System-Level Characterization of Datacenter Applications

Manu Awasthi, Tameesh Suri, Zvika Guz, Anahita Shayesteh, Mrinmoy Ghosh, Vijay Balakrishnan
2015 Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering - ICPE '15  
In recent years, a number of benchmark suites have been created for the "Big Data" domain, and a number of such applications fit the client-server paradigm. A large volume of recent literature in characterizing "Big Data" applications have largely focused on two extremes of the characterization spectrum. On one hand, multiple studies have focused on client-side performance. These involve fine-tuning serverside parameters for an application to get the best client-side performance. On the other
more » ... treme, characterization focuses on picking one set of client-side parameters and then reporting the server microarchitectural statistics under those assumptions. While the two ends of the spectrum present interesting results, this paper argues that they are not enough, and in some cases, undesirable, to drive system-wide architectural decisions in datacenter design. This paper shows that for the purposes of designing an efficient datacenter, detailed microarchitectural characterization of "Big Data" applications is an overkill. It identifies four main system-level macro-architectural features and shows that these features are more representative of an application's system level behavior. To this end, a number of datacenter applications from a variety of benchmark suites are evaluated and classified into these previously identified macro-architectural features. Based on this analysis, the paper further shows that each application class will benefit from a very different server configuration leading to a highly efficient, cost-effective datacenter.
doi:10.1145/2668930.2688059 dblp:conf/wosp/AwasthiSGSGB15 fatcat:sxlhqvtmljfvxnbe5y226yefzu