University Calendar
Events for July
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PhD Defense - Alana Shine
Tue, Jul 14, 2020 @ 03:30 PM - 05:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Defense - Alana Shine
Tues, Jul 14, 2020
3:30 PM - 5:30 PM
Ph.D. Defense - Alana Shine 7/14 3:30 pm Generative graph models subject to global similarity
Ph.D. Candidate: Alana Shine
Date: Tuesday, July 14, 2020
Time: 3:30 pm - 5:30 pm
Committee: David Kempe (chair), Aram Galstyan, Xiang Ren, Kayla de la Haye
Title: Generative graph models subject to global similarity
Zoom: https://usc.zoom.us/j/8333742899
Abstract:
This thesis explores how to build generative graph models subject to global features in order to capture connectivity structure. Generative graph models sample from sets of "similar" graphs according to a probability distribution and are important for simulation studies, anomaly detection, and characterizing properties of real world graphs in areas such as social science and network design. The vague notion of generating "similar" graphs has prompted a vast quantity of generative graph models that define similarity according to various graph features. Graph features used include degree distribution, motif counts, and high-level community structure. Typically, these features are local: the property can be segmented into parts with each part being computed entirely from its own subgraph. For example, node degrees. Instead, this work analyzes graph generation subject to global features that require the entire graph to compute. This thesis focuses on global features that capture connectivity because they are critical in determining how information/diseases spread on graphs and simulations of information/disease spread is a prominent application of generative graph models.
A large class of generative graph models are built from a single real world "target" graph and its features. This thesis presents three new generative graph models that target global similarity through matching (1) connectivity across cuts, (2) random walk behavior, and (3) eigenvalues of the symmetric normalized Laplacian matrix. All three of these global graph features are related to a widely used notion of graph connectivity called conductance. We observe on a number of real world target graphs that the global generative graph models perform superior to benchmark generative graph models on a number of similarity objectives.
WebCast Link: https://usc.zoom.us/j/8333742899
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
PhD Defense - Haoyu Huang
Thu, Jul 16, 2020 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Haoyu Huang 7/16 2:00 pm "Nova-LSM: A Distributed, Component-based LSM-tree Data Store"
Ph.D. Candidate: Haoyu Huang
Date: Thursday, July 16, 2020
Time: 2:00 pm - 4:00 pm
Committee: Shahram Ghandeharizadeh (chair), Murali Annavaram, Jyotirmoy V. Deshmukh
Title: Nova-LSM: A Distributed, Component-based LSM-tree Data Store
Zoom: https://usc.zoom.us/j/99943500149
Google Meet (only if there are issues with Zoom): meet.google.com/ruu-jjiu-fbk
Abstract:
The cloud challenges many fundamental assumptions of today's monolithic data stores. It offers a diverse choice of servers with alternative forms of processing capability, storage, memory sizes, and networking hardware. It also offers fast network between servers and racks such as RDMA. This motivates a component-based architecture that separates storage from processing for a data store. This architecture complements the classical shared-nothing architecture by allowing nodes to share each other's disk bandwidth. This is particularly useful with a skewed pattern of access to data by scattering a large file across many disks instead of storing it on one disk.
This emerging component-based software architecture constitutes the focus of this dissertation. We present design, implementation, and evaluation of Nova-LSM as an example of this architecture. Nova-LSM is a component-based design of LSM-tree using RDMA. Its components implement the following novel concepts. First, they use RDMA to enable nodes of a shared-nothing architecture to share their disk bandwidth and storage. Second, they construct ranges dynamically at runtime to parallelize compaction and boost performance. Third, they scatter blocks of a file across an arbitrary number of disks and use power-of-d to scale. Fourth, the logging component separates availability of log records from their durability. These design decisions provide for an elastic system with well-defined knobs that control its performance and scalability characteristics. We present an implementation of these designs using LevelDB as a starting point. Our evaluation shows Nova-LSM scales and outperforms its monolithic counterpart by several orders of magnitude. This is especially true with workloads that exhibit a skewed pattern of access to data.WebCast Link: https://usc.zoom.us/j/99943500149
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
