PiCasso: A lightweight edge computing platform

Lertsinsrubtavee, Adisorn; Ali, Anwaar; Molina-Jiménez, Carlos; Sathiaseelan, Arjuna; Crowcroft, Jon

doi:10.1109/cloudnet.2017.8071529

Cited by 40 publications

(27 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We can also use this data to auto-scale server and storage resources based on the changing demand, optimizing the amount of clients we can serve without overprovisioning. Additionally, we will build larger evaluation scenarios and use emulation techniques [34] to evaluate our solutions. Recently, a number of frameworks have been presented in literature [35] for container orchestration on a cluster of low-cost devices such as Raspberry Pi.…”

Section: Discussionmentioning

confidence: 99%

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Smet

Dhoedt

Simoens

2018

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

Driven by the increasing popularity of the microservice architecture, we see an increase in services with unknown demand pattern located in the edge network. Predeployed instances of such services would be idle most of the time, which is economically infeasible. Also, the finite storage capacity limits the amount of deployed instances we can offer. Instead, we present an on-demand deployment scheme using the Docker platform. In Docker, service images consist of layers, each layer adding specific functionality. This allows different services to reuse layers, avoiding cluttering the storages with redundant replicas. We propose a layer placement method which allows users to connect to a server, retrieve all necessary layers-possibly from multiple locations-and deploy an instance of the requested service within the desired response time. We search for the best layer placement which maximizes the satisfied demand given the storage and delay constraints. We developed an iterative optimization heuristic which is less exhaustive by dividing the global problem in smaller subproblems. Our simulation results show that our heuristic is able to solve the problem with less system resources. Last, we present interesting use-cases to use this approach in real-life scenarios.

show abstract

Section: Discussionmentioning

confidence: 99%

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Smet

Dhoedt

Simoens

2018

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

show abstract

“…Wang et al [295] proposed a lightweight edge computing platform that is based on SBCs, lightweight virtual switching, and lightweight container virtualization. Taking into account both the QoS requirements of edge services and the deployment cost and status of the underlying hardware, a lightweight platform for service deployment at the network edge was considered in [296]. To evaluate performance of the proposed platform, the authors developed RPis as edge servers and identified a set of the system parameters, such as, the number of services to be deployed and the number of supported users per service.…”

Section: ) Lightweight Platforms For Edge Computingmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

View full text Add to dashboard Cite

Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

show abstract

“…PiCasso is a container orchestration platform that specifically targets edge clouds with a focus on lightness and platform automation [16]. The developers of PiCasso intend to develop a service proxy that will redirect user's request to the closest node.…”

Section: B State Of the Artmentioning

confidence: 99%

Proximity-Aware Traffic Routing in Distributed Fog Computing Platforms

Fahs¹,

Pierre²

2019

2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

View full text Add to dashboard Cite

Container orchestration engines such as Kubernetes do not take into account the geographical location of application replicas when deciding which replica should handle which request. This makes them ill-suited to act as a generalpurpose fog computing platforms where the proximity between end users and the replica serving them is essential. We present proxy-mity, a proximity-aware traffic routing system for distributed fog computing platforms. It seamlessly integrates in Kubernetes, and provides very simple control mechanisms to allow system administrators to address the necessary trade-off between reducing the user-to-replica latencies and balancing the load equally across replicas. proxy-mity is very lightweight and it can reduce average user-to-replica latencies by as much as 90% while allowing the system administrators to control the level of load imbalance in their system.

show abstract

PiCasso: A lightweight edge computing platform

Cited by 40 publications

References 14 publications

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Proximity-Aware Traffic Routing in Distributed Fog Computing Platforms

Contact Info

Product

Resources

About