Auswahl der wissenschaftlichen Literatur zum Thema „Azure Policy“

Cloud computing providers face the problem of matching heterogeneous customer workloads to resources that will serve them. This is particularly challenging if customers, who are already running a job on a cluster, scale their resource usage up and down over time. The provider therefore has to continuously decide whether she can add additional workloads to a given cluster or if doing so would impact existing workloads’ ability to scale. Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low efficiency (i.e., low average utilization of the cluster). We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization. We first introduce the cluster admission problem and formalize it as a constrained Partially Observable Markov Decision Process (POMDP). As it is infeasible to solve the POMDP optimally, we then systematically design admission policies that estimate moments of each workload’s distribution of future resource usage. Via extensive simulations grounded in a trace from Microsoft Azure, we show that our admission policies lead to a substantial improvement over the simple threshold policy. We then show that substantial further gains are possible if high-quality information is available about arriving workloads. Based on this, we propose an information elicitation approach to incentivize users to provide this information and simulate its effects.

Abstract The wide adoption of cloud computing by businesses is due to several reasons, among which the elasticity of the cloud virtual infrastructure is the definite leader. Container technology allows increasing the flexibility of an application by adding another layer of virtualization. The containers can be dynamically created and terminated, and also moved from one host to another. A company can achieve a significant cost reduction and increase the manageability of its applications by allowing the running of containerized microservice applications in the cloud. Scaling for such solutions is conducted on both the virtual infrastructure layer and the container layer. Scaling on both layers needs to be synchronized so that, for example, the virtual machine is not terminated with containers still running on it. The synchronization between layers is enabled by multilayered cooperative scaling, implying that the autoscaling solution of the virtual infrastructure layers is aware of the decisions of the autoscaling solution on the container layer and vice versa. In this paper, we introduce the notion of cooperative multilayered scaling and the performance of multilayered autoscaling solutions evaluated using the approach implemented in ScaleX (previously known as Autoscaling Performance Measurement Tool, APMT). We provide the results of the experimental evaluation of multilayered autoscaling performance for the combination of virtual infrastructure autoscaling of AWS, Microsoft Azure and Google Compute Engine with pods horizontal autoscaling of Kubernetes by using ScaleX with four distinct load patterns. We also discuss the effect of the Docker container image size and its pulling policy on the scaling performance.

The residents of Furukawa-cho, Hida City, Gifu Prefecture, Japan use cooperative learning during festival organization to preserve the various traditional arts of Hida Furukawa Festival, such as Hayashi. The goals of this study were to (1) analyze the aspects of cooperative learning of Seiryu-tai Hayashi learners involved in the Furukawa Festival, and (2) determine the effects of factors influencing cooperative learning on the aspects of cooperative learning among these learners. We applied grounded theory and conducted a field study on the six years of the Furukawa Festival and residents’ daily lives since 2014. We numbered, coded, and categorized text data, and classified the data using the KJ method. We applied the five elements of cooperative learning by Johnson and Johnson, and analyzed the cooperative learning processing of Seiryu-tai, considering five aspects: (1) cultivation and skills: to follow the norms of Hayashi learning and then cultivate the skills; (2) demonstration and imitation: the process of Hayashi learning without notation; (3) instruction and accompanying: elders addressed norms and demonstrated skills, providing comfort and encouragement; (4) experience and feeling: interacting with unqualified learners to experience the Hayashi atmosphere, and (5) others and interaction: experience of a different culture. From the analyses, we found that the five elements of cooperative learning in Hayashi cooperative learning provide new functions. We applied the five elements of cooperative learning to analyze the five aspects of Azure Dragon Stall’s Hayashi cooperative learning: (1) positive interdependence: deep interdependence exists in the core of the learning circle, and guarding and immersion are included; (2) individual accountability: achievement of following the norms, enhancing skills, and having a tacit agreement on the instrumental ensemble; (3) promotive interaction: five interactive types are influenced, including people who follow etiquette, people who need to be advised, people who imitate, people with tolerance, and people who interact; (4) the division of social skills into four types including playing, performing, senior, and foreign, and (5) group processing, which includes learners, instructors, senior learners, elders, youths, and foreign visitors, who jointly form the Hayashi learning circle.

