Learning-aided Stochastic Network Optimization with Imperfect State Prediction

We investigate the problem of stochastic network optimization in the presence of imperfect state prediction and non-stationarity. Based on a novel distribution-accuracy curve prediction model, we develop the predictive learning-aided control (PLC) algorithm, which jointly utilizes historic and predicted network state information for decision making. PLC is an online algorithm that requires zero a-prior system statistical information, and consists of three key components, namely sequential distribution estimation and change detection, dual learning, and online queue-based control. Specifically, we show that PLC simultaneously achieves good long-term performance, short-term queue size reduction, accurate change detection, and fast algorithm convergence. In particular, for stationary networks, PLC achieves a near-optimal $[O(\epsilon)$, $O(\log(1/\epsilon)^2)]$ utility-delay tradeoff. For non-stationary networks, \plc{} obtains an $[O(\epsilon), O(\log^2(1/\epsilon)$ $+ \min(\epsilon^{c/2-1}, e_w/\epsilon))]$ utility-backlog tradeoff for distributions that last $\Theta(\frac{\max(\epsilon^{-c}, e_w^{-2})}{\epsilon^{1+a}})$ time, where $e_w$ is the prediction accuracy and $a=\Theta(1)>0$ is a constant (the Backpressue algorithm \cite{neelynowbook} requires an $O(\epsilon^{-2})$ length for the same utility performance with a larger backlog). Moreover, PLC detects distribution change $O(w)$ slots faster with high probability ($w$ is the prediction size) and achieves an $O(\min(\epsilon^{-1+c/2}, e_w/\epsilon)+\log^2(1/\epsilon))$ convergence time. Our results demonstrate that state prediction (even imperfect) can help (i) achieve faster detection and convergence, and (ii) obtain better utility-delay tradeoffs

Low latency via redundancy

Low latency is critical for interactive networked applications. But while we know how to scale systems to increase capacity, reducing latency --- especially the tail of the latency distribution --- can be much more difficult. In this paper, we argue that the use of redundancy is an effective way to convert extra capacity into reduced latency. By initiating redundant operations across diverse resources and using the first result which completes, redundancy improves a system's latency even under exceptional conditions. We study the tradeoff with added system utilization, characterizing the situations in which replicating all tasks reduces mean latency. We then demonstrate empirically that replicating all operations can result in significant mean and tail latency reduction in real-world systems including DNS queries, database servers, and packet forwarding within networks

Tachyon: Reliable, Memory Speed Storage for Cluster Computing Frameworks

Tachyon is a distributed file system enabling reliable data sharing at memory speed across cluster computing frameworks. While caching today improves read workloads, writes are either network or disk bound, as replication is used for fault-tolerance. Tachyon eliminates this bottleneck by pushing lineage, a well-known technique, into the storage layer. The key challenge in making a long-running lineage-based storage system is timely data recovery in case of failures. Tachyon addresses this issue by introducing a checkpointing algorithm that guarantees bounded recovery cost and resource allocation strategies for recomputation under commonly used resource schedulers. Our evaluation shows that Tachyon outperforms in-memory HDFS by 110x for writes. It also improves the end-to-end latency of a realistic workflow by 4x. Tachyon is open source and is deployed at multiple companies.National Science Foundation (U.S.) (CISE Expeditions Award CCF-1139158)Lawrence Berkeley National Laboratory (Award 7076018)United States. Defense Advanced Research Projects Agency (XData Award FA8750-12-2-0331

Online Convex Optimization Using Predictions

Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. We prove that achieving sublinear regret and constant competitive ratio for online algorithms requires the use of an unbounded prediction window in adversarial settings, but that under more realistic stochastic prediction error models it is possible to use Averaging Fixed Horizon Control (AFHC) to simultaneously achieve sublinear regret and constant competitive ratio in expectation using only a constant-sized prediction window. Furthermore, we show that the performance of AFHC is tightly concentrated around its mean

Low Latency Geo-distributed Data Analytics

Low latency analytics on geographically distributed dat-asets (across datacenters, edge clusters) is an upcoming and increasingly important challenge. The dominant approach of aggregating all the data to a single data-center significantly inflates the timeliness of analytics. At the same time, running queries over geo-distributed inputs using the current intra-DC analytics frameworks also leads to high query response times because these frameworks cannot cope with the relatively low and variable capacity of WAN links. We present Iridium, a system for low latency geo-distri-buted analytics. Iridium achieves low query response times by optimizing placement of both data and tasks of the queries. The joint data and task placement op-timization, however, is intractable. Therefore, Iridium uses an online heuristic to redistribute datasets among the sites prior to queries ’ arrivals, and places the tasks to reduce network bottlenecks during the query’s ex-ecution. Finally, it also contains a knob to budget WAN usage. Evaluation across eight worldwide EC2 re-gions using production queries show that Iridium speeds up queries by 3 × − 19 × and lowers WAN usage by 15% − 64 % compared to existing baselines

