Efficient microservice deployment in Kubernetes multi-clusters through reinforcement learning

Microservices have revolutionized application deployment in popular cloud platforms, offering flexible scheduling of loosely-coupled containers and improving operational efficiency. However, this transition made applications more complex, consisting of tens to hundreds of microservices. Efficient orchestration remains an enormous challenge, especially with emerging paradigms such as Fog Computing and novel use cases as autonomous vehicles. Also, multi-cluster scenarios are still not vastly explored today since most literature focuses mainly on a single-cluster setup. The scheduling problem becomes significantly more challenging since the orchestrator needs to find optimal locations for each microservice while deciding whether instances are deployed altogether or placed into different clusters. This paper studies the multi-cluster orchestration challenge by proposing a Reinforcement Learning (RL)-based approach for efficient microservice deployment in Kubernetes (K8s), a widely adopted container orchestration platform. The study demonstrates the effectiveness of RL agents in achieving near-optimal allocation schemes, emphasizing latency reduction and deployment cost minimization. Additionally, the work highlights the versatility of the DeepSets neural network in optimizing microservice placement across diverse multi-cluster setups without retraining. Results show that DeepSets algorithms optimize the placement of microservices in a multi-cluster setup 32 times higher than its trained scenario

miR824-Regulated AGAMOUS-LIKE16 Contributes to Flowering Time Repression in Arabidopsis

The timing of flowering is pivotal for maximizing reproductive success under fluctuating environmental conditions. Flowering time is tightly controlled by complex genetic networks that integrate endogenous and exogenous cues, such as light, temperature, photoperiod, and hormones. Here, we show that AGAMOUS-LIKE16 (AGL16) and its negative regulator microRNA824 (miR824) control flowering time in Arabidopsis thaliana. Knockout of AGL16 effectively accelerates flowering in nonvernalized Col-FRI, in which the floral inhibitor FLOWERING LOCUS C (FLC) is strongly expressed, but shows no effect if plants are vernalized or grown in short days. Alteration of AGL16 expression levels by manipulating miR824 abundance influences the timing of flowering quantitatively, depending on the expression level and number of functional FLC alleles. The effect of AGL16 is fully dependent on the presence of FLOWERING LOCUS T (FT). Further experiments show that AGL16 can interact directly with SHORT VEGETATIVE PHASE and indirectly with FLC, two proteins that form a complex to repress expression of FT. Our data reveal that miR824 and AGL16 modulate the extent of flowering time repression in a long-day photoperiod

Towards multi-tenant cache management for ISP networks

The decreasing cost of storage and the advent of virtualization technology can allow Internet Service Providers (ISPs) to deploy multi-tenant caching infrastructures and lease them to content producers and Content Delivery Networks (CDNs). Serving content requests directly from the ISP network does not only reduce the delivery time, but also allows the ISP to optimize the network resources by controlling the placement and routing of content items. In this paper, we introduce a multi-tenant cache management approach that significantly reduces the bandwidth utilization of ISPs networks by pro-actively allocating caching space, leased by content producers and/or CDNs, and intelligently routing content to the end users. Using real content request traces, we show that the optimal solution to this problem can increase the cache hit ratio by 70.64% while reducing the bandwidth usage by 57.17% on average, compared to a commonly used reactive cache management scheme. These results provide a benchmark for the development of novel multi-tenant cache management strategies

Plasma fibrinogen: now also an antidepressant response marker?

Major depressive disorder (MDD) is one of the leading causes of global disability. It is a risk factor for noncompliance with medical treatment, with about 40% of patients not responding to currently used antidepressant drugs. The identification and clinical implementation of biomarkers that can indicate the likelihood of treatment response are needed in order to predict which patients will benefit from an antidepressant drug. While analyzing the blood plasma proteome collected from MDD patients before the initiation of antidepressant medication, we observed different fibrinogen alpha (FGA) levels between drug responders and nonresponders. These results were replicated in a second set of patients. Our findings lend further support to a recently identified association between MDD and fibrinogen levels from a large-scale study

Advances in Networking Software

The six articles in this special section focus on advancements in networking software. Networking and communications systems are currently undergoing a substantive transformation on several fronts, promising substantially lower cost, simplified operations, and dramatically faster innovation cycles as traditional barriers to the deployment of innovations are removed. Where in the past networking functions were predominantly implemented using purpose-built hardware, custom protocols, and firmware images, those networking functions are increasingly instantiated through software that is abstracted from hardware, freely programmable, and relying on algorithmic invocation of generic application programming interfaces (APIs). This transformation is best summarized as “softwarization” of the network, which is, in turn, realized through advances in networking software. These articles exemplify this transformation, providing an excellent cross-section across these facets

Improvements in data quality for decision support in intensive care

Nowadays, there is a plethora of technology in hospitals and, in particular, in intensive care units. The clinical data produced everyday can be integrated in a decision support system in real-time to improve quality of care of the critically ill patients. However, there are many sensitive aspects that must be taken into account, mainly the data quality and the integration of heterogeneous data sources. This paper presents INTCare, an Intelligent Decision Support System for Intensive Care in real-time and addresses the previous aspects, in particular, the development of an Electronic Nursing Record and the improvements in the quality of monitored data.Fundação para a Ciência e a Tecnologia (FCT