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5G realizes an impactful convergence, where Network Functions Virtualization (NFV) and cloud-native models become fundamental for profiting from the unprecedented capacity offered at the 5G Radio Access Network (RAN). For providing scalability and automation management over resources in 5G infrastructure, cloud-native and Platform as a service (PaaS) are proposed as solutions for paving the way for vertical applications in 5G. This paper leverages cloud-native models, PaaS, and virtual testbed instances to provide key platform provisioning and service life-cycle management features to a selected User Generated Content (UGC) scenario in multimedia applications. Specifically, this article and results show how service-level telemetry from a UGC cloud-native application is used to automatically scale system resources across the NFV infrastructure., Previous version of the paper is accepted in IEEE Future Networks World Forum (FNWF), Montreal, 2022

Network platform as a Service (NPaaS) is an emerging concept in the telecommunications industry, in which services are deployed across a multitude of communication service provider (CSP) networks. In addition to connectivity requirements, these services will have associated and varied security and performance requirements. While such requirements must be verified across multiple CSP networks, CSPs will be selective about the degree of network information and guarantees they are willing to provide to NPaaS providers, application service providers, and other CSPs. Furthermore, all such providers are likely to be wary about network information or guarantees provided by CSPs with whom they do not have trust relationships. In this short paper, we present some challenges facing NPaaS security and performance, and demonstrate through an example case study how recent formal network verification techniques and the open-source network verification toolset NV can be used as a framework to verify security and performance requirements in an NPaaS context.

Cloud computing has transformed the way IT services are provisioned and consumed, shifting the burden of configuration and management to the cloud provider. A lot of research has been conducted in the field aimed at optimizing and/or devise new in-cloud service delivery models. Yet, service outages are a norm rather than an exception. A natural evolution for application developers, especially for those applications that must meet high availability requirements, is to rely on resources and services provisioned by multiple cloud simultaneously. The so-called multi-cloud, distributed deployment model coupled with a monitoring solution spanning multiple domains could help in a timely identification and isolation of faulty components, alleviating service impairment issues. To this end, we propose an extension to NoMISHAP, a Platform as a Service (PaaS) multi-cloud middleware, introducing a cross layer monitoring solution for use in distributed cloud environments. Experimental results show the effectiveness of our approach and its ease of adoption for management tasks.

Academic researchers who are performing computational-heavy research may be at a disadvantage if their available systems are not suitable enough or may take a lot of time to produce a result set. Some academic institutions might not have the necessary resources to address the research needs. In this research, we extend our initial concept of clustering Raspberry Pi systems to investigate the viability and requirements needed to utilise school computers to create a Platform as a Service that can serve the needs for undergraduate researchers. A computer cluster utilising twenty-five school mid-level computers was created with a novel quality of service offering and bench-marked for performance while also creating a platform for students to be able to submit multiple neural network models to train them concurrently by utilising Distributed TensorFlow. It was concluded that low-to-medium end computers could be used to create a computer cluster for research purposes, yet a number of factors need to be taken into consideration.

Many organizations, driven by the need for greater productivity and lower costs, are moving their portfolio of applications to the cloud. However, there are many challenges in migrating an application to the cloud. That is, there are different aspects of an application that affect its cloud-migration feasibility. That is why assessing the feasibility of migrating an application to the cloud is not an easy task. Also, it is non-trivial to map which cloud solution - whether infrastructure as a service, platform as a service, or software as a service, should be used for hosting the application, if it is shown to be cloud-migration feasible. In this paper, we present a novel cloud-migration feasibility advisor that analyzes the meta data received about an application and evaluates whether it is feasible to move it to the cloud or not. In addition, the advisor uses dependency graphs and the aforementioned meta data to classify which cloud solution would suit best for the application, if it is deemed migration feasible.

Security is one of the key considerations when businesses look to adopt cloud models like Platform as a Service (PaaS). The main concern comes from the fact that business losses total control of deployed applications and data, leaving that trust in the hands of the Cloud Service Providers (CSP). Although CSPs put some level of security controls in place, it is ultimately the responsibility of the client to verify the presence of the controls and verify the suitability of their security needs. It is also the user's responsibility to ascertain compliance with these offerings to appropriate standards before patronage. This paper reviewed various security issues inherent in the PaaS cloud model, classified them according to the essential cloud characteristics and finally recommended high-level solutions to the identified security issues. The result of the review and recommendations provided can serve as a reference for organizations and individual users when deciding security needs and service level objectives for PaaS based deployments.