We are happy to present this special issue of the scientific journal Scalable Computing: Practice and Experience. In this special issue on Infrastructures and Algorithms for Scalable Computing (Volume 19, No 3 June 2018), we have selected four papers out of submitted nine, which gone through a peer review according to the journal policy. All papers represent novel results in the fields of distributed algorithms and infrastructures for scalable computing. The first paper presents present a novel approach for efficient data placement, which improves the performance of workflow execution in distributed datacenters. The greedy heuristic algorithm, which is based on a network flow optimization framework, minimizes the total storage cost, including efforts to move and store the data from different source locations and dependencies. The second paper evaluated the significance of different clustering techniques viz. k-means, Hierarchical Agglomerative Clustering and Markov Clustering in groupingawaredata placement for data-intensive applications with interest locality. The evaluation in Azure reported that Markov Clustering-based data placement strategy improves the local map execution and reduces the execution time compared to Hadoops Default Data Placement Strategy and other evaluated clustering techniques. This is more emphasized for data-intensive applications that have interest locality. The third paper presents an experimental evaluation of the openMP thread-mapping strategies in different hardware environments (IntelXeon Phi coprocessor and hybrid CPU-MIC platforms). The paper shows the optimal choice of thread affinity, the number of threads and the execution mode that can provide optimal performance of the LU factorization. In the fourth paper, the authors study the amount of memory occupied by sparse matrices split up into same-size blocks. The paper considers and statistically evaluates four popular storage formats and combinations among them. The conclusion is that block-based storage formats may significantly reduce memory footprints of sparse matrices arising from a wide range of application domains. We use this opportunity to thank all contributors to this Special Issue: all authors who submitted the results of their latest research and all reviewers for their valuable comments and suggestions for improvement. We would like to express our special gratitude for the Editor-in-Chief, Professor Dana Petcu, for her constant support during the whole process of this Special Issue.

Objective To assess the effectiveness of the current cervical cancer screening tools in Western Kazakhstan. Methods Smears taken through (i) conventional cytology using azur-eosin staining and (ii) liquid-based cytology (LBC) ‘Cell Scan’ in the general female population and in women first diagnosed with cervical cancer were collected throughout the region. ROC-analysis with curve construction and weighted Cohen's κ calculation were applied. A total of 494 cytological pairs were collected, including 94 sets with histology findings. Results The conventional (azur-eosin staining) technique contained 0.2% non-informative material and LBC ‘Cell Scan’ had 5.9%. Area under the curve was 0.95 for the conventional technique and 0.92 for ‘Cell Scan’ ( p > 0.05). The conventional smears showed κ 0.62, sensitivity 90.4% at specificity 90.0% for CIN2+, while LBC ‘Cell Scan’ smears showed κ 0.47, sensitivity 83.3% at specificity 92.5%. Conclusions In this analysis it was not possible to prove that the LBC ‘Cell Scan’ technique was superior to its predecessor, azur-eosin staining. These findings highlight the need to modify the current screening programme according to updated international scientific evidence on effective screening design, such as the use of HPV DNA testing with Pap smear triage in women aged 30 or older. Further research, and a Health Technology Assessment, are necessary if we wish to establish a national standardized screening programme using the available technology appropriately.

Since 2000, Morocco has launched important institutional and economic reforms based on strategies to correct market failures in important sectors of the economy. These strategies have included tax exemptions and other financial incentives, facilitating access to land and simplifying administrative procedures, and launching major public infrastructure projects, all of which have combined to create a new investment dynamic in strategic sectors such as agriculture, industry, and energy. These efforts have affected many sectors: the Azur Vision 2020 plan for tourism, the Green Morocco Plan for agriculture, the Halieutis 2020 Plan for fishing, Maroc Plus Export for export, the Emergence Plan (2005) for industry followed by the National Pact for Industrial Emergence, 2009-2015 and the new Industrial Acceleration Plan, 2014-2020. For example, to support the new Industrial Acceleration Plan, the government has provided a grant, a financial support of about 2% of GDP over 6 years. The government has also offered ad hoc support to attract foreign investors in large private projects likely to generate significant positive externalities. One example is the project to set up a Renault Company plant in Tangiers, which aims to produce and export 400,000 cars per year (World Bank, 2017).This note highlights the industrial sectors that can offer the best potential for growth, attract private investors and contribute to the creation of decent jobs. It will also identify ways to accelerate investment in Morocco. These industrial investments may include purely private investments or those made with the support of international financial institutions. The note analyzes investment and sectoral integration opportunities using the input model (outputs and employment elasticity indicators), the evaluation of the industrial acceleration plan, and sets out the measures taken by the CVE to promote VSEs. Only one classic sector is selected is highlighted for their investment potential and their commitment to reform in the sectors. It is the food industry.The note follows the diagram below:1) An introduction to the economic, political and social frameworks, including key indicators 2) A sector analysis to identify one to three sectors that are likely to be the subject of accelerated investment and in which reforms will have a particularly high chance of improving the business and investment climate and having an impact on development. 3) A mapping of the most relevant initiatives, technical assistance, grants and loans, and investments by donors and other stakeholders, such as development finance institutions. In the selected sector(s).4) An analysis of the existing reform agenda in the respective sector(s) and the identification of areas where there is a high likelihood of effective cooperation to support the implementation of these reforms.

