Algorithmic challenges in green data centers

With data centers being the supporting infrastructure for a wide range of IT services, their efficiency has become a big concern to operators, as well as to society, for both economic and environmental reasons. The goal of this thesis is to design energy-efficient algorithms that reduce energy cost while minimizing compromise to service. We focus on the algorithmic challenges at different levels of energy optimization across the data center stack. The algorithmic challenge at the device level is to improve the energy efficiency of a single computational device via techniques such as job scheduling and speed scaling. We analyze the common speed scaling algorithms in both the worst-case model and stochastic model to answer some fundamental issues in the design of speed scaling algorithms. The algorithmic challenge at the local data center level is to dynamically allocate resources (e.g., servers) and to dispatch the workload in a data center. We develop an online algorithm to make a data center more power-proportional by dynamically adapting the number of active servers. The algorithmic challenge at the global data center level is to dispatch the workload across multiple data centers, considering the geographical diversity of electricity price, availability of renewable energy, and network propagation delay. We propose algorithms to jointly optimize routing and provisioning in an online manner. Motivated by the above online decision problems, we move on to study a general class of online problem named "smoothed online convex optimization", which seeks to minimize the sum of a sequence of convex functions when "smooth" solutions are preferred. This model allows us to bridge different research communities and help us get a more fundamental understanding of general online decision problems.

Situation awareness application

This thesis describes the design and implementation of a situation awareness application. The application gathers data from sensors including accelerometers for monitoring earthquakes, carbon monoxide sensors for monitoring fires, radiation detectors, and dust sensors. The application also gathers Internet data sources including data about traffic congestion on daily commute routes, information about hazards, news relevant to the user of the application, and weather. The application sends the data to a Cloud computing service which aggregates data streams from multiple sites and detects anomalies. Information from the Cloud service is then displayed by the application on a tablet, computer monitor, or television screen. The situation awareness application enables almost all members of a community to remain aware of critical changes in their environments.