Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

SCHEMA: Service Chain Elastic Management with distributed reinforcement learning

As the demand for Network Function Virtualization accelerates, service providers are expected to advance the way they manage and orchestrate their network services to offer lower latency services to their future users. Modern services require complex data flows between Virtual Network Functions, placed in separate network domains, risking an increase in latency that compromises the offered latency constraints. This shift requires high levels of automation to deal with the scale and load of future networks. In this paper, we formulate the Service Function Chaining (SFC) placement problem and then we tackle it by introducing SCHEMA, a Distributed Reinforcement Learning (RL) algorithm that performs complex SFC orchestration for low latency services. We combine multiple RL agents with a Bidding Mechanism to enable scalability on multi-domain networks. Finally, we use a simulation model to evaluate SCHEMA, and we demonstrate its ability to obtain a 60.54% reduction of average service latency when compared to a centralised RL solution.Peer ReviewedPostprint (author's final draft

Network automation: challenges, enablers, and benefits

Communication infrastructures are evolving towards an ad-hoc service provisioning scenario where programmability and flexibility are fundamental concepts. Network automation is expected to play a vital role in streamlining all aspects of the service provisioning process (i.e., deployment, maintenance, and tear down). However, to fully realize this autonomous operation vision, closed-loop automation procedures need to be developed.This tutorial will present the main motivations and challenges behind designing and operating closed-loop autonomous decision-making processes, including a brief overview of current standardization initiatives. The tutorial will then address several use cases showcasing how network automation can alleviate the complexity of the service provisioning processes and the benefits brought in by the introduction of network automation

Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. A softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this article, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.Part of this work has been performed within the 5G-MoNArch project (Grant Agreement No. 761445), part of the Phase II of the 5th Generation Public Private Partnership (5G-PPP) program partially funded by the European Commission within the Horizon 2020 Framework Program. This work was also supported by the the 5G-Transformer project (Grant Agreement No. 761536)

The 6G Computing Continuum (6GCC): Meeting the 6G computing challenges

6G systems, such as Large Intelligent Surfaces, will require distributed, complex, and coordinated decisions throughout a very heterogeneous and cell free infrastructure. This will require a fundamentally redesigned software infrastructure accompanied by massively distributed and heterogeneous computing resources, vastly different from current wireless networks.To address these challenges, in this paper, we propose and motivate the concept of a 6G Computing Continuum (6GCC) and two research testbeds, to advance the rate and quality of research. 6G Computing Continuum is an end-to-end computeand software platform for realizing large intelligent surfaces and its tenant users and applications. One for addressing the challenges or orchestrating shared computational resources in the wireless domain, implemented on a Large Intelligent Surfaces testbed. Another simulation-based testbed is intended to address scalability and global-scale orchestration challenges

Overall 5G-MoNArch architecture and implications for resource elasticity

Proceeding of: 2018 European Conference on Networks and Communications (EuCNC), June 18-21, Ljubljana, SloveniaThe fifth generation (5G) of mobile and wireless communications networks aims at addressing a diverse set of use cases, services, and applications with a particular focus on enabling new business cases via network slicing. The development of 5G has thus advanced quickly with research projects and standardization efforts resulting in the 5G baseline architecture. Nevertheless, for the realization of native end-to-end (E2E) network slicing, further features and optimizations shall still be introduced. In this paper, essential building blocks and design principles of the 5G architecture will be discussed capitalizing on the innovations that are being developed in the 5G-MoNArch project. Furthermore, building on the concept of resource elasticity introduced by 5G-MoNArch and briefly resummarized in this paper, an elasticity functional architecture is presented where the architectural implications required for each of the three dimensions of elasticity are described, namely computational, orchestration-driven, and slice-aware elasticity.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the EU