Cloud Clone Augmented Execution

Exploiting cloud clone augmentation

A very impressive white paper has come out from the Intel Berkley Lab from Byung-Gon Chun and Petros Maniatis titled  "Augmented Smartphone Applications Through Clone Execution".  as part of the Proceedings of the 12th Workshop on Hot Topics in Operating Systems (HotOS XII), May 2009.

http://berkeley.intel-research.net/bgchun/clonecloud-hotos09.pdf       

The paper explores a research topic of how to deploy workload intensive operations from a Smartphone platform into the cloud and return the results to exploit bursting to augment the mobile services. The project title is CloneCloud. 

http://berkeley.intel-research.net/bgchun/clonecloud/   

What is particularly interesting is the way a number of virtualization topics and augmented processes for workloads can be split between the cell phone and the cloud. The key point here is the separation and spread of workloads between different local and virtual platforms such that the computational and storage capabilities are leveraged as "one networked computer" service. Add to this augmented application services not covered in the article and you start to see a number of added value services in a business context. This approach is evident when in a recent analysis I completed of the types of cloud burst services, it is clearly not constrained to excess volume or low volume workload management but also the redirection of specific types of work loads to cloud facilities.

Another very interesting statement in the white paper has been "multiplicity"   a feature that has been in the scope of virtualization to optimize workloads but in the case of cloud computing services starts to create a number of very interesting possibilities hitherto considered as capacity resource constrained.

To quote the article:  

–Use multiple copies of the  system image executed in different ways.  This can help run data parallel applications.  E.g. indexing for disjoint sets of images.  This can also help the application “see the future”, by exhaustively exploring all possible next steps within some small horizon.  To enable for scenario model checking such as in monte carlo simulation.

This is a different way of of not only considering the virtualized device cloned into the cloud but in replicating the machine image it is possible to create multiple parallel tasks and from there a range of new service augmentation possibility not envisioned by the initial invocation. As previously suggested, if you add augmented application services into this you start to see a wider set of added value services. 

What this says to me is that the on premise and off premise geographical distinction is wrong in the sense it is the device and the machine specific locations that are the real on and off premise locations.   It also supports the view that temporal transformation as seen in batch to near real time processing in traditional timeframes may now evolve into new temporal transgformations that operate beyond immediate time and create multiple versions of "parallel time services".  In a sense there are multiple arrows of time.What it does also suggest is that the concept of a cloud switch may involve a number of second level and higher tiers of event interaction and types of VM patterns than just the Hypervisor workload management.

A summary of the cloud workload distribution patterns described in the article are:

•Primary functionality outsourcing

–Computation hungry applications such as speech processing, video indexing, and super-resolution are auto0matically split from the user interface and other low processing within the Smartphone.

•Background

–Functionality that does not need to interact with users in short term time scale. E.g. virus scanning, indexing files for faster search, analyzing photos for common search, crawling news web pages,

•Mainline

–Sitting between the primary and background augmentation. The user may opt to run a particular application in a wrapped fashion, altering the method of execution  not the semantics. E.g. private-data leak detection (taint check an application or applcation group, fault tolerance  (e.g. use a multi-variant execution analysis to protect the application from transparent bugs), debugging (e.g. keep track dynamically of allocated memory  in the heap to catch memory leaks

•Hardware

–Compensation for Smartphone weaknesses e.g. memory caps or other constraints and hardware peculiarities

•Multiplicity

–Use multiple copies of the  system image executed in different ways.  This can help run data parallel applications.  E.g. indexing for disjoint sets of images.  This can also help the application “see the future”, by exhaustively exploring all possible next steps within some small horizon.  To enable for scenario model checking such as in monte carlo simulation.