Does Oracle run the world? I would have to say no but Raj (Larry Ellison is his idol) and the 40,000 Oracle customers that descended upon SF last week might beg to differ. What do James Carville and Mary Matalin have to do with enterprise software? Pretty much nothing, except for the fact that they delivered the opening keynote for Oracle OpenWorld. (And that’s the only and last politically-oriented thing you’ll hear from me as we run up to the election). For a surprisingly funny and extensive photo gallery of the eye-popping event, check out cdye1’s photostream on Flickr.

But UB40, Elvis Costello and Seal aside, Oracle OpenWorld did offer training, certifications, and always entertaining speeches by Ellison. Ben Worthen’s favorite – “Larry Ellison’s Brilliant Anti-Cloud Computing Rant” delivered to analysts on Thursday. From Ben’s slightly-edited excerpt:

“The interesting thing about cloud computing is that we’ve redefined cloud computing to include everything that we already do. I can’t think of anything that isn’t cloud computing with all of these announcements. The computer industry is the only industry that is more fashion-driven than women’s fashion. Maybe I’m an idiot, but I have no idea what anyone is talking about. What is it? It’s complete gibberish. It’s insane. When is this idiocy going to stop?

“We’ll make cloud computing announcements. I’m not going to fight this thing. But I don’t understand what we would do differently in the light of cloud computing other than change the wording of some of our ads. That’s my view.”

So did everyone catch that? Cloud computing is complete gibberish and idiocy, but apparently Oracle’s already been doing enough around it to advertise the fact. I will have my cake and eat it too!

We’ve been pumping out the posts from the shows we went to – let me tell you, live-blogging is hard when you’re trying to share apparently miniscule amounts of bandwidth with 14,000 other attendees – and we have even more to share as we step back, contemplate and describe how some of the announcements, info and especially roadmaps fit into our overall picture over here at ScienceLogic.

For example, we released the results of our annual industry IT survey last week. Twice a year – at FOSE (for Government IT) and at Interop NY (for enterprises) – we take advantage of the fact that we have a big beautiful booth at these shows and offer a fabulous ScienceLogic t-shirt in return for a couple of minutes time with attendees living the problems we try to solve. Instead of telling people what their problems and priorities are, we like to ask.
Interop NY Survey - Trends and Challenges
Detailed Reports on Trends and Comparison to Government IT

And I just had to share this one because it is so bizarre. Are VMware and Paul Maritz guilty of plagiarism? You have to check this out to get even part of the picture. Apparently this guy has posted his slides (we know they are from VMworld 2007 because it says so in the lower-right-hand corner…) which prove that the “virtual datacenter operating system” idea was his idea a year before it showed up on Maritz’s keynote this year. Hmmm. And then after posting all these slides and making all the connections between his presentation and Maritz’s, he says he’s just kidding about the plagiarism. Can anyone sort this out and let me know?

I’ll tell you who wasn’t kidding when I went by their booth at VMworld – a certain chargeback vendor and VMware “partner” who was quite shocked two months ago when they walked into a meeting with VMware about future roadmap. Apparently, the slides they saw (preview of VMware’s announcement re adding extended chargeback capability within vCenter management services) were mighty might similar to slides they had given in a presentation to VMware about their own roadmap. Coincidence? I’ll let you decide. And I’ll also say, their strategy to combat this – support for Hyper-V coming early in 2009.

