QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope

Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in terms of fine-grained scalability and modular development, these applications are subject to suboptimal performance, resource inefficiency, and high costs to a larger degree than previous simple serverless functions. We present Aquatope, a QoS-and-uncertainty-aware resource scheduler for end-to-end serverless workflows that takes into account the inherent uncertainty present in FaaS platforms, and improves performance predictability and resource efficiency. Aquatope uses a set of scalable and validated Bayesian models to create pre-warmed containers ahead of function invocations, and to allocate appropriate resources at function granularity to meet a complex workflow's end-to-end QoS, while minimizing resource cost. Across a diverse set of analytics and interactive multi-stage serverless workloads, Aquatope significantly outperforms prior systems, reducing QoS violations by 5x, and cost by 34% on average and up to 52% compared to other QoS-meeting methods

Analytically-Driven Resource Management for Cloud-Native Microservices

Resource management for cloud-native microservices has attracted a lot of recent attention. Previous work has shown that machine learning (ML)-driven approaches outperform traditional techniques, such as autoscaling, in terms of both SLA maintenance and resource efficiency. However, ML-driven approaches also face challenges including lengthy data collection processes and limited scalability. We present Ursa, a lightweight resource management system for cloud-native microservices that addresses these challenges. Ursa uses an analytical model that decomposes the end-to-end SLA into per-service SLA, and maps per-service SLA to individual resource allocations per microservice tier. To speed up the exploration process and avoid prolonged SLA violations, Ursa explores each microservice individually, and swiftly stops exploration if latency exceeds its SLA. We evaluate Ursa on a set of representative and end-to-end microservice topologies, including a social network, media service and video processing pipeline, each consisting of multiple classes and priorities of requests with different SLAs, and compare it against two representative ML-driven systems, Sinan and Firm. Compared to these ML-driven approaches, Ursa provides significant advantages: It shortens the data collection process by more than 128x, and its control plane is 43x faster than ML-driven approaches. At the same time, Ursa does not sacrifice resource efficiency or SLAs. During online deployment, Ursa reduces the SLA violation rate by 9.0% up to 49.9%, and reduces CPU allocation by up to 86.2% compared to ML-driven approaches

What Would You Do?

Table of Contents: Trapeze Artist; The People We Never Meet; Wendy; The Creek; PlatonicBachelor of Science in Public Healt

Guanylyl cyclase activation reverses resistive breathing–induced lung injury and inflammation

Inspiratory resistive breathing (RB), encountered in obstructive lung diseases, induces lung injury. The soluble guanylyl cyclase (sGC)/cyclic guanosine monophosphate (cGMP) pathway is down-regulated in chronic and acute animal models of RB, such as asthma, chronic obstructive pulmonary disease, and in endotoxin-induced acute lung injury. Our objectives were to: (1) characterize the effects of increased concurrent inspiratory and expiratory resistance in mice via tracheal banding; and (2) investigate the contribution of the sGC/cGMP pathway in RB-induced lung injury. Anesthetized C57BL/6 mice underwent RB achieved by restricting tracheal surface area to 50% (tracheal banding). RB for 24 hours resulted in increased bronchoalveolar lavage fluid cellularity and protein content, marked leukocyte infiltration in the lungs, and perturbed respiratory mechanics (increased tissue resistance and elasticity, shifted static pressure–volume curve right and downwards, decreased static compliance), consistent with the presence of acute lung injury. RB down-regulated sGC expression in the lung. All manifestations of lung injury caused by RB were exacerbated by the administration of the sGC inhibitor, 1H-[1,2,4]oxodiazolo[4,3-]quinoxalin-l-one, or when RB was performed using sGCα1 knockout mice. Conversely, restoration of sGC signaling by prior administration of the sGC activator BAY 58-2667 (Bayer, Leverkusen, Germany) prevented RB-induced lung injury. Strikingly, direct pharmacological activation of sGC with BAY 58-2667 24 hours after RB reversed, within 6 hours, the established lung injury. These findings raise the possibility that pharmacological targeting of the sGC–cGMP axis could be used to ameliorate lung dysfunction in obstructive lung diseases

Differential effects on BAFF and APRIL levels in rituximab-treated patients with systemic lupus erythematosus and rheumatoid arthritis

The objective of this study was to investigate the interaction between levels of BAFF (B-cell activation factor of the tumour necrosis factor [TNF] family) and APRIL (a proliferation-inducing ligand) and B-cell frequencies in patients with systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) treated with the B-cell-depleting agent rituximab. Ten patients with SLE were treated with rituximab in combination with cyclophosphamide and corticosteroids. They were followed longitudinally up to 6 months after B-cell repopulation. Nine patients with RA, resistant or intolerant to anti-TNF therapy, treated with rituximab plus methotrexate were investigated up to 6 months after treatment. The B-cell frequency was determined by flow cytometry, and serum levels of BAFF and APRIL were measured by enzyme-linked immunosorbent assays. BAFF levels rose significantly during B-cell depletion in both patient groups, and in patients with SLE the BAFF levels declined close to pre-treatment levels upon B-cell repopulation. Patients with SLE had normal levels of APRIL at baseline, and during depletion there was a significant decrease. In contrast, patients with RA had APRIL levels 10-fold higher than normal, which did not change during depletion. At baseline, correlations between levels of B cells and APRIL, and DAS28 (disease activity score using 28 joint counts) and BAFF were observed in patients with RA. In summary, increased BAFF levels were observed during absence of circulating B cells in our SLE and RA patient cohorts. In spite of the limited number of patients, our data suggest that BAFF and APRIL are differentially regulated in different autoimmune diseases and, in addition, differently affected by rituximab treatment

Synthetic RNA modules for fine-tuning gene expression levels in yeast by modulating RNase III activity

The design of synthetic gene networks requires an extensive genetic toolbox to control the activities and levels of protein components to achieve desired cellular functions. Recently, a novel class of RNA-based control modules, which act through post-transcriptional processing of transcripts by directed RNase III (Rnt1p) cleavage, were shown to provide predictable control over gene expression and unique properties for manipulating biological networks. Here, we increase the regulatory range of the Rnt1p control elements, by modifying a critical region for enzyme binding to its hairpin substrates, the binding stability box (BSB). We used a high throughput, cell-based selection strategy to screen a BSB library for sequences that exhibit low fluorescence and thus high Rnt1p processing efficiencies. Sixteen unique BSBs were identified that cover a range of protein expression levels, due to the ability of the sequences to affect the hairpin cleavage rate and to form active cleavable complexes with Rnt1p. We further demonstrated that the activity of synthetic Rnt1p hairpins can be rationally programmed by combining the synthetic BSBs with a set of sequences located within a different region of the hairpin that directly modulate cleavage rates, providing a modular assembly strategy for this class of RNA-based control elements