Introduction This article explores the changing relational quality of “the shadow of hierarchy”, in the context of the merging of platforms with infrastructure as the source of the shadow of hierarchy. In governance and regulatory studies, the shadow of hierarchy (or variations thereof), describes the space of influence that hierarchal organisations and infrastructures have (Héritier and Lehmkuhl; Lance et al.). A shift in who/what casts the shadow of hierarchy will necessarily result in changes to the attendant relational values, logics, and (techno)socialities that constitute the shadow, and a new arrangement of shadow that presents new challenges and opportunities. This article reflects on relevant literature to consider two different ways the shadow of hierarchy has qualitatively changed as platforms, rather than infrastructures, come to cast the shadow of hierarchy – an increase in scalability; and new socio-technical arrangements of (non)participation – and the opportunities and challenges therein. The article concludes that more concerted efforts are needed to design the shadow, given a seemingly directionless desire to enact data-driven solutions. The Shadow of Hierarchy, Infrastructures, and Platforms The shadow of hierarchy refers to how institutional, infrastructural, and organisational hierarchies create a relational zone of influence over a particular space. This commonly refers to executive decisions and legislation created by nation states, which are cast over private and non-governmental actors (Héritier and Lehmkuhl, 2). Lance et al. (252–53) argue that the shadow of hierarchy is a productive and desirable thing. Exploring the shadow of hierarchy in the context of how geospatial data agencies govern their data, Lance et al. find that the shadow of hierarchy enables the networked governance approaches that agencies adopt. This is because operating in the shadow of institutions provides authority, confers bureaucratic legitimacy and top-down power, and offers financial support. The darkness of the shadow is thus less a moral or ethicopolitical statement (such as that suggested by Fisher and Bolter, who use the idea of darkness to unpack the morality of tourism involving death and human suffering), and instead a relationality; an expression of differing values, logics, and (techno)socialities internal and external to those infrastructures and institutions that cast it (Gehl and McKelvey). The shadow of hierarchy might therefore be thought of as a field of relational influences and power that a social body casts over society, by virtue of a privileged position vis-a-vis society. It modulates society’s “light”; the resources (Bourdieu) and power relationships (Foucault) that run through social life, as parsed through a certain institutional and infrastructural worldview (the thing that blocks the light to create the shadow). In this way the shadow of hierarchy is not a field of absolute blackness that obscures, but instead a gradient of light and dark that creates certain effects. The shadow of hierarchy is now, however, also being cast by decentralised, privately held, and non-hierarchal platforms that are replacing or merging with public infrastructure, creating new social effects. Platforms are digital, socio-technical systems that create relationships between different entities. They are most commonly built around a relatively fixed core function (such as a social media service like Facebook), that then interacts with a peripheral set of complementors (advertising companies and app developers in the case of social media; Baldwin and Woodard), to create new relationships, forms of value, and other interactions (van Dijck, The Culture of Connectivity). In creating these relationships, platforms become inherently political (Gillespie), shaping relationships and content on the platform (Suzor) and in embodied life (Ajunwa; Eubanks). While platforms are often associated with optional consumer platforms (such as streaming services like Spotify), they have increasingly come to occupy the place of public infrastructure, and act as a powerful enabler to different socio-technical, economic, and political relationships (van Dijck, Governing Digital Societies). For instance, Plantin et al. argue that platforms have merged with infrastructures, and that once publicly held and funded institutions and essential services now share many characteristics with for-profit, privately held platforms. For example, Australia has had a long history of outsourcing employment services (Webster and Harding), and nearly privatised its entire visa processing data infrastructure (Jenkins). Platforms therefore have a greater role in casting the shadow of hierarchy than before. In doing so, they cast a shadow that is qualitatively different, modulated through a different set of relational values and (techno)socialities. Scalability A key difference and selling point of platforms is their scalability; since they can rapidly and easily up- and down-scale their functionalities in a way that traditional infrastructure cannot (Plantin et al.). The ability to respond “on-demand” to infrastructural requirements has made platforms the go-to service delivery option in the neo-liberalised public infrastructure environment (van Dijck, Governing Digital Societies). For instance, services providers like Amazon Web Services or Microsoft Azure provide on demand computing capacity for many nations’ most valuable services, including their intelligence