Microtubule-mitochondrial attachment facilitates cell division symmetry and mitochondrial partitioning in fission yeast

Association with microtubules inhibits the fission of mitochondria in Schizosaccharomyces pombe. Here, we show that this attachment of mitochondria to microtubules is an important cell-intrinsic factor in determining cell division symmetry. By comparing mutant cells that exhibited enhanced attachment and no attachment of mitochondria to microtubules (Dnm1Δ and Mmb1Δ, respectively), we show that microtubules in these mutants displayed aberrant dynamics compared to wild-type cells, which resulted in errors in nuclear positioning. This translated to cell division asymmetry in a significant proportion of both Dnm1Δ and Mmb1Δ cells. Asymmetric division in Dnm1Δ and Mmb1Δ cells resulted in unequal distribution of mitochondria, with the daughter cell that received more mitochondria growing faster than the other daughter cell. Taken together, we show the existence of homeostatic feedback controls between mitochondria and microtubules in fission yeast, which directly influence mitochondrial partitioning and, thereby, cell growth. This article has an associated First Person interview with the first author of the paper

Anti-cancer drug validation: the contribution of tissue engineered models

Abstract Drug toxicity frequently goes concealed until clinical trials stage, which is the most challenging, dangerous and expensive stage of drug development. Both the cultures of cancer cells in traditional 2D assays and animal studies have limitations that cannot ever be unraveled by improvements in drug-testing protocols. A new generation of bioengineered tumors is now emerging in response to these limitations, with potential to transform drug screening by providing predictive models of tumors within their tissue context, for studies of drug safety and efficacy. Considering the NCI60, a panel of 60 cancer cell lines representative of 9 different cancer types: leukemia, lung, colorectal, central nervous system (CNS), melanoma, ovarian, renal, prostate and breast, we propose to review current Bstate of art^ on the 9 cancer types specifically addressing the 3D tissue models that have been developed and used in drug discovery processes as an alternative to complement their studyThis article is a result of the project FROnTHERA (NORTE-01-0145-FEDER-000023), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This article was also supported by the EU Framework Programme for Research and Innovation HORIZON 2020 (H2020) under grant agreement n° 668983 — FoReCaST. FCT distinction attributed to Joaquim M. Oliveira (IF/00423/2012) and Vitor M. Correlo (IF/01214/2014) under the Investigator FCT program is also greatly acknowledged.info:eu-repo/semantics/publishedVersio

Bioreactor technologies to support liver function in vitro

Liver is a central nexus integrating metabolic and immunologic homeostasis in the human body, and the direct or indirect target of most molecular therapeutics. A wide spectrum of therapeutic and technological needs drives efforts to capture liver physiology and pathophysiology in vitro, ranging from prediction of metabolism and toxicity of small molecule drugs, to understanding off-target effects of proteins, nucleic acid therapies, and targeted therapeutics, to serving as disease models for drug development. Here we provide perspective on the evolving landscape of bioreactor-based models to meet old and new challenges in drug discovery and development, emphasizing design challenges in maintaining long-term liver-specific function and how emerging technologies in biomaterials and microdevices are providing new experimental models.National Institutes of Health (U.S.) (R01 EB010246)National Institutes of Health (U.S.) (P50-GM068762-08)National Institutes of Health (U.S.) (R01-ES015241)National Institutes of Health (U.S.) (P30-ES002109)5UH2TR000496-02National Science Foundation (U.S.). Emergent Behaviors of Integrated Cellular Systems (CBET-0939511)United States. Defense Advanced Research Projects Agency. Microphysiological Systems Program (W911NF-12-2-0039

Small nucleolar RNAs determine resistance to doxorubicin in human osteosarcoma

Doxorubicin (Dox) is one of the most important first-line drugs used in osteosarcoma therapy. Multiple and not fully clarified mechanisms, however, determine resistance to Dox. With the aim of identifying new markers associated with Dox-resistance, we found a global up-regulation of small nucleolar RNAs (snoRNAs) in human Dox-resistant osteosarcoma cells. We investigated if and how snoRNAs are linked to resistance. After RT-PCR validation of snoRNAs up-regulated in osteosarcoma cells with different degrees of resistance to Dox, we overexpressed them in Dox-sensitive cells. We then evaluated Dox cytotoxicity and changes in genes relevant for osteosarcoma pathogenesis by PCR arrays. SNORD3A, SNORA13 and SNORA28 reduced Dox-cytotoxicity when over-expressed in Dox-sensitive cells. In these cells, GADD45A and MYC were up-regulated, TOP2A was down-regulated. The same profile was detected in cells with acquired resistance to Dox. GADD45A/MYC-silencing and TOP2A-over-expression counteracted the resistance to Dox induced by snoRNAs. We reported for the first time that snoRNAs induce resistance to Dox in human osteosarcoma, by modulating the expression of genes involved in DNA damaging sensing, DNA repair, ribosome biogenesis, and proliferation. Targeting snoRNAs or down-stream genes may open new treatment perspectives in chemoresistant osteosarcomas