V-Edge: Virtual Edge Computing as an Enabler for Novel Microservices and Cooperative Computing

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays that are just owed to limited propagation speed, edge servers deployed in close proximity to the user, e.g., at some 5G gNB, serve as proxy for the cloud. Yet this promising idea is hampered by the limited availability of such edge servers. In this paper, we discuss a way forward, namely the virtual edge computing (V-Edge) concept. V-Edge bridges the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available using well-defined interfaces. V-Edge also acts as an enabler for novel microservices as well as cooperative computing solutions. We introduce the general V-Edge architecture and we characterize some of the key research challenges to overcome, in order to enable wide-spread and even more powerful edge services

Optimum VM Placement for NFV Infrastructures

This paper shows how to use a Linux-based operating system as a real-time processing platform for low-latency and predictable packet processing in cloudified radio-access network (cRAN) scenarios. This use-case exhibits challenging end-to-end processing latencies, in the order of milliseconds for the most time-critical layers of the stack. A significant portion of the variability and instability in the observed end-to-end performance in this domain is due to the power saving capabilities of modern CPUs, often in contrast with the low-latency and high-performance requirements of this type of applications. We discuss how to properly configure the system for this scenario, and evaluate the proposed configuration on a synthetic application designed to mimic the behavior and computational requirements of typical software components implementing baseband processing in production environments

V-Edge: Virtual Edge Computing as an Enabler for Novel Microservices and Cooperative Computing

As we move from 5G to 6G, edge computing is one of the concepts that needs revisiting. Its core idea is still intriguing: Instead of sending all data and tasks from an end user's device to the cloud, possibly covering thousands of kilometers and introducing delays lower-bounded by propagation speed, edge servers deployed in close proximity to the user (e.g., at some base station) serve as proxy for the cloud. This is particularly interesting for upcoming machine-learning-based intelligent services, which require substantial computational and networking performance for continuous model training. However, this promising idea is hampered by the limited number of such edge servers. In this article, we discuss a way forward, namely the V-Edge concept. V-Edge helps bridge the gap between cloud, edge, and fog by virtualizing all available resources including the end users' devices and making these resources widely available. Thus, V-Edge acts as an enabler for novel microservices as well as cooperative computing solutions in next-generation networks. We introduce the general V-Edge architecture, and we characterize some of the key research challenges to overcome in order to enable wide-spread and intelligent edge services

A survey on mobility-induced service migration in the fog, edge, and related computing paradigms

The final publication is available at ACM via http://dx.doi.org/10.1145/3326540With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.Peer ReviewedPreprin

An experimental study on latency-aware and self-adaptive service chaining orchestration in distributed NFV and SDN infrastructures

Network Function Virtualization (NFV) and Software Defined Networking (SDN) changed radically the way 5G networks will be deployed and services will be delivered to vertical applications (i.e., through dynamic chaining of virtualized functions deployed in distributed clouds to best address latency requirements). In this work, we present a service chaining orchestration system, namely LASH-5G, running on top of an experimental set-up that reproduces a typical 5G network deployment with virtualized functions in geographically distributed edge clouds. LASH-5G is built upon a joint integration effort among different orchestration solutions and cloud deployments and aims at providing latency-aware, adaptive and reliable service chaining orchestration across clouds and network resource domains interconnected through SDN. In this paper, we provide details on how this orchestration system has been deployed and it is operated on top of the experimentation infrastructure provided within the Fed4FIRE+ facility and we present performance results assessing the effectiveness of the proposed orchestration approach