Defining Cloud Computing

Cloud computing is a very active community. The Google Group gets 600 posts per month and many bloggers are covering the space. However, “cloud computing” is impossible to define in a way that satisfies everyone (or even most). Cloud computing is not alone in this controversy, consider the definition and meaning of “Web 2.0″, “mashups” or “RESTful architecture”. All of these terms are relatively recent. According to Google Trends, these terms became popular to the general public sometime between 2005 and 2007:

• Web 2.0 - often confused with RIA, AKA Social Computing, Long-Tail Apps, Crowdware (2005 by O’Reilly Media)
• Mashup - made popular by Google Maps, AKA Composite/Situational Apps. (2005)
• REST - Has a strict definition, but many don’t understand it and abuse the term. (2006 by R. Fielding)
• Cloud computing - collides with many other terms, such as SaaS, Grid, Utility, PaaS, etc. (2007)

The definition of cloud computing is in progress:

There’s a Darwinian evolution of the exact definition of cloud computing running around. We’re about a country mile away from “knowing when I see it”, which is excellent progress. The cloud to everyone’s silver-lining has enough material to write a 3 volume desktop reference at this point. - Michael Cote, June 2008

Definition #1 - “Cloud computing is the realisation of Internet (”Cloud”) based development and use of computer technology (”Computing”) delivered by an ecosystem of providers. - Sam Johnston, July 2008

Definition #2 - “Cloud computing = network computing. I love the idea of cloud computing, the next evolution of the most network intensive architecture possible, but one that if it works well, is transparent. It’s all about the transparency.” - Douglas Gourlay, Cisco, May 2008

Definition #3 - “There seems to be a group myopia around so-called “cloud computing” and its definitions. What we’re really talking about are “cloud services” of which, “computing” is only a subset…Cloud services are not SaaS. They are far more akin to web services…” - Randy Bias, neoTactics, May 2008

(Anti-)Definition #4 - “Note that I refer to cloud services, not to the could. I am not interested in defining cloud as a term, because I don’t think it’s very useful. For those of us in the distributed computing’s pace

The Working Definition (Winner!):

“…the notion of providing easily accessible compute and storage resources on a pay-as-you-go, on-demand basis, from a virtually infinite infrastructure managed by someone else. As a customer, you don’t know where the resources are, and for the most part, you don’t care. What’s really important is the capability to access your application anywhere, move it freely and easily, and inexpensively add resources for instant scalability.” - Mitchell Crandell, Rightscale, June 2008

Taxonomies of the Cloud Space

Taxonomies are useful to provide insight into a market. It classifies a multitude of players into a smaller bucket.

Andreessen’s Platforms - September 2007

Provided an early taxonomy model for emerging cloud platforms

Platform being a system that can be programmed

• Access API - platform that provides web service endpoints
• Plug-In API - platform invokes your code, that you have deployed remotely
• Runtime Environment - your code runs inside the platform’s process space.

Mehta 11 Layer Stack, April 2008

1. Facilities (space, power, cooling)
2. Network
3. Hardware (e.g. servers Amazon EC2 runs)
4. Hardware virtualization (e.g. Xen for EC2) - optional
5. O/S (e.g. Linux)
6. Systems Management (e.g., tools to manage EC2 instances)
7. Application Middleware (e.g., MySQL on EC2)
8. Application Code
9. Application APIs / Web Services
10. GUI for Application
11. GUI for Application Development / Customization

Croll Cloud Stack, June 2008

7 layer stack within Turnkey app and Generic Platform.

Turnkey app

• SaaS
• Extensible app
• Generic IDE
• Constrained APIs
• App Cluster
• Virtual Data Center
• Virtual Servers

Generic Platform

The bottom of Alistair’s stack includes “root access “style compute clouds.

Robert Anderson, July 2008

3 layer stack

• Software (SaaS)
• Platform (PaaS)
• Infrastructure (IaaS)

This is the model taxonomy for this session.

Related Concepts and Terms

• Infrastructure as a Service (IaaS), Hardware as a Service (HaaS) are synonyms to cloud infrastructure.
• Virtualization
• Hosting
• Autonomic computing
• Distributed computing
• Grid computing

Cloud Applications

• SaaS
• S+S (Software+Services)
• Managed Service Provider (MSP)

I agree that these gentlemen are top notch researchers, witnessed by the fact that the authors do not mention nor claim to be “complex event processing” anywhere in their paper!  This paper is not about CEP, nor does it claim to be about CEP, it is about stream processing and unifying SQL standards.

