New Scheduling Algorithms: Improving Fairness and Quality of Service [1] Sungjin Im, Benjamin Moseley, Kirk Pruhs: A tutorial on amortized local competitiveness in online scheduling. SIGACT News 42(2): 83-97 (2011) [2] Sungjin Im, Benjamin Moseley: An online scalable algorithm for average flow time in broadcast scheduling. ACM Transactions on Algorithms 8(4): 39 (2012) [3] Kyle Fox, Sungjin Im, Benjamin Moseley: Energy Efficient Scheduling of Parallelizable Jobs. SODA 2013: 948-957 Schedule 1 optimizes the average flow time and schedule 2 optimizes the L2-norm of the flow times. The example shows how the first scheduler can be unfair as compared to the second scheduler. Develop new algorithmic and analysis techniques to discover scheduling algorithms with strong worst-case performance guarantees Optimize quality of service objectives that enforce both fairness to clients and enforce good quality of service Discover fundamental algorithmic ideas that transcend different objectives and environments Introduce analysis techniques that are widely applicable Improve upon the state of the art performance guarantees Introduce models for scheduling environments in new and emerging technologies Heterogenous parallel processor environments Distributed computing Energy aware scheduling for more sustainable computing In the past, most work focus on settings where all machines are identical. In the future, it is thought that heterogeneous environments will be the dominant architecture. This is so that slow low power energy efficient machines can process less critical jobs. Scheduling in heterogenous environments is challenging and requires sophisticated algorithmic techniques