Challenges for the comprehensive management of cloud services in a PaaS framework

The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies

Opportunities and Challenges of Joint Edge and Fog Orchestration

Pushing contents, applications, and network functions closer to end users is necessary to cope with the huge data volume and low latency required in future 5G networks. Edge and fog frameworks have emerged recently to address this challenge. Whilst the edge framework was more infrastructure focused and more mobile operator-oriented, the fog was more pervasive and included any node (stationary or mobile), including terminal devices. This article analyzes the opportunities and challenges to integrate, federate, and jointly orchestrate the edge and fog resources into a unified framework.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586

A Deep Reinforcement Learning based Algorithm for Time and Cost Optimized Scaling of Serverless Applications

Serverless computing has gained a strong traction in the cloud computing community in recent years. Among the many benefits of this novel computing model, the rapid auto-scaling capability of user applications takes prominence. However, the offer of adhoc scaling of user deployments at function level introduces many complications to serverless systems. The added delay and failures in function request executions caused by the time consumed for dynamically creating new resources to suit function workloads, known as the cold-start delay, is one such very prevalent shortcoming. Maintaining idle resource pools to alleviate this issue often results in wasted resources from the cloud provider perspective. Existing solutions to address this limitation mostly focus on predicting and understanding function load levels in order to proactively create required resources. Although these solutions improve function performance, the lack of understanding on the overall system characteristics in making these scaling decisions often leads to the sub-optimal usage of system resources. Further, the multi-tenant nature of serverless systems requires a scalable solution adaptable for multiple co-existing applications, a limitation seen in most current solutions. In this paper, we introduce a novel multi-agent Deep Reinforcement Learning based intelligent solution for both horizontal and vertical scaling of function resources, based on a comprehensive understanding on both function and system requirements. Our solution elevates function performance reducing cold starts, while also offering the flexibility for optimizing resource maintenance cost to the service providers. Experiments conducted considering varying workload scenarios show improvements of up to 23% and 34% in terms of application latency and request failures, while also saving up to 45% in infrastructure cost for the service providers.Comment: 15 pages, 22 figure

Decision support for application migration to the cloud

The Cloud Computing paradigm promises a major shift in providing computing resources and enterprises are encouraged to consider migrating existing applications to this new environment. In this regard various approaches of decision support for application migration to the Cloud exist to aid decision makers with this challenging multi-dimensional issue. This Master's thesis considers the elaboration of a recent vision of a decision support system for application migration to the Cloud taking into account decisions to be made, and tasks that support decision-making. Based on a literature investigation this work constitutes a refined version of this approach by identifying several specific decisions and their relationships to other decisions and tasks. By means of a survey these extensions have been evaluated by peers in research and professional practice. Finally, a prototype based on current web technologies has been implemented to actually make the decision support approach available to decision makers considering application migration to the Cloud

Enabling horizontal scalability in an open source enterprise services bus

Cloud computing is a recent paradigm which describes a new way of consuming and delivering IT Services. In the Platform as a Service (PaaS) model, an underlying infrastructure such as network, operative system or server is provided to the Cloud consumers for either deploying their own applications, or applications supplied by the Cloud provider. In effect, Cloud computing modifies how applications should be built, provided, and consumed, as they may provide or be totally exposed as services, or consume existing third party applications services. The main advantages in Cloud computing are related to dynamic scaling of resources which are able to adapt to changes based on demand of resources and the use of multi-tenancy techniques in order based on sharing of resources between different users towards achieving the economy of scale. The Enterprise Service Bus (ESB) is essential as an integration middleware between application and services within and between multiple Cloud infrastructures. Different communication protocols might be used by application services and it is therefore necessary to have a mediator between them. Several challenges might arise when using an ESB as communication mediator between applications in cloud when to scale in and scale out to optimize resource consumption. The number of ESB instances should vary depending on the load in the Cloud infrastructure. This can be achieved by dynamically scaling in and out multiple ESB instances which constitute the horizontal ESB cluster. In this Master Thesis we focus on enabling horizontal scalability support for an open source multi-tenant aware Enterprise Service Bus (ESB). The investigation is based on two possible scenarios for enabling horizontal scalability: interconnected vs. non interconnected ESB instances. Therefore, in this work we investigate their advantages, disadvantages, and possible challenges and solutions. Based on previous investigations, a realization approach for enabling multi-instance management of a multi-tenant aware ESB is provided

4CaaSt: Comprehensive management of Cloud services through a PaaS

The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its management and lifecycle, a one stop shop for Cloud services and the management of resources in the PaaS level (including elasticity). 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud- aware immigrant technologies