Data stream processing is an attractive paradigm for analyzing IoT data at the edge of the Internet before transmitting processed results to a cloud. However, the relative scarcity of fog computing resources combined with the workloads’ non-stationary properties make it impossible to allocate a static set of resources for each application. We propose Gesscale, a resource auto-scaler which guarantees that a stream processing application maintains a sufficient Maximum Sustainable Throughput to process its incoming data with no undue delay, while not using more resources than strictly necessary. Gesscale derives its decisions about when to rescale and which geo-distributed resource(s) to add or remove on a performance model that gives precise predictions about the future maximum sustainable throughput after reconfiguration. We show that this auto-scaler uses 17% less resources, generates 52% fewer reconfigurations, and processes more input data than baseline auto-scalers based on threshold triggers or a simpler performance model.

/pdf/model-based-stream-processing-auto-scaling-in-geo-5ffq9ljnnq.pdf

Model-based Stream Processing Auto-scaling in Geo-Distributed Environments

Edge computing brings services and data closer to their users. It targets applications where low latency is key, such as cloud-based gaming, augmented reality, and connected cars. In these scenarios mobile users are often the norm, and services need to follow their users to continuously ensure the benefits of edge computing. Mobile edge computing addresses this demand, however, we identified three open challenges: First, edge computing facilities are sparse so far and achieving good connectivity requires a seamless transfer of workloads between different edge providers. Second, migration of edge services needs to be lightweight to make relocation transparent to users. Third, using geo-distributed edge resources of various providers demands for security measures to ensure service integrity and confidentiality.; B@We present E'dgedancer, a platform that offers infrastructure support for portable, provider-independent, and secure migration of edge services. E'dgedancer offers a lightweight and generic execution environment by utilising WebAssembly, which features an efficient, easy to transfer bytecode format. To make edge services self-migratable, and independent from the provider, E'dgedancer supports the notion of mobile agents offering weak and strong migration support. It utilises trusted execution to ensure the security of edge services during execution and migration. Finally, we shows that E'dgedancer features a lower migration time compared to previously proposed virtual machine migration while offering better security and platform independence.

Edgedancer: Secure Mobile WebAssembly Services on the Edge

Container migration is an essential functionality in large-scale geo-distributed platforms such as fog computing infrastructures. Contrary to migration within a single data center, long-distance migration requires that the container's disk state should be migrated together with the container itself. However, this state may be arbitrarily large, so its transfer may create long periods of unavailability for the container. We propose to exploit the layered structure provided by the OverlayFS file system to transparently snapshot the volumes' contents and transfer them prior to the actual container migration. We implemented this mechanism within Kubernetes. Our evaluations based on a real fog computing test-bed show that our techniques reduce the container's downtime during migration by a factor 4 compared to a baseline with no volume checkpoint.

/pdf/stateful-container-migration-in-geo-distributed-environments-vhhkgjqefd.pdf

Stateful Container Migration in Geo-Distributed Environments

Container technology has become a very popular choice for easing and managing the deployment of cloud applications and services. Container orchestration systems such as Kubernetes can automate to a large extent the deployment, scaling, and operations for containers across clusters of nodes, reducing human errors and saving cost and time. Designed with "traditional" cloud environments in mind (i.e., large datacenters with close-by machines connected by high-speed networks), systems like Kubernetes present some limitations in geo-distributed environments where computational workloads are moved to the edges of the network, close to where data is being generated/consumed. In geo-distributed environments, moving around containers, either to follow moving data sources/sinks or due to unpredictable changes in the network substrate, is a rather common operation. We present MyceDrive, a stateful resource migration solution natively integrated with the Kubernetes orchestrator. We show that geo-distributed Kubernetes pod migration is feasible while remaining fully transparent to the migrated application as well as its clients, while reducing downtimes up to 7x compared to state-of-the-art solutions.

/pdf/good-shepherds-care-for-their-cattle-seamless-pod-migration-3ecoafit.pdf

Good Shepherds Care For Their Cattle: Seamless Pod Migration in Geo-Distributed Kubernetes

The last decade has witnessed important development of network softwarization that has revolutionized the practice of networks. Virtualized networks bring novel and specific requirements for the control and orchestration of containerized network functions that are scattered across the network. In this regard, the migration of virtualized network functions plays a pivotal role to best meet the requirements of optimal resource utilization, load balancing and fault tolerance. The purpose of this survey is to offer a detailed overview of the progress on container migration so as to provide a better understanding of the trade‐off between the benefits associated with the migration and the practical challenges. The paper includes a classification of the placement algorithms that map the containerized network functions on the virtualized infrastructure. Following, a taxonomy of the migration techniques that perform the transfer of the containerized microservices is proposed.

https://hal.science/hal-03638246/document

Container placement and migration strategies for cloud, fog, and edge data centers: A survey

https://hal.inria.fr/hal-02513467/document

Paulo Souza Junior

Papers

Stateful Container Migration in Geo-Distributed Environments

Good Shepherds Care For Their Cattle: Seamless Pod Migration in Geo-Distributed Kubernetes

Potable Water Management with integrated Fog computing and LoRaWAN technologies

Stateful Application Migration in Geo-Distributed Systems. (Migration à état d'applications dans les systèmes géo-distribués)