213 research outputs found
On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration
Network slicing enables multiple virtual networks to be instantiated and
customized to meet heterogeneous use case requirements over 5G and beyond
network deployments. However, most of the solutions available today face
scalability issues when considering many slices, due to centralized controllers
requiring a holistic view of the resource availability and consumption over
different networking domains. In order to tackle this challenge, we design a
hierarchical architecture to manage network slices resources in a federated
manner. Driven by the rapid evolution of deep reinforcement learning (DRL)
schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware
local decision agents (DAs) dynamically placed in the radio access network
(RAN). These federated decision entities tailor their resource allocation
policy according to the long-term dynamics of the underlying traffic, defining
specialized clusters that enable faster training and communication overhead
reduction. Indeed, aided by a traffic-aware agent selection algorithm, our
proposed Federated DRL approach provides higher resource efficiency than
benchmark solutions by quickly reacting to end-user mobility patterns and
reducing costly interactions with centralized controllers.Comment: 15 pages, 15 Figures, accepted for publication at IEEE TV
EasyFL: A Low-code Federated Learning Platform For Dummies
Academia and industry have developed several platforms to support the popular
privacy-preserving distributed learning method -- Federated Learning (FL).
However, these platforms are complex to use and require a deep understanding of
FL, which imposes high barriers to entry for beginners, limits the productivity
of researchers, and compromises deployment efficiency. In this paper, we
propose the first low-code FL platform, EasyFL, to enable users with various
levels of expertise to experiment and prototype FL applications with little
coding. We achieve this goal while ensuring great flexibility and extensibility
for customization by unifying simple API design, modular design, and granular
training flow abstraction. With only a few lines of code, EasyFL empowers them
with many out-of-the-box functionalities to accelerate experimentation and
deployment. These practical functionalities are heterogeneity simulation,
comprehensive tracking, distributed training optimization, and seamless
deployment. They are proposed based on challenges identified in the proposed FL
life cycle. Compared with other platforms, EasyFL not only requires just three
lines of code (at least 10x lesser) to build a vanilla FL application but also
incurs lower training overhead. Besides, our evaluations demonstrate that
EasyFL expedites distributed training by 1.5x. It also improves the efficiency
of deployment. We believe that EasyFL will increase the productivity of
researchers and democratize FL to wider audiences
Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions
Edge and Fog computing paradigms utilise distributed, heterogeneous and
resource-constrained devices at the edge of the network for efficient
deployment of latency-critical and bandwidth-hungry IoT application services.
Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up
with the rapid development and deployment needs of the fast-evolving IoT
applications. Due to the fine-grained modularity of the microservices along
with their independently deployable and scalable nature, MSA exhibits great
potential in harnessing both Fog and Cloud resources to meet diverse QoS
requirements of the IoT application services, thus giving rise to novel
paradigms like Osmotic computing. However, efficient and scalable scheduling
algorithms are required to utilise the said characteristics of the MSA while
overcoming novel challenges introduced by the architecture. To this end, we
present a comprehensive taxonomy of recent literature on microservices-based
IoT applications scheduling in Edge and Fog computing environments.
Furthermore, we organise multiple taxonomies to capture the main aspects of the
scheduling problem, analyse and classify related works, identify research gaps
within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey
Rise of the Planet of Serverless Computing: A Systematic Review
Serverless computing is an emerging cloud computing paradigm, being adopted to develop a wide range of software applications.
It allows developers to focus on the application logic in the granularity of function, thereby freeing developers from tedious and
error-prone infrastructure management. Meanwhile, its unique characteristic poses new challenges to the development and deployment
of serverless-based applications. To tackle these challenges, enormous research efforts have been devoted. This paper provides a
comprehensive literature review to characterize the current research state of serverless computing. Specifically, this paper covers 164
papers on 17 research directions of serverless computing, including performance optimization, programming framework, application
migration, multi-cloud development, testing and debugging, etc. It also derives research trends, focus, and commonly-used platforms
for serverless computing, as well as promising research opportunities
- …