Serverless computing has gained attention due to its fine-grained provisioning, large-scale multi-tenancy, and on-demand scaling. However, it also forces applications to externalize state in remote storage, adding substantial overheads. To fix this "data shipping problem" we built Shredder, a low-latency multi-tenant cloud store that allows small units of computation to be performed directly within storage nodes. Storage tenants provide Shredder with JavaScript functions (or WebAssembly programs), which can interact directly with data without moving them over the network.

Economies of scale associated with hyper-scale public cloud platforms offer flexibility and cost-effectiveness, resulting in various services and businesses moving to the cloud. One area with little progress in this direction is cellular core networks. A cellular core network manages the state of cellular clients; it is essentially a large distributed state machine with very different virtualization challenges compared to typical cloud services. In this paper we present a novel cellular core network architecture, called ECHO, particularly suited to public cloud deployments, where the availability guarantees might be an order of magnitude worse compared to existing (redundant) hardware platforms. We present the design and implementation of our approach and evaluate its functionality on a public cloud platform. Analysis shows ECHO promises higher availability than existing telco solutions.

Summary: Although RNA expression data are accumulating at a remarkable speed, gaining insights from them still requires laborious analyses, which hinder many biological and biomedical researchers. This report introduces a visual analytics framework that applies several well-known visualization techniques to leverage understanding of an RNA expression dataset. Our analyses on glycosaminoglycan-related genes have demonstrated the broad application of this tool, anexVis (analysis of RNA expression), to advance the understanding of tissue-specific glycosaminoglycan regulation and functions, and potentially other biological pathways. Availability and implementation: The application is accessible at https://anexvis.chpc.utah.edu/, source codes deposited on GitHub. Supplementary information: Supplementary data are available at Bioinformatics online.

Scalable in-memory key-value stores provide low-latency access times of a few microseconds and perform millions of operations per second per server. With all data in memory, these systems should provide a high level of reconfigurability. Ideally, they should scale up, scale down, and rebalance load more rapidly and flexibly than disk-based systems. Rapid reconfiguration is especially important in these systems since a) DRAM is expensive and b) they are the last defense against highly dynamic workloads that suffer from hot spots, skew, and unpredictable load. However, so far, work on in-memory key-value stores has generally focused on performance and availability, leaving reconfiguration as a secondary concern.