Computer Science Department
School of Computer Science, Carnegie Mellon University
AutoScale: Dynamic, Robust Capacity
Anshul Gandhi, Mor Harchol-Balter,
Energy costs for data centers continue to rise, already exceeding $15 billion yearly. Sadly much of this power is wasted. Servers are only busy 10-30% of the time on average, but they are often left on, while idle, utilizing 60% or more of peak power when in the idle state.
We introduce a dynamic capacity management policy, AutoScale, that greatly reduces the number of servers needed in data centers driven by unpredictable, time-varying load, while meeting response time SLAs. AutoScale scales the data center capacity, adding or removing servers as needed. AutoScale has two key features: (i) it autonomically maintains just the right amount of spare capacity to handle bursts in the request rate; and (ii) it is robust not just to changes in the request rate of real-world traces, but also request size and server efficiency.
We evaluate our dynamic capacity management approach via implementation on a 38-server multitier data center, serving a web site of the type seen in Facebook or Amazon, with a key-value store workload. We demonstrate that AutoScale vastly improves upon existing dynamic capacity management policies with respect to meeting SLAs and robustness.
*Intel Labs, Pittsburgh, Pennsylvania