Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Reducing State Loss For Effective Trace Sampling of Superscalar Processors
ICCD '96 Proceedings of the 1996 International Conference on Computer Design, VLSI in Computers and Processors
SMARTS: accelerating microarchitecture simulation via rigorous statistical sampling
Proceedings of the 30th annual international symposium on Computer architecture
Understanding and Designing New Server Architectures for Emerging Warehouse-Computing Environments
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
FAWN: a fast array of wimpy nodes
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Energy-efficient cluster computing with FAWN: workloads and implications
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Web search using mobile cores: quantifying and mitigating the price of efficiency
Proceedings of the 37th annual international symposium on Computer architecture
| Hi-index | 0.00 |
Designing data centers for Web 2.0 social networking applications is a major challenge because of the large number of users, the large scale of the data centers, the distributed application base, and the cost sensitivity of a data center facility. Optimizing the data center for performance per dollar is far from trivial. In this paper, we present a case study characterizing and evaluating hardware/software design choices for a real-life Web 2.0 workload. We sample the Web 2.0 workload both in space and in time to obtain a reduced workload that can be replayed, driven by input data captured from a real data center. The reduced workload captures the important services (and their interactions) and allows for evaluating how hardware choices affect end-user experience (as measured by response times). We consider the Netlog workload, a popular and commercially deployed social networking site with a large user base, and we explore hardware trade-offs in terms of core count, clock frequency, traditional hard disks versus solid-state disks, etc., for the different servers, and we obtain several interesting insights. Further, we present two use cases illustrating how our characterization method can be used for guiding hardware purchasing decisions as well as software optimizations.