The Forest Overlay Network

Forest is an overlay network designed for large real-time distributed systems. In particular, we’re interested in virtual worlds that provide high-quality interaction over a constantly changing pattern of communication. Forest is suitable for applications in which many entities send data to a large set of constantly changing entities. By using tree-structured channels, we can create logically isolated private networks which support both unicast and multicast routing. In this paper, we will discuss the core components of Forest and measure the performance of the control elements of the network. We will also provide examples of control sequences and the roles played by control elements in those sequences to help maintain fast, reliable data delivery

Cell Proliferation in Neuroblastoma

Neuroblastoma, the most common extracranial solid tumor of childhood, continues to carry a dismal prognosis for children diagnosed with advanced stage or relapsed disease. This review focuses upon factors responsible for cell proliferation in neuroblastoma including transcription factors, kinases, and regulators of the cell cycle. Novel therapeutic strategies directed toward these targets in neuroblastoma are discussed

Utility Scheduling for Multi-Tenant Clusters

The rapid increase in data size along with the complex patterns of data usage amongst data scientists presents new challenges for large-scale data analytics systems. Modern dis- tributed computing frameworks must support complex applications that range from answer- ing database queries to training machine learning models. As data centers have grown, managing their resources has become an increasingly important task. New applications have become popular that make traditional scheduling systems inadequate. In this thesis, we present distributed scheduling systems aimed at increasing cluster resource utilization by taking advantage of specific characteristics of data processing ap- plications. First, we identify a set of applications whose characteristics make them prime targets for utility-based scheduling. We then focus on two specific types of these applica- tions in the following systems: (i) SLAQ: a cluster scheduling system for machine learning (ML) training jobs that aims to maximize the qualities of all models trained. In exploratory model training, models can be improved more quickly by redirecting resources to jobs with the highest potential for improvement. SLAQ reduces latency and maximizes the quality of models being trained by a distributed ML cluster. (ii) ReLAQS: a cluster scheduling system for incremental approximate query process- ing (AQP) systems that aims to minimize the error of all approximate results. In AQP, queries compute approximate results by sampling data. In AQP, error can be reduced more quickly by allocating resources to queries with higher error. ReLAQS reduces the latency required to reach a query result with a given level of error in a shared AQP environment. These works demonstrate a novel set of methods that can be used in fine-grained scheduling to build responsive, efficient distributed systems. We have evaluated these systems on standard benchmark workloads and datasets, as well as popular ML algorithms, and show both reduced latency and increased accuracy of intermediary results

Utility Scheduling for Multi-Tenant Clusters

The rapid increase in data size along with the complex patterns of data usage amongst data scientists presents new challenges for large-scale data analytics systems. Modern dis- tributed computing frameworks must support complex applications that range from answer- ing database queries to training machine learning models. As data centers have grown, managing their resources has become an increasingly important task. New applications have become popular that make traditional scheduling systems inadequate. In this thesis, we present distributed scheduling systems aimed at increasing cluster resource utilization by taking advantage of specific characteristics of data processing ap- plications. First, we identify a set of applications whose characteristics make them prime targets for utility-based scheduling. We then focus on two specific types of these applica- tions in the following systems: (i) SLAQ: a cluster scheduling system for machine learning (ML) training jobs that aims to maximize the qualities of all models trained. In exploratory model training, models can be improved more quickly by redirecting resources to jobs with the highest potential for improvement. SLAQ reduces latency and maximizes the quality of models being trained by a distributed ML cluster. (ii) ReLAQS: a cluster scheduling system for incremental approximate query process- ing (AQP) systems that aims to minimize the error of all approximate results. In AQP, queries compute approximate results by sampling data. In AQP, error can be reduced more quickly by allocating resources to queries with higher error. ReLAQS reduces the latency required to reach a query result with a given level of error in a shared AQP environment. These works demonstrate a novel set of methods that can be used in fine-grained scheduling to build responsive, efficient distributed systems. We have evaluated these systems on standard benchmark workloads and datasets, as well as popular ML algorithms, and show both reduced latency and increased accuracy of intermediary results

Cell Proliferation in Neuroblastoma

Neuroblastoma, the most common extracranial solid tumor of childhood, continues to carry a dismal prognosis for children diagnosed with advanced stage or relapsed disease. This review focuses upon factors responsible for cell proliferation in neuroblastoma including transcription factors, kinases, and regulators of the cell cycle. Novel therapeutic strategies directed toward these targets in neuroblastoma are discussed

SLAQ: quality-driven scheduling for distributed machine learning

Training machine learning (ML) models with large datasets can incur significant resource contention on shared clusters. This training typically involves many iterations that continually improve the quality of the model. Yet in exploratory settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. We describe SLAQ, a cluster scheduling system for approximate ML training jobs that aims to maximize the overall job quality. When allocating cluster resources, SLAQ explores the quality-runtime trade-offs across multiple jobs to maximize system-wide quality improvement. To do so, SLAQ leverages the iterative nature of ML training algorithms, by collecting quality and resource usage information from concurrent jobs, and then generating highly-tailored quality-improvement predictions for future iterations. Experiments show that SLAQ achieves an average quality improvement of up to 73% and an average delay reduction of up to 44% on a large set of ML training jobs, compared to resource fairness schedulers

Targeting Focal Adhesion Kinase Suppresses the Malignant Phenotype in Rhabdomyosarcoma Cells

Despite the tremendous advances in the treatment of childhood solid tumors, rhabdomyosarcoma (RMS) continues to provide a therapeutic challenge. Children with metastatic or relapsed disease have a disease-free survival rate under 30%. Focal adhesion kinase (FAK) is a nonreceptor tyrosine kinase that is important in many facets of tumorigenesis. Signaling pathways both upstream and downstream to FAK have been found to be important in sarcoma tumorigenesis, leading us to hypothesize that FAK would be present in RMS and would impact cellular survival. In the current study, we showed that FAK was present and phosphorylated in pediatric alveolar and embryonal RMS tumor specimens and cell lines. We also examined the effects of FAK inhibition upon two RMS cell lines utilizing parallel approaches including RNAi and small molecule inhibitors. FAK inhibition resulted in decreased cellular survival, invasion, and migration and increased apoptosis. Furthermore, small molecule inhibition of FAK led to decreased tumor growth in a nude mouse RMS xenograft model. The findings from this study will help to further our understanding of the regulation of tumorigenesis in RMS and may provide desperately needed novel therapeutic strategies for these difficult-to-treat tumors