The seven deadly sins of cloud computing research

The paper is presented in HotCloud2012 by

Steven Hand and Derek Murray as co-authors and Malte Schwarzkopf as the main author who is a PhD student at Cambridge.

The paper is on seven mistakes that researchers commit when dealing with Cloud and data processing in the cloud. The list is the following

• Unnecessary distributed parallelism 
• Assuming performance homogeneity
• Picking the low hanging fruit
• Forcing the abstraction
• Unrepresentative workload
• Assuming perfect elasticity
• Ignoring fault tolerance

what seems to be the case with all seven arguments is that it particularly focuses on parallel algorithms applicable to the cloud, mostly by benefiting from Map / Reduce.

The paper is heavily performance oriented and at least the first 3 points also represented by the author in the talk have performance comparisons. Those are the ones that provided nice graphs for the slides to the author. Some of the points that are interesting are the fact that machines may get overloaded and that the data needs to be more representative. But I think there is nothing very new about the paper and these are the already known facts re-iterated in the paper. Yet it is good that he highlighted some issues with the papers already being published. Maybe others will try to avoid the mistakes mentioned in the paper.

You can tell it is a work at the early years of this guy's PhD.

Link to the paper: https://www.usenix.org/system/files/conference/hotcloud12/hotcloud12-final70.pdf