and security capabilities (Amoore, Cloud Ethics; Konkel). The value of such platforms to government lies in the reduced cost and risk that comes with using rented capabilities, and the enhanced flexibility to increase or decrease their usage as required, without any of the economic sunk costs attached to owning the infrastructure. Scalability is, however, not just about on-demand technical capability, but about how platforms can change the scale of socio-technical relationships and services that are mediated through the platform. This changes the relational quality of the shadow of hierarchy, as activities and services occurring within the shadow are now connected into a larger and rapidly modulating scale. Scalability allows the shadow of hierarchy to extend from those in proximity to institutions to the broader population in general. For example, individual citizens can more easily “reach up” into governmental services and agencies as a part of completing their everyday business through platform such as MyGov in Australia (Services Australia). Using a smartphone application, citizens are afforded a more personalised and adaptive experience of the welfare state, as engaging with welfare services is no-longer tied to specific “brick-and-mortar” locations, but constantly available through a smartphone app and web portal. Multiple government services including healthcare and taxation are also connected to this platform, allowing users to reach across multiple government service domains to complete their personal business, seeking information and services that would have once required separate communications with different branches of government. The individual’s capacities to engage with the state have therefore upscaled with this change in the shadow, retaining a productivity and capacity enhancing quality that is reminiscent of older infrastructures and institutions, as the individual and their lived context is brought closer to the institutions themselves. Scale, however, comes with complications. The fundamental driver for scalability and its adaptive qualities is datafication. This means individuals and organisations are inflecting their operational and relational logics with the logic of datafication: a need to capture all data, at all times (van Dijck, Datafication; Fourcade and Healy). Platforms, especially privately held platforms, benefit significantly from this, as they rely on data to drive and refine their algorithmic tools, and ultimately create actionable intelligence that benefits their operations. Thus, scalability allows platforms to better “reach down” into individual lives and different social domains to fuel their operations. For example, as public transport services become increasingly datafied into mobility-as-a-service (MAAS) systems, ride sharing and on-demand transportation platforms like Uber and Lyft become incorporated into the public transport ecosystem (Lyons et al.). These platforms capture geospatial, behavioural, and reputational data from users and drivers during their interactions with the platform (Rosenblat and Stark; Attoh et al.). This generates additional value, and profits, for the platform itself with limited value returned to the user or the broader public it supports, outside of the transport service. It also places the platform in a position to gain wider access to the population and their data, by virtue of operating as a part of a public service. In this way the shadow of hierarchy may exacerbate inequity. The (dis)benefits of the shadow of hierarchy become unevenly spread amongst actors within its field, a function of an increased scalability that connects individuals into much broader assemblages of datafication. For Eubank, this can entrench existing economic and social inequalities by forcing those in need to engage with digitally mediated welfare systems that rely on distant and opaque computational judgements. Local services are subject to increased digital surveillance, a removal of agency from frontline advocates, and algorithmic judgement at scale. More fortunate citizens are also still at risk, with Nardi and Ekbia arguing that many digitally scaled relationships are examples of “heteromation”, whereby platforms convince actors in the platform to labour for free, such as through providing ratings which establish a platform’s reputational economy. Such labour fuels the operation of the platform through exploiting users, who become both a product/resource (as a source of data for third party advertisers) and a performer of unrewarded digital labour, such as through providing user reviews that help guide a platform’s algorithm(s). Both these examples represent a particularly disconcerting outcome for the shadow of hierarchy, which has its roots in public sector institutions who operate for a common good through shared and publicly held infrastructure. In shifting towards platforms, especially privately held platforms, value is transmitted to private corporations and not the public or the commons, as was the case with traditional infrastructure. The public also comes to own the risks attached to platforms if they become tied to public services, placing a further burden on the public if the platform fails, while reaping none of the profit and value generated through datafication. This is a poor bargain at best. (Non)Participation Scalability forms the basis for a further predicament: a changing socio-technical dynamic of (non)participation between individuals and