ABSTRACT: This paper describes a unification of two different SQL extensions for streams and its associated semantics. We use the data models from Oracle and StreamBase as our examples. Oracle uses a time-based execution model while StreamBase uses a tuple-based execution model. Time-based execution provides a way to model simultaneity while tuple-based execution provides a way to react to primitive events as soon as they are seen by the system.

Asmentioned on numerous occasions, stream processing is a very important area in CEP/EP.   It is important not to confuse the higher situational knowledge from object-object correlation and state management with the single-object event refinement that occurs in stream processsing.    Event stream processing is fundamentally different than complex event processing. 

Event stream processing performs operations on streaming event objects.   In almost all advanced CEP/EP applications is is necessary to perform robust track and trace operations on streaming event objects, like tracking the position of an airplane.    Tracking the position of an aircraft can be modelled very nicely with event stream processing.  Tracking individual event objects is a precuror to multiclass object situation refinement.

When we manage the state of all the aircraft in the skies over New York, you need more than a stream processing construct.  You need to manage the state of all the aircraft.  Paul Vincent of TIBCO Software being to address this important point in The Value of State…  

Again, we will be better equiped to solve complex distributed event processing problems if we do not confuse the notion of event stream processing and complex event processing.   These technologies are indeed complimentary, both very important, but they are not the same.

I applaud Oracle and StreamBase’s work toward a unified standard for SQL extensions for streams.

The following excellent discussion is reposted from Streaming SQL approaches insist in ignoring causality by PatternStorm.

The recent paper “Towards a Streaming SQL Standard” by Oracle and Streambase unifies and generalizes two different execution models of Streaming SQL: Oracle’s and StreamBase’s.

While it’s true that the generalization succeeds in overcoming the unability of both execution models of producing correct results for astonishing simple queries (showing evidence of the actual limitations of these two Streaming SQL languages) it is also true that the generalization is closer to being overly complex than natural and intuitive.

The root cause behind the actual limitations of these two Streaming SQL languages is that their execution models “hardcode” the way events can be related to each other: in the Oracle case events are partially ordered by timestamp, in the StreamBase case events are totally ordered by time of arrival. These design decisions (natural in a stream oriented lamguage) have strong implications on what queries can be answered correctly, particularly when these queries involve joins of derived streams.

The generalization, of course, mainly consists in providing a new operator that allows the user to establish custom ordering relationships among the events (the SPREAD operator), which is good news but takes us to the fundamental issue: event processing cannot be reduced to stream processing, that is, to the processing of events that are totally or partially ordered by a pre-defined relationship (as Oracle and StreamBase actual implementations do), on the contrary, no particular ordering can be assumed because the user needs to be able to order the events in different ways in order to solve different problems. This is what event processing is about and the paper provides evidence that Streaming SQL approaches have found the need to move towards that direction and are having trouble in their way.

For instance, one of the queries used in the paper as an example of a query that StreamBase cannot solve (but Oracle can) is the following: correlate the stream that contains the total number of cars on the road for each time interval with the stream that contains the total average speed of the cars on the road for each time interval in order to detect the situation where the avergae speed is below 45 and the total number of cars is two or more. This query can be very easily and more robustly solved if you order the events by causality rather than by time, that is, if you have each position report update the average speed stream and the total number of cars stream and then you causally relate each position report to the new average speed event and the new total number of cars event that it generates; then the query is just a matter of detecting all report speeds that are causally related both to an average speed event below 45 and a total number of cars event of two or more (notice that this approach is more robust than Oracle’s time-based one because it works without requiring derived streams to be synchronized with the report speed stream)

Conclusions:

• Event Processing is a generalization of Stream Processing (as the paper shows)
• Event Processing requires providing the ability to the user of creating custom relationships among events and then define patterns/queries using those custom relationships.
• Causality is more often than not a more robust and easier criteria to order events than time or order of arrival.
• Event Processing Languages should support causality.

Regards,
PatternStorm