The adoption of Hybrid cloud in enterprises has grown rapidly every year. Hybrid cloud solution implements the better of Private and Public cloud while proving that running of multiple application in different environment to provide segregation of personal useful data into a private environment from the raw meta data to reside on the cost-effective public cloud. This paper covers the deployment of hybrid cloud application with the use of Aneka PaaS (Platform as a Service) computing and Amazon Web Services Elastic Cloud Computing (AWS EC2) cloud service. The deployed application is automated using Orchestra- tor- an automation tool that allows EC2 resources to be deployed that enables uninterrupted running off application without having to take down the application during the situations of low compute resources. Aneka allows for the monitoring of resources to allow Optimization and billing for respective on demand resource usage. The proposed Orchestrator flow algorithm allows the application to load balance computing resources between the private and public cloud.

Know Your Customer (KYC) is done as a mandatory entry step into any financial institution. However, even today the amount of manual intervention involved in the process is staggering. Often the data is centrally stored, and the computer programs acting are also centrally governed thus are not tamperproof making them susceptible to vulnerabilities and attacks. Different organizations do not have a unified application where the KYC information can be seamlessly shared between them without any risk of repudiation from any of the participating organizations. Our application, that is based on blockchain technology, aims to provide this platform as a service to financial institutions as an electronic-KYC solution, in the process of making the life of the end consumer easier.

The majority of cloud computing deployment environments follow the typical service delivery models, i.e., Software as a Service (SaaS), Platform as a Service (PaaS) and the Infrastructure as a Service (IaaS), that provide specific functionalities to users depending on the service delivery layer and clear resource isolation among different tenants. However, in certain scenarios, such as the Cultural heritage one, it is often required to provide SaaS, IaaS and PaaS cross-functionality support to users and cross-tenant resource visibility among isolated tenants. In the CH scenario, for example, restorers and professionals may often need to connect and to share temporarily data among different tenants or to work with old customized software requiring the possibility to exploit specific management functionalities that are typically available not at the SaaS but at the IaaS or PaaS layers. This paper proposes a middleware called Registry that can provide additional functionalities to different user categories by joining the SaaS with the IaaS layer and that offers also multi-tenancy operations making private data available to certain project on-demand and enabling, in this way, cross-tenant data sharing. The Registry has been designed, developed and tested within the context of the SACHER project that provides a cloud-based infrastructure for the management of the cultural data lifecycle.

Cloud computing, now-a-days has become the jargon in the industry of IT. It is a model that gives worldwide access to shared pools of configurable resources over the internet. That means it run several applications or programs at the same time on more than one computer. The nature and usage of the cloud computing are developing rapidly both virtually and in reality. It is making things easier for the user of internet by many of its attractive features but these features, have not just only challenged the existing security system, but have also revealed new security issues. In this paper the deployment of cloud models are discussed . The cloud models are deployed as public cloud, private cloud and hybrid cloud. Each of these models have their own advantages and disadvantages. Cloud also provides some services like software as a service (SaaS), Platform as a service (PaaS), Infrastructure as a service (IaaS) . This paper is an insightful analysis of the features as well as the securities challenges of Software as a Service (SaaS) for cloud computing.

In this paper, we propose CLOUDGEN workflow that produces synthetic workloads for Infrastructure and Platform as a Service for the evaluation of resource management approaches in cloud computing systems. To this end, CLOUDGEN systematically processes and clusters records in a given workload trace and fits distributions for different workload parameters within the clusters. Different than the previous work, clustering is carried out to produce different virtual machine types for achieving models that are suitable for producing Infrastructure and Platform as a Service workload models. Finally, we demonstrate CLOUDGEN by modeling recent Azure traces with enough detail to enable researchers to use these models and generating synthetic traces that are statistically similar to the Azure traces.

Platform as a Service (PaaS) cloud offers a plethora of architectural options when designing new PaaS cloud applications. The quality evaluation of each architectural alternative is very difficult due to unknown performance and internal architecture of the used PaaS cloud services. The PaasArch tool supports software architects in quality evaluation of PaaS cloud applications using generated prototypes. Within the tool, fully functional cloud applications can be automatically generated based on their model, deployed to the cloud, and benchmarked. Our experiments show that the benchmarks give very good guidance to the software architect about the quality (namely performance) of evaluated architectural alternatives.