services. According to Star (118), infrastructures are defined through their relationships to a given context. These relationships, which often exist as boundary objects between different communities, are “loosely structured in common use, and become tightly bound in particular locations” (Star, 118). While platforms are certainly boundary objects and relationally defined, the affordances of cloud computing have enabled a decoupling from physical location, and the operation of platforms across time and space through distributed digital nodes (smartphones, computers, and other localised hardware) and powerful algorithms that sort and process requests for service. This does not mean location is not important for the cloud (see Amoore, Cloud Geographies), but platforms are less likely to have a physically co-located presence in the same way traditional infrastructures had. Without the same institutional and infrastructural footprint, the modality for participating in and with the shadow of hierarchy that platforms cast becomes qualitatively different and predicated on digital intermediaries. Replacing a physical and human footprint with algorithmically supported and decentralised computing power allows scalability and some efficiency improvements, but it also removes taken-for-granted touchpoints for contestation and recourse. For example, ride-sharing platform Uber operates globally, and has expressed interest in operating in complement to (and perhaps in competition with) public transport services in some cities (Hall et al.; Conger). Given that Uber would come to operate as a part of the shadow of hierarchy that transport authorities cast over said cities, it would not be unreasonable to expect Uber to be subject to comparable advocacy, adjudication, transparency, and complaint-handling requirements. Unfortunately, it is unclear if this would be the case, with examples suggesting that Uber would use the scalability of its platform to avoid these mechanisms. This is revealed by ongoing legal action launched by concerned Uber drivers in the United Kingdom, who have sought access to the profiling data that Uber uses to manage and monitor its drivers (Sawers). The challenge has relied on transnational law (the European Union’s General Data Protection Regulation), with UK-based drivers lodging claims in Amsterdam to initiate the challenge. Such costly and complex actions are beyond the means of many, but demonstrate how reasonable participation in socio-technical and governance relationships (like contestations) might become limited, depending on how the shadow of hierarchy changes with the incorporation of platforms. Even if legal challenges for transparency are successful, they may not produce meaningful change. For instance, O’Neil links algorithmic bias to mathematical shortcomings in the variables used to measure the world; in the creation of irritational feedback loops based on incorrect data; and in the use of unsound data analysis techniques. These three factors contribute to inequitable digital metrics like predictive policing algorithms that disproportionately target racial minorities. Large amounts of selective data on minorities create myopic algorithms that direct police to target minorities, creating more selective data that reinforces the spurious model. These biases, however, are persistently inaccessible, and even when visible are often unintelligible to experts (Ananny and Crawford). The visibility of the technical “installed base” that support institutions and public services is therefore not a panacea, especially when the installed base (un)intentionally obfuscates participation in meaningful engagement like complaints handling. A negative outcome is, however, also not an inevitable thing. It is entirely possible to design platforms to allow individual users to scale up and have opportunities for enhanced participation. For instance, eGovernance and mobile governance literature have explored how citizens engage with state services at scale (Thomas and Streib; Foth et al.), and the open government movement has demonstrated the effectiveness of open data in understanding government operations (Barns; Janssen et al.), although these both have their challenges (Chadwick; Dawes). It is not a fantasy to imagine alternative configurations of the shadow of hierarchy that allow more participatory relationships. Open data could facilitate the governance of platforms at scale (Box et al.), where users are enfranchised into a platform by some form of membership right and given access to financial and governance records, in the same way that corporate shareholders are enfranchised, facilitated by the same app that provides a service. This could also be extended to decision making through voting and polling functions. Such a governance form would require radically different legal, business, and institutional structures to create and enforce this arrangement. Delacoix and Lawrence, for instance, suggest that data trusts, where a trustee is assigned legal and fiduciary responsibility to achieve maximum benefit for a specific group’s data, can be used to negotiate legal and governance relationships that meaningfully benefit the users of the trust. Trustees can be instructed to only share data to services whose algorithms are regularly audited for bias and provide datasets that are accurate representations of their users, for instance, avoiding erroneous proxies that disrupt algorithmic models. While these developments are in their