735 research outputs found
{\mu}-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments
Fog and Edge computing extend cloud services to the proximity of end users,
allowing many Internet of Things (IoT) use cases, particularly latency-critical
applications. Smart devices, such as traffic