Data-Aware Scheduling in Datacenters

Datacenters have emerged as the dominant form of computing infrastructure over the last two decades. The tremendous increase in the requirements of data analysis has led to a proportional increase in power consumption and datacenters are now one of the fastest growing electricity consumers in the United States. Another rising concern is the loss of throughput due to network congestion. Scheduling models that do not explicitly account for data placement may lead to a transfer of large amounts of data over the network causing unacceptable delays. In this dissertation, we study different scheduling models that are inspired by the dual objectives of minimizing energy costs and network congestion in a datacenter. As datacenters are equipped to handle peak workloads, the average server utilization in most datacenters is very low. As a result, one can achieve huge energy savings by selectively shutting down machines when demand is low. In this dissertation, we introduce the network-aware machine activation problem to find a schedule that simultaneously minimizes the number of machines necessary and the congestion incurred in the network. Our model significantly generalizes well-studied combinatorial optimization problems such as hard-capacitated hypergraph covering and is thus strongly NP-hard. As a result, we focus on finding good approximation algorithms. Data-parallel computation frameworks such as MapReduce have popularized the design of applications that require a large amount of communication between different machines. Efficient scheduling of these communication demands is essential to guarantee efficient execution of the different applications. In the second part of the thesis, we study the approximability of the co-flow scheduling problem that has been recently introduced to capture these application-level demands. Finally, we also study the question, "In what order should one process jobs?'' Often, precedence constraints specify a partial order over the set of jobs and the objective is to find suitable schedules that satisfy the partial order. However, in the presence of hard deadline constraints, it may be impossible to find a schedule that satisfies all precedence constraints. In this thesis we formalize different variants of job scheduling with soft precedence constraints and conduct the first systematic study of these problems.

Mobilizing Maryland: a look into the voting tendencies of students at the University of Maryland

Gemstone Team VOTE-CP (Voice of the Electorate - Collegiate Participation)

The tithe: Public research university STEM faculty perspectives on sponsored research indirect costs

This study sought to understand the phenomenon of faculty involvement in indirect cost under-recovery. The focus of the study was on public research university STEM (science, technology, engineering and mathematics) faculty, and their perspectives on, and behavior towards, a higher education fiscal policy. The explanatory scheme was derived from anthropological theory, and incorporated organizational culture, faculty socialization, and political bargaining models in the conceptual framework. This study drew on two key assumptions. The first assumption was that faculty understanding of, and behavior toward, indirect cost recovery represents values, beliefs, and choices drawn from the distinct professional socialization and distinct culture of faculty. The second assumption was that when faculty and institutional administrators are in conflict over indirect cost recovery, the resultant formal administrative decision comes about through political bargaining over critical resources. The research design was a single site, qualitative case study with a focus on learning the meaning of the phenomenon as understood by the informants. In this study the informants were tenured and tenure track research university faculty in the STEM fields who were highly successful at obtaining Federal sponsored research funds, with individual sponsored research portfolios of at least one million dollars. The data consisted of 11 informant interviews, bolstered by documentary evidence. The findings indicated that faculty socialization and organizational culture were the most dominant themes, while political bargaining emerged as significantly less prominent. Public research university STEM faculty are most concerned about the survival of their research programs and the discovery facilitated by their research programs. They resort to conjecture when confronted by the issue of indirect cost recovery. The findings direct institutional administrators to consider less emphasis on compliance and hierarchy when working with expert professionals such as science faculty. Instead a more effective focus might be on communication and clarity in budget processes and organizational decision-making, and a concentration on critical administrative support that can relieve faculty administrative burdens. For higher education researchers, the findings suggest that we need to create more sophisticated models to help us understand organizations dependent on expert professionals.