infancy, it is not unreasonable to reflect on such endeavours now, as the technologies to achieve these are already in use. Conclusions There is a persistent myth that data will yield better, faster, more complete results in whatever field it is applied (Lee and Cook; Fourcade and Healy; Mayer-Schönberger and Cukier; Kitchin). This myth has led to data-driven assemblages, including artificial intelligence, platforms, surveillance, and other data-technologies, being deployed throughout social life. The public sector is no exception to this, but the deployment of any technological solution within the traditional institutions of the shadow of hierarchy is fraught with challenges, and often results in failure or unintended consequences (Henman). The complexity of these systems combined with time, budgetary, and political pressures can create a contested environment. It is this environment that moulds societies' light and resources to cast the shadow of hierarchy. Relationality within a shadow of hierarchy that reflects the complicated and competing interests of platforms is likely to present a range of unintended social consequences that are inherently emergent because they are entering into a complex system – society – that is extremely hard to model. The relational qualities of the shadow of hierarchy are therefore now more multidimensional and emergent, and experiences relating to socio-technical features like scale, and as a follow-on (non)participation, are evidence of this. Yet by being emergent, they are also directionless, a product of complex systems rather than designed and strategic intent. This is not an inherently bad thing, but given the potential for data-system and platforms to have negative or unintended consequences, it is worth considering whether remaining directionless is the best outcome. There are many examples of data-driven systems in healthcare (Obermeyer et al.), welfare (Eubanks; Henman and Marston), and economics (MacKenzie), having unintended and negative social consequences. Appropriately guiding the design and deployment of theses system also represents a growing body of knowledge and practical endeavour (Jirotka et al.; Stilgoe et al.). Armed with the knowledge of these social implications, constructing an appropriate social architecture (Box and Lemon; Box et al.) around the platforms and data systems that form the shadow of hierarchy should be encouraged. This social architecture should account for the affordances and emergent potentials of a complex social, institutional, economic, political, and technical environment, and should assist in guiding the shadow of hierarchy away from egregious challenges and towards meaningful opportunities. To be directionless is an opportunity to take a new direction. The intersection of platforms with public institutions and infrastructures has moulded society’s light into an evolving and emergent shadow of hierarchy over many domains. With the scale of the shadow changing, and shaping participation, who benefits and who loses out in the shadow of hierarchy is also changing. Equipped with insights into this change, we should not hesitate to shape this change, creating or preserving relationalities that offer the best outcomes. Defining, understanding, and practically implementing what the “best” outcome(s) are would be a valuable next step in this endeavour, and should prompt considerable discussion. 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Cloud technologies are spearheading today’s innovation and automation efforts. With the use of Azure Policy it is possible to govern Azure resources and services through customized Azure policy definitions. This project aims to simplify the creation process for the Azure policy definition by creating a web application that removes the need to construct the JSON structure. Instead of code-based, it uses click&add to create the code blocks. The web application was created through the library React and with the use of Typescript as the main programming language. The result is a web application that removes the need to construct the JSON structure and instead produces this JSON structure for the user. The project concludes that the result web application did not fully achieve simplification through click&add. However, it creates a good basis for further development of policy simplification. In its current form, the app can be viewed as an alternative method for creating an Azure policy definition.
Molnteknologierna står i spetsen för dagens innovations- och automatiseringsinsatser. Med användning av Azure Policy är det möjligt att styra Azure-resurser och tjänster genom anpassade Azure-policy definitioner. Detta projekt syftar till att förenkla skapandeprocessen fär Azure-policy definitionen genom att skapa en webbapplikation som tar bort behovet av att konstruera JSON struktur. Istället för att strukturera JSON via kod använder den click&add för att skapa kodblocken. Webapplikationen skapades genom biblioteket React med programmeringsspråket TypeScript. Resultatet är en webbapplikation som tar bort behovet av att konstruera JSON strukturen och istället producerar den här JSON-strukturen för användaren. Projektet drar slutsatsen att resultatapplikationen inte helt nådde förenkling genom användning av klicka&lägg till. Det skapar dock en bra grund för vidareutveckling av förenkling för Azure-princip definition. I sin nuvarande form kan appen ses som en alternativ metod för att skapa en Azure-princip definition.