Run Time Approximation of Non-blocking Service Rates for Streaming Systems

Stream processing is a compute paradigm that promises safe and efficient parallelism. Modern big-data problems are often well suited for stream processing's throughput-oriented nature. Realization of efficient stream processing requires monitoring and optimization of multiple communications links. Most techniques to optimize these links use queueing network models or network flow models, which require some idea of the actual execution rate of each independent compute kernel within the system. What we want to know is how fast can each kernel process data independent of other communicating kernels. This is known as the "service rate" of the kernel within the queueing literature. Current approaches to divining service rates are static. Modern workloads, however, are often dynamic. Shared cloud systems also present applications with highly dynamic execution environments (multiple users, hardware migration, etc.). It is therefore desirable to continuously re-tune an application during run time (online) in response to changing conditions. Our approach enables online service rate monitoring under most conditions, obviating the need for reliance on steady state predictions for what are probably non-steady state phenomena. First, some of the difficulties associated with online service rate determination are examined. Second, the algorithm to approximate the online non-blocking service rate is described. Lastly, the algorithm is implemented within the open source RaftLib framework for validation using a simple microbenchmark as well as two full streaming applications.Comment: technical repor

Cytoskeletal dynamics of Cytotoxic T cells during migration in the tumour microenvironment

Typically, migrating T cells display an elongated polarized shape with a very dynamic leading edge and a uropod in the rear. This ‘amoeboid’ movement guarantees a fast migration driven by the formation of polarized protrusions at the front. The actomyosin cytoskeleton is responsible for the generation of the forces that are involved in this process. This thesis aims to determine what is the effect of T cell migration when different components of the actomyosin cortex were inhibited using a pharmacological approach. We found that the inhibition of each component of the actomyosin cortex, T cells display different conformation of the actin filaments and produce different type of protrusion. Furthermore, T cell migration is an important feature for the killing and clearance of canner cells. It has been reported that T cells can migrate efficiently in any kind of tissue whilst scanning for cognate antigen. On the other hand, it is known that the tumor microenvironment secretes immunosuppressive cytokines such as TGF-β impairing the antitumor activity of T cells. Therefore, we aim to determine how TGF-β affects the migration behavior of T cells and its consequences in the scanning strategy to search their cognate antigen

Adaptive Network Dynamics and Evolution of Leadership in Collective Migration

The evolution of leadership in migratory populations depends not only on costs and benefits of leadership investments but also on the opportunities for individuals to rely on cues from others through social interactions. We derive an analytically tractable adaptive dynamic network model of collective migration with fast timescale migration dynamics and slow timescale adaptive dynamics of individual leadership investment and social interaction. For large populations, our analysis of bifurcations with respect to investment cost explains the observed hysteretic effect associated with recovery of migration in fragmented environments. Further, we show a minimum connectivity threshold above which there is evolutionary branching into leader and follower populations. For small populations, we show how the topology of the underlying social interaction network influences the emergence and location of leaders in the adaptive system. Our model and analysis can describe other adaptive network dynamics involving collective tracking or collective learning of a noisy, unknown signal, and likewise can inform the design of robotic networks where agents use decentralized strategies that balance direct environmental measurements with agent interactions.Comment: Submitted to Physica D: Nonlinear Phenomen

Cytoskeletal dynamics of Cytotoxic T cells during migration in the tumour microenvironment

Typically, migrating T cells display an elongated polarized shape with a very dynamic leading edge and a uropod in the rear. This ‘amoeboid’ movement guarantees a fast migration driven by the formation of polarized protrusions at the front. The actomyosin cytoskeleton is responsible for the generation of the forces that are involved in this process. This thesis aims to determine what is the effect of T cell migration when different components of the actomyosin cortex were inhibited using a pharmacological approach. We found that the inhibition of each component of the actomyosin cortex, T cells display different conformation of the actin filaments and produce different type of protrusion. Furthermore, T cell migration is an important feature for the killing and clearance of canner cells. It has been reported that T cells can migrate efficiently in any kind of tissue whilst scanning for cognate antigen. On the other hand, it is known that the tumor microenvironment secretes immunosuppressive cytokines such as TGF-β impairing the antitumor activity of T cells. Therefore, we aim to determine how TGF-β affects the migration behavior of T cells and its consequences in the scanning strategy to search their cognate antigen

A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network

Seismic modeling using the frozen Gaussian approximation

We adopt the frozen Gaussian approximation (FGA) for modeling seismic waves. The method belongs to the category of ray-based beam methods. It decomposes seismic wavefield into a set of Gaussian functions and propagates these Gaussian functions along appropriate ray paths. As opposed to the classic Gaussian-beam method, FGA keeps the Gaussians frozen (at a fixed width) during the propagation process and adjusts their amplitudes to produce an accurate approximation after summation. We perform the initial decomposition of seismic data using a fast version of the Fourier-Bros-Iagolnitzer (FBI) transform and propagate the frozen Gaussian beams numerically using ray tracing. A test using a smoothed Marmousi model confirms the validity of FGA for accurate modeling of seismic wavefields.Comment: 5 pages, 8 figure