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The main responsibility of resource and delivery managers at Hewlett-Packard (HP) Enterprise Services (HPES) involves matching resources (skilled professionals) with jobs that project opportunities require. The previous Solution Opportunity Approval and Review (SOAR) process at HPES addressed uncertainty by producing decentralized project staffing decisions. This often led to many last-minute subjective, sometimes costly, resource allocation decisions. Based on our research, we developed a decision support tool for resource planning (RP) to enhance the SOAR process. It optimizes matching professionals who have diverse delivery roles and skills to jobs and projects across geographical locations while explicitly accounting for both demand and supply uncertainties. It also embeds capabilities for managers to incorporate tacit human knowledge and judgment information into the decision-making process. With its 2009 deployment in Best Shore, Bangalore operations of HPES, the RP tool’s significant benefits include reduced service delivery costs, increased workforce utilization, and profitability.

We describe a new class of utility-maximization scheduling problem with precedence constraints, the disconnected staged scheduling problem (DSSP). DSSP is a nonpreemptive multiprocessor deadline scheduling problem that arises in several commercially-important applications, including animation rendering, protein analysis, and seismic signal processing. DSSP differs from most previously-studied deadline scheduling problems because the graph of precedence constraints among tasks within jobs is disconnected, with one component per job. Another difference is that in practice we often lack accurate estimates of task execution times, and so purely offline solutions are not possible. However we do know the set of jobs and their precedence constraints up front and therefore some offline planning is possible.Our solution decomposes DSSP into an offline job selection phase followed by an online task dispatching phase. We model the former as a knapsack problem and explore several solutions to it, describe a new dispatching algorithm for the latter, and compare both with existing methods. Our theoretical results show that while DSSP is NP-hard and inapproximable in general, our two-phase scheduling method guarantees a good performance bound for many special cases. Our empirical results include an evaluation of scheduling algorithms on a real animation-rendering workload; we present a characterization of this workload in a companion paper. The workload records eight weeks of activity on a 1,000-CPU cluster used to render portions of the full-length animated feature film Shrek 2 in 2004. We show that our improved scheduling algorithms can substantially increase the aggregate value of completed jobs compared to existing practices. Our new task dispatching algorithm LCPF performs well by several metrics, including job completion times as well as the aggregate value of completed jobs. * A 2-page poster will appear in SIGMETRICS'05 in Banff, Canada.