Nowadays, big data and machine learning are transforming the way we realize and manage our data. Even though the healthcare domain has recognized big data analytics as a prominent candidate, it has not yet fully grasped their promising benefits that allow medical information to be converted to useful knowledge. In this paper, we introduce AEGLE’s big data infrastructure provided as a Platform as a Service. Utilizing the suite of genomic analytics from the Chronic Lymphocytic Leukaemia (CLL) use case, we show that on-demand acceleration is profitable w.r.t a pure software cloud-based solution. However, we further show that on-demand acceleration is not offered as a "free-lunch" and we provide an in-depth analysis and lessons learnt on the co-design implications to be carefully considered for enabling cost-effective acceleration at the cloud-level.

Functional testing of web and mobile applications consists on impersonating a user through some kind of GUI automation technology, being Selenium the most popular solution nowadays. This piece of research progresses in this area by proposing an advanced User Impersonation as a Service (UIaaS) based on cloud technologies. The proposed approach can be seen as a layered service, in which the top tier is a platform called ElasTest, a Software as a Service (SaaS) testing tool aimed to ease end-to-end testing of different kind of applications. The capability to impersonate user provided by ElasTest is built on the top of a Platform as a Service (PaaS) implemented by extending the W3C WebDriver recommendation with advance features to assess web and mobile applications. Finally, an Infrastructure as a Service (IaaS) layer is used as ground technology. This infrastructure has been implemented by leveraging open source lightweight container technologies such as Docker and Kubernetes.

With the ongoing growth of internet, cloud computing plays significant role in providing requested resources and services to end users. But cloud based solutions were often associated with the risk of availability. In a private cloud setup, this risk gets enhanced, owing to less opportunities for inter operation. However, Interoperated solutions often employ brokers in private cloud environment, which lapses the security of cloud and increases the cost of interoperation. Toolkit named Cloud Inter-operation Toolkit (CIT), developed to fondle this issues in interoperation without using brokers. CIT promises to improve the fault tolerance of private clouds through inter operation among external clouds. Our Work, enhances CIT to provide Software as a Service along with Infrastructure as a Service and Platform as a Service, to take the toolkit one step ahead. To achieve effective SaaS interoperation, we made use of different services such as Authentication Service, Storage Service, Image Service and Payment Service offered by different external clouds. Finally this, work ensures sound inter-cloud operations at all service levels in a private multi cloud environment.

Thanks to the virtualization technology, which allow sharing of virtual resources (hardware/software) among the cloud users. With the help of the virtualization technology, cloud computing provides on-demand computing services such as software, infrastructure, and platform as a service to users via the Internet. Virtual machine placement is a hot research topic, which is a process of mapping of virtual machines (VMs) on host machines (HMs) in the cloud. In our work, we propose a new multi-objective on-demand VM placement algorithm in infrastructure cloud referred as OVMP, in which VMs placed in real time with help of a resource-balanced model. The proposed algorithm maximize resource utilization in a balanced manner and also minimize the power consumption and resource wastage of infrastructure cloud. We test the proposed algorithm through simulation using Amazon EC2 Instances and simulation results show that the proposed algorithm performs better than the existing ones in terms of various performance metrics.

Serverless computing provides a small runtime container to execute lines of codes without infrastructure management which is similar to Platform as a Service (PaaS) but a functional level. Amazon started the event-driven compute named Lambda functions in 2014 with a 25 concurrent limitation, but it now supports at least a thousand of concurrent invocation to process event messages generated by resources like databases, storage and system logs. Other providers, i.e., Google, Microsoft, and IBM offer a dynamic scaling manager to handle parallel requests of stateless functions in which additional containers are provisioning on new compute nodes for distribution. However, while functions are often developed for microservices and lightweight workload, they are associated with distributed data processing using the concurrent invocations. We claim that the current serverless computing environments can support dynamic applications in parallel when a partitioned task is executable on a small function instance. We present results of throughput, network bandwidth, a file I/O and compute performance regarding the concurrent invocations. We deployed a series of functions for distributed data processing to address the elasticity and then demonstrated the differences between serverless computing and virtual machines for cost efficiency and resource utilization.

Trend technologies such as big data, cloud, Internet of things remain to be progressed and applied in many ways both industry and academics. Remote sensing application or geospatial information system have been regarded as important application domains linked to those huge technologies. This study considered the linkage of satellite image processing services to cloud computing on Platform as a Service (PaaS) for easy access to development components. PaaS is a service driven by infrastructure and helps to simplify the use of multiple technology elements as a service unit. The Web Processing Service (WPS), Open GIS standard, supporting online asynchronous processing is also an important element for multiple data processing of large volumes of satellite image sets. This study presents the generalized scheme to develop web-based satellite image processing application with WPS as a service based on PaaS cloud. All computing resources used in this study are fully open source containing GeoServer and ZOO project for geo-processing and OpenStack and Cloud Foundry for cloud computing. This approach will be useful to build cost-effective and service-oriented remote sensing applications with image processing functions in the standpoint of system engineering.

In 2012, the Emilia-Romagna Region started realizing three regional data centers, to provide services to local Public Administrations (PAs), with an aim of reuse and efficiency, to be reached by reducing waste and costs. The data centers are connected to each other in ultra-wide bandwidth technology, thanks to the optical fiber Lepida Network owned by the Region and local public entities, already serving the Public Administrations of the territory. One of the strategic goals pursued by the Region through the data center is to provide IaaS (Infrastructure as a Service) and PaaS (Platform as a Service) opensource services to the PAs; for this reason the experimentation for the installation and deployment of the Open City Platform (OCP) was started.

Facing the rapidly growing user group of the EV charging network and the massive data information produced by it, the operational service system which uses the traditional monitoring platform framework has some deficiencies in terms of scalability and flexibility. In this paper, a cloud computing-based EV charging network operation service architecture is proposed, and Infrastructure as a Service layer, Platform as a Service layer, Software as a Service layer under the charging network cloud architecture are designed accordingly. In addition, the key technologies such as RFID label data security access of data acquisition layer, data integration and efficient indexing for multi-tenancy in the data layer, and detection of abnormal behavior of charge pile streaming data based on machine learning in data processing layer are studied, moreover, the corresponding solutions are given.

The Platform-as-a-Service (PaaS) model, allows service providers to build and deploy their services following streamlined work-flows. However, platforms deployed through the PaaS model can be very diverse in terms of technologies and involved subsystems (e.g. infrastructure, orchestration). Thus, the means for deploying and managing a service can significantly vary depending on the deployed platform. To address this issue, this paper proposes a policy-based framework designed for the Next Generation Platform-as-a-Service (NGPaaS). This framework allows service providers to define platform-wide and technology-agnostic policies to NGPaaS, by means of abstraction of the underlying platforms and the use of generic interfaces. The paper also presents a specific use case for the proposed framework, which targets network-oriented policies.

Cloud Computing provides a range of network services for applications running over the Internet. This leads to the huge accumulation of data on the cloud. The existing security provisions for cloud accessing techniques are proved to be inoperable or inadequate and highly vulnerable to security threats through the earlier studies. Because this problem is of highest concern it should be dealt with highest priority. Thus the three service models of cloud computing, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), are to be made highly secure. As an initiative to provide relatively high security to user data on the cloud, a new security feature in cloud computing model is preferred. In this paper, a top notch security model with maximum security approaches that can be implemented practically is proposed to safeguard the user's data present on the cloud.

5G standard emerges at a particular time in technology history when cloud transforms deeply almost all industries and services: it becomes obvious that innovations have to be made cloud-native for being successful. 5G must become the ubiquitous fabric blending universal connectivity (to humans, robots, sensors…) with cloud versatility and scalability. For realizing this vision, another model than IaaS must be adopted, the Platform as a Service (PaaS), which should be built to support telco-grade requirements and combine all sort of third-party applications. These are the core objectives of the Next Generation Platform as a Service (NGPaaS) project, a H2020 5G PPP Phase 2 project. The paper presents the project fundamentals, its architectural proposal and most relevant features.

Platform-As-A-Service (PaaS) systems offer customers a rich environment in which to build, deploy, and run applications. Today’s PaaS offerings are tailored mainly to the needs of web and mobile applications developers, and involve a fairly rigid stack of components and features. The vision of the H2020 5GPPP Phase 2 Next Generation Platform-as-a-Service (NGPaaS) project is to enable “build-to-order” customized PaaSes, tailored to the needs of a wide range of use cases with telcograde5G characteristics. This paper sets out the salient and innovative features of NGPaaS and explores the impacts on Operational Support Systems and Business Support Systems (OSS/BSS), moving from fixed centralized stacks to a much more flexible and modular distributed architecture.

Advances in sensors, wireless communications, embedded software, artificial intelligence, and data mining provide rapid growth of IoT, the Internet of Things, instances worldwide. Penetration of IoT has been increasingly visible every part of life on this planet. It also increases new conflicts between technologies and people. Smart cities, city-scale IoT platforms, are getting more common in many countries. We are engaged in one of such projects, CPaaS.io, City Platform as a Service, as an EU-Japan collaborative research. The author presents lessons learned on organizational coordination during IoT realization from requirement engineering with city governments in Tokyo. Then, the author presents a general view of organizational coordination model of IoT based on the learned lessons.

Evolution of IoT-empowered city-scale platform leads to migration requirements. All successful IoT systems need to address migration of existing or external systems. The author discusses the challenges of phased migration of heterogeneous IoT systems. Then, the author presents approaches of migratable architecture. Finally, the author proposes a partial adaptation migration architecture to cope with the challenges. The mapping architecture can provide a framework of accommodating evolution of heterogeneous IoT systems.

Internet of Things, IoT, connects physical objects and the cyberspace, which provides potentials to leverage organization and business redesign. An increasing need of digital business integration presents a broad challenge for a wide range of industries. It requires changes in design and operation processes, which ultimately leads to an organizational change. The author proposes dimensions to provide positioning of an IoT service in a comparative framework. Then, the author presents a framework using the proposed dimensions to classify IoT-empowered service engineering. This provides a general framework to assess coverage of city-scale IoT platforms.

Platform as a Service (PaaS) clouds provide part of the hardware/software stack and related services to tenant applications. Increased load is handled elastically by scaling, which either modifies the number of instances an application has available on the cloud or increases their available resources. However, because all these instances run inside isolated containers, experience gained by the first instance of an application cannot be easily shared with subsequent scaled instances. This results in both increased startup time and response timeout errors for the scaled instances as well as increased performance interference for any co-located applications; reacquiring this experience is a time-consuming and resource-intensive process. We propose a scalable and secure technique to share dynamically compiled artifacts produced by the first execution instance of an application and otherwise created for intra-OS sharing only with subsequent scaled or restarted instances as a solution to these problems. Our solution abides by the usual PaaS limitations and uses a distributed and containerized cloud service, which we experimentally show to be scalable on a Docker Swarm running on top of a 6-VM cluster; also, we discuss the results of a usability survey for the service's GUI conducted with expert subjects. The effectiveness of the DCAS technique was experimentally tested on an isolated installation of the PaaS software Cloudy Foundry; we measured significant reductions in both the startup time and response errors of scaled out instances as well as performance interference to co-located tenants during scaling.

Cloud computing is most recent advanced rising innovation that provides every kind of service to its users. It is generally based on “pay per usage” methodology that provides an immense Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS) model that altogether form a cloud. However it also has some issues that is to be resolved. Among many issues, Load balancing is major issue for cloud computing. Load Balancing is the technique to divide the load among different available resources and equalize with virtual machines to achieve efficiency and increases throughput. This paper first of all shows the theoretical study of load balancing and then shows different types of load balancing algorithms used to balance load. Further it also demonstrates and compares algorithms that gives an idea to merge them and have a choice to select dynamically based on some parameters and situation in which they are better for enhancing efficiency and increasing throughput. Experimental results clearly shows that hybrid approach of using different algorithms in different situations can lead to achieve our goal and better results.

Platform as a Service is the major productivity enabler in the cloud computing stack. By providing managed and highly automated application environments, it enhances developer productivity and reduces developer operations and maintenance efforts. The market, however, is fast-changing and offerings are differing conceptually as well as in their supported technological ecosystem. Therefore, provider selection is an important but currently not well supported step for companies trying to benefit from the technology. Influenced by the diversity of service offerings and the absence of applied standards this is a tedious task, especially for ensuring application portability. In this paper, we present a multi-criteria selection approach for cloud platforms based on a field-tested ontology and a comprehensive data set. The methodology is enhanced by semantic algorithms and mappings to reduce hidden query and data biases. This allows not only the exact matching of requirements but also the evaluation of possible alternatives that can be adapted to fit the defined requirements. We validate our approach by contrasting real user queries against the results of our semantically enhanced algorithms.

Auto-scaling, a key property of cloud computing, allows application owners to acquire and release resources on demand. However, the shared environment, along with the exponentially large configuration space of available parameters, makes the configuration of auto-scaling policies a challenging task. In particular, it is difficult to quantify, a priori, the impact of a policy on Quality of Service (QoS) provision. To address this problem, we propose a novel approach based on performance modelling and formal verification to produce performance guarantees on particular rule-based auto-scaling policies. We demonstrate the usefulness and efficiency of our techniques through a detailed validation process on two public cloud providers, Amazon EC2 and Microsoft Azure, targeting two cloud computing models, Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), respectively. Our experimental results show that the modelling process along with the model itself can be very effective in providing the necessary formal reasoning to cloud application owners with respect to the configuration of their auto-scaling policies, and consequently helping them to specify an auto-scaling policy which could minimise QoS violations.

The distant data centre-centric Internet of Things systems face the latency issue especially in the real-time-based applications. Recently, Fog Computing models have been introduced to overcome the latency issue by utilising the proximity-based computational resources. However, the increasing users of Fog Computing servers will cause bottleneck issues and consequently the latency issue arises again. This paper introduces the utilisation of Mist Computing (Mist) model, which exploits the computational and networking resources from the devices at the very edge of IoT networks. The proposed service-oriented mobile-embedded Platform as a Service framework enables the edge IoT devices to provide a platform that allows requesters to deploy and execute their own program models. The framework supports resource-aware autonomous service configuration that can manage the availability of the functions provided by the Mist node based on the dynamically changing hardware resource availability. Additionally, the framework also supports task distribution among a group of Mist nodes. The prototype has been tested and performance evaluated on the real world devices.

Cloud Computing is becoming a new computing model that organizations are adopting, mainly for the great benefits that presents. Platform as a Service (PaaS), enables the development of modern applications in cloud infrastructures, abstracting developers several infrastructure management issues. Due to the growing number of existing PaaS providers, the selection of a provider whose services are most appropriate to the requirements of an organization is not a trivial problem, considering the variety of options and the absence of appropriate comparable metrics. This work presents a framework for a methodological selection of a PaaS provider, providing suitable metrics to compare heterogeneous PaaS environments offered by existing providers. As an example of utilization of the proposed framework, two PaaS providers were compared.

Cloud services can be consumed using web service calls through the APIs provided by the cloud provider. Although using a fine-grained web-service request allows better flexibility of services, this approach will require service consumers to make multiple service requests to the cloud. The cloud will end up receiving a significantly higher number of requests, lowering the performance and increasing latency. The fine-grain APIs can also leave the data in an inconsistent state and create maintenance problems. Using coarse-grain requests will reduce the load on the server, especially necessary for cloud PAAS (Platform as a service) with governor limits on the number of API requests. In our research, we have proved that encapsulating multiple objects in a single API request will also improve the bandwidth, latency and API consumption for cloud PAAS.

For PaaS-deployed (Platform as a Service) customer-facing applications (e.g., online gaming and online chatting), ensuring low latencies is not just a preferred feature, but a must-have feature. Given the popularity and powerfulness of Java platforms, a significant portion of today's PaaS platforms run Java. JVM (Java Virtual Machine) manages a heap space to hold application objects. The heap space can be frequently GC-ed (Garbage Collected), and applications can be occasionally stopped for long time during some GC and JVM activities. In this work, we investigated the JVM pause problem. We found out that there are some (and large) JVM STW pauses cannot be explained by application-level activities and JVM activities during GC, instead, they are caused by OS mechanisms. We successfully reproduced such problems and root-cause-ed the reasons. The findings can be used to enhance JVM implementation. We also proposed a set of solutions to mitigate and eliminate these large STW pauses. We share the knowledge and experiences in this writing.