity: Shifting the Risk
"pay as you go"
usage-based pricing
Few users deliberately provision for less than the expected peak.
6.2 Comparing Costs: Should I Move to the Cloud?
Pay separately per resource.
Power, cooling and physical plant costs.
Operation costs.
Tasks are shifted to the cloud provider.
Costs will be lower for managed environment than for hardware-level utility computing.
7. Top 10 obstacles and Opportunities for Cloud Computing
Each obstacle is paired with an opportunity.
#1 Obstacle: Availability of a Service
Simple Storage Service (S3)
The only plausible solution to very high availability is multiple Cloud Computing providers. (The best chance for independent software stacks is for them to be provided by different companies.)
Another availability obstacle is Distributed Denial of Service (DDoS) attacks.
The longer an attack lasts the easier it is to uncover and defend against.
#2 Obstacle: Data Lock-In
Concern about the difficult of extracting data from the cloud is preventing some organizations from adopting Cloud Computing.
1. The quality of a service matters as well as the price, so customers will not necessarily jump to the lowest cost service.
2. In addition to mitigating data lock-in concerns, standardization of APIs enables a new usage model in which the same software infrastructure can be used in a Private Cloud and in a Public Cloud. Such an option could enable "Surge Computing" in which the public Cloud is used to capture the extra tasks that cannot be easily run in the datacenter (or private cloud) due to temporarily heavy workloads.
#3 Obstacle: Data Confidentiality and Auditability
#4 Obstacle: Data Transfer Bottlenecks
Applications continue to become more data-intensive.
One opportunity to overcome the high cost of Internet transfer is to ship disks.
A second opportunity is to find other reasons to make it attractive to keep data in the cloud.
A third, more radical opportunity is to try to reduce the cost of WAN bandwidth more quickly.
#5 Obstacle: Performance Unpredictability
I/O sharing is more problematic.
One opportunity is to improve architecture and operating systems to efficiently virtualize interrupts and I/O channels.
Another possibility is that flash memory will decrease I/O interference.
Another unpredictability obstacle concerns the scheduling of virtual machines for some classes of batch processing programs, especially for high performance computing.
Many HPC applications need to ensure that all the threads of a program are running simultaneously.
Thus, the opportunity of overcome this obstacle is to offer something like "gang scheduling" for Cloud Computing.
#6 Obstacle: Scalable Storage
The opportunity, which is still an open research problem, is to create a storage system would not only meet these needs but combine them with the cloud advantage of scaling arbitrarily up and down on-demand, as well as meeting programmer expectations in regard to resource management for scalability, data durability, and high availability.
#7 Obstacle: Bugs in Large-Scale Distributed Systems
A common occurrence is that these bugs cannot be reproduced in smaller configurations, so the debugging must at scale in the production datacenters.
One opportunity may be the reliance on virtual machine in Cloud Computing.
Since VM are de rigueur in Utility Computing, that level of virtualization may make it possible to capture valuable information in ways that are implausible without VMs. (de rigueur: prescribed or required by fashion, etiquette or customer)
#8 Obstacle: Scaling Quickly
Google AppEngine automatically scales in response to load increases and decreases, and users are charged by the cycles used. AWS charges by the hour for the number of instance you occupy, even if your machine is idle.
One RAD Lab focus is the pervasive and aggressive use of statistical machine learning as a diagnostic and predictive tool that would allow dynamic scaling, automatic reaction to performance and correctness problems, and generally automatic management of many aspects of these systems.
Another reason for scaling is to conserve resources as well as money.
#9 Obstacle: Reputation Fate Sharing
#10 Obstacle: Software Licensing
The primary opportunity is either for open source to remain popular or simply for commercial software companies to change their licensing structure to better fit Cloud Computing.
8. Conclusion and Questions about the Clouds of Tomorrow
Application Software
The client piece needs to be useful when disconnected from the Cloud, which is not the case for many Web 2.0 applications today. (Evernote?)
Infrastructure Software
Hardware System
Change In Technology and Prices Over Time
Will technology or business innovations accelerate network bandwidth pricing, which is currently the most slowly-improving technology?
Virtualization Level:
Dominated by low-level hardware virtual machines like Amazon EC2, intermediate language offerings like Microsoft Azure or high-level frameworks like Google AppEngine?
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