PaaS platform comparison based on users feedback

This article presents a description of the opinion study about SalesForce and ServiceNow platforms. These are modern PaaS (Platform as a Service) environments that are gaining more and more popularity. Their main common feature is the use of cloud technology in creating and maintaining internet applications. The scope of the study included such platform properties as: applicability, interface assessment, as well as the required level of knowledge of code development. For the purpose of comparing the platforms on each of them, the respondents were asked to create an identical business form. It covers different types of fields and how to validate them. The research group received full access to the test platforms. Training was conducted to introduce the respondents to a given system. The prerequisite for completing the survey was that they created a form based on a template on each of the platforms. The answers to the questions were in the form of a point scale, where the minimum value was 1 and the highest value was 5. Based on the results obtained, it was found that the SalesForce platform is a better choice, gaining more points compared to ServiceNow by about 15%

Value co-creation through APIs in Multi-sided platforms : A design science research in the E-Mobility industry

This thesis discusses various elements of value co-creation through APIs in the context of Multi-sided platforms. A design science research methodology is applied to answer the main research question of how APIs contribute to value co-creation in a Multi-sided platform environment. During the research, a model is developed that shows the impact of offering APIs to complementors, competitors and individual developers. This model is applied in the context of the E-Mobility industry. The target company, a Multi-sided platform provider that connects EV drivers with charging stations, serves as a real-world context for this thesis. During the application of the model, several artifacts are created, and the theoretical model is refined through direct feedback from business and IT professionals in the E-Mobility field

Governance of Cloud-hosted Web Applications

Cloud computing has revolutionized the way developers implement and deploy applications. By running applications on large-scale compute infrastructures and programming platforms that are remotely accessible as utility services, cloud computing provides scalability, high availability, and increased user productivity.Despite the advantages inherent to the cloud computing model, it has also given rise to several software management and maintenance issues. Specifically, cloud platforms do not enforce developer best practices, and other administrative requirements when deploying applications. Cloud platforms also do not facilitate establishing service level objectives (SLOs) on application performance, which are necessary to ensure reliable and consistent operation of applications. Moreover, cloud platforms do not provide adequate support to monitor the performance of deployed applications, and conduct root cause analysis when an application exhibits a performance anomaly.We employ governance as a methodology to address the above mentioned issues prevalent in cloud platforms. We devise novel governance solutions that achieve administrative conformance, developer best practices, and performance SLOs in the cloud via policy enforcement, SLO prediction, performance anomaly detection and root cause analysis. The proposed solutions are fully automated, and built into the cloud platforms as cloud-native features thereby precluding the application developers from having to implement similar features by themselves. We evaluate our methodology using real world cloud platforms, and show that our solutions are highly effective and efficient

Resource Allocation in Multi-analytics, Resource-Constrained Environments

The vast proliferation of monitoring and sensing devices equipped with Internet connectivity, commonly known as the Internet of Things (IoT) generates an unprecedented volume of data, which requires Big Data Analytics Systems (BDAS) to process it and extract actionable insights. The large diversity of IoT data processing applications require the deployment of multiple processing frameworks under the coordination of a resource allocator. To enable prompt actuation, these applications must meet deadlines and their processing takes place near where data is generated, in private clouds or edge computing clusters, which have limited resources.In resource-constrained and multi-analytics settings there are issues related to the combined use of open-source BDAS, originally designed for resource-rich, standalone clusters, that remain unaddressed. Specifically, open-source BDAS have unknown behavior when used combined under the coordination of a cluster-manager and the available resources are limited. Moreover, existing allocation policies are not suitable to meet deadlines in resource-constrained settings without wasting resources or requiring particular repetitive job patterns. Lastly, in such settings fair-share policies cannot reliably preserve fairness.To satisfy deadlines and achieve allocation fairness in resource constrained clusters for multi-analytics, we employ predictive resource allocation and admission control. We evaluate the performance and behavior of BDAS in resource-constrained multi-analyticsclusters and understand the root causes of their interference. Moreover, we design admission control and resource allocation suitable for resource-managers. Allocation decisions adapt to changing cluster conditions to satisfy deadlines and preserve fairness under resource-constrained multi-analytics settings. We evaluate our approach with trace-based simulations and production workloads and show that it satisfies more deadlines, preserves fairness, and utilizes the cluster more efficiently compared to existing fair-share allocators designed for resource managers

Using Workload Prediction and Federation to Increase Cloud Utilization

The wide-spread adoption of cloud computing has changed how large-scale computing infrastructure is built and managed. Infrastructure-as-a-Service (IaaS) clouds consolidate different separate workloads onto a shared platform and provide a consistent quality of service by overprovisioning capacity. This additional capacity, however, remains idle for extended periods of time and represents a drag on system efficiency.The smaller scale of private IaaS clouds compared to public clouds exacerbates overprovisioning inefficiencies as opportunities for workload consolidation in private clouds are limited. Federation and cycle harvesting capabilities from computational grids help to improve efficiency, but to date have seen only limited adoption in the cloud due to a fundamental mismatch between the usage models of grids and clouds. Computational grids provide high throughput of queued batch jobs on a best-effort basis and enforce user priorities through dynamic job preemption, while IaaS clouds provide immediate feedback to user requests and make ahead-of-time guarantees about resource availability.We present a novel method to enable workload federation across IaaS clouds that overcomes this mismatch between grid and cloud usage models and improves system efficiency while also offering availability guarantees. We develop a new method for faster-than-realtime simulation of IaaS clouds to make predictions about system utilization and leverage this method to estimate the future availability of preemptible resources in the cloud. We then use these estimates to perform careful admission control and provide ahead-of-time bounds on the preemption probability of federated jobs executing on preemptible resources. Finally, we build an end-to-end prototype that addresses practical issues of workload federation and evaluate the prototype's efficacy using real-world traces from big data and compute-intensive production workloads

Cloud Platform Support for API Governance

more cloud-like model, digital assets (code, data and software environments) increasingly require curation as web-accessible services. “Service-izing ” digital assets consists of encapsulating assets in software that exposes them to web and mobile applications via well-defined yet flexible, network accessible, application programming interfaces (APIs). In this paper, we postulate that recent advances in cloud computing make cloud platforms as-aservice (PaaS) ideal for deployment, lifecycle management, and policy-based control – i.e. API governance – for extant and future digital assets. Toward this end, we overview API governance as a PaaS technology and outline some early results generated by our investigation of a prototype we are developing, called EAGER, for implementing API governance at scale. Index Terms—API Governance; PaaS; cloud platforms; API similarity