-Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this paper, we propose a novel dynamic provisioning technique for multi-tier Internet applications that employs (i) a flexible queuing model to determine how much resources to allocate to each tier of the application, and (ii) a combination of predictive and reactive methods that determine when to provision these resources, both at large and small time scales. We propose a novel data center architecture based on virtual machine monitors to reduce provisioning overheads. Our experiments on a forty-machine Xen/Linux-based hosting platform demonstrate the responsiveness of our technique in handling dynamic workloads. In one scenario where a flash crowd caused the workload of a three-tier application to double, our technique was able to double the application capacity within five minutes, thus maintaining response time targets. Our technique also reduced the overhead of switching servers across applications from several minutes to less than a second, while meeting the performance targets of residual sessions.

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In this position paper, we examine recent technology trends that have resulted in a broad spectrum of camera sensors, wireless radio technologies, and embedded sensor platforms with varying capabilities. We argue that future sensor applications will be hierarchical with multiple tiers, where each tier employs sensors with different characteristics. We argue that multi-tier networks are not only scalable, they offer a number of advantages over simpler, single-tier unimodal networks: lower cost, better coverage, higher functionality, and better reliability. However, the design of such mixed networks raises a number of new challenges that are not adequately addressed by current research. We discuss several of these challenges and illustrate how they can be addressed in the context of SensEye, a multi-tier video surveillance application that we are designing in our research group.

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