An SDN-Based Solution for Horizontal Auto-Scaling and Load Balancing of Transparent VNF Clusters

This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.

The Everyday, Circuitry, and Scalability

This chapter unpacks two concepts that lie at the heart of the book: the everyday and circuitry. In order to understand everyday peace, it seems sensible to unpack the notion of the everyday and illustrate why the hyperlocal level is relevant to how peace is embodied and enacted. The chapter defines ‘everyday peace’ and discusses the ‘local turn’ in peace and conflict studies before going on to discuss how we might see the local level in comparison with other scales such as the national, the international, the transnational, and all levels in between. It is here that the chapter uses the notion of biological and electronic circuits as a way of explaining the multi-scalar nature of peace and conflict and the messy connectivity between them. In its final substantive part, the chapter considers how everyday peace might be scaled up and thus become more significant than local-level actions and stances. Important here is the notion of scaling out, or the horizontal spread of civility. This leads us to think about how there can be multi-speed and multilevel peace.

PolyFrame

In the last few years, the field of data science has been growing rapidly as various businesses have adopted statistical and machine learning techniques to empower their decision-making and applications. Scaling data analyses to large volumes of data requires the utilization of distributed frameworks. This can lead to serious technical challenges for data analysts and reduce their productivity. AFrame, a data analytics library, is implemented as a layer on top of Apache AsterixDB, addressing these issues by providing the data scientists' familiar interface, Pandas Dataframe, and transparently scaling out the evaluation of analytical operations through a Big Data management system. While AFrame is able to leverage data management facilities (e.g., indexes and query optimization) and allows users to interact with a large volume of data, the initial version only generated SQL++ queries and only operated against AsterixDB. In this work, we describe a new design that retargets AFrame's incremental query formation to other query-based database systems, making it more flexible for deployment against other data management systems with composable query languages.

Thermally-actuated microfluidic membrane valve for point-of-care applications

Microfluidics has enabled low volume biochemistry reactions to be carried out at the point-of-care. A key component in microfluidics is the microfluidic valve. Microfluidic valves are not only useful for directing flow at intersections but also allow mixtures/dilutions to be tuned real-time and even provide peristaltic pumping capabilities. In the transition from chip-in-a-lab to lab-on-a-chip, it is essential to ensure that microfluidic valves are designed to require less peripheral equipment and that they are transportable. In this paper, a thermally-actuated microfluidic valve is presented. The valve itself is fabricated with off-the-shelf components without the need for sophisticated cleanroom techniques. It is shown that multiple valves can be controlled and operated via a power supply and an Arduino microcontroller; an important step towards transportable microfluidic devices capable of carrying out analytical assays at the point-of-care. It is been calculated that a single actuator costs less than $1, this highlights the potential of the presented valve for scaling out. The valve operation is demonstrated by adjusting the ratio of a water/dye mixture in a continuous flow microfluidic chip with Y-junction channel geometry. The power required to operate one microfluidic valve has been characterised both theoretically and experimentally. Cyclical operation of the valve has been demonstrated for 65 h with 585 actuations. The presented valve is capable of actuating rectangular microfluidic channels of 500 μm × 50 μm with an expected temperature increase of up to 5 °C. The fastest actuation times achieved were 2 s for valve closing (heating) and 9 s for valve opening (cooling).

Does the Delivery System Matter? The Scaling-Out of a School-Based Resilience Curriculum to the Social Services Sector

Background: The context is highly relevant to the implementation of new health-related programs and is an implicit or explicit part of the major implementation models in the literature. The Resilience Curriculum (RESCUR) program was developed to foster the psychosocial development of children in early and primary education. RESCUR seeks specifically to decrease children's vulnerability. It aims to promote the emotional and social learning of children who may be at risk of leaving school pre-maturely, social exclusion and mental-health problems. The program is taught using a teachers' manual to support consistency of delivery, a parents' guide, and a resource package. This study aimed to examine the scaling-out of RESCUR to social services, and specifically to test if implementation differs between the school and social services sectors.Methods: RESCUR was implemented in schools and social services in Sweden 2017–2019. Data were collected via group leaders' self-reports and observation protocols for 3 months after implementation started. There were 34 self-reports from schools, and 12 from the social services sector; 30 observation protocols were collected from schools, and 10 from social services. We examined whether there were differences in implementation outcomes (in, for example, dosage, duration, fidelity, adaptation, quality of delivery) between the two delivery systems. Descriptive statistics were prepared and non-parametric tests of significance conducted to compare implementation-related factors across the two settings.Results: Analyses of both the observation protocols and group leaders' self-reports revealed that RESCUR was well-implemented in both schools and social services. The results showed a few significant differences in the outcomes of implementation between the sectors. First, regarding observations, school staff more often adapted the pace of RESCUR lessons to ensure that the children could understand than did social services staff (p < 0.01). Second, social services staff demonstrated greater interest in students and sensitivity to the needs of individual students than did school staff (p = 0.02). Regarding self-reports, social services staff reported having delivered more (p = 0.4) and longer (p < 0.01) lessons than did school staff. Second, school staff reported greater fidelity to (p = 0.02) and less adaptation of (p < 0.01) the intervention than did social services staff. Both observations and self-reports, however, indicated a high fidelity of implementation.Conclusions: Overall, the findings suggest that the resilience program, designed for delivery in schools, can be scaled-out to social services with its implementation outcomes retained. Further research is needed to test the effectiveness of the program regarding child health-related outcomes.Clinical Trial Registration: National Institute of Health, ClinicalTrials.gov, identifier: NCT03655418. Registered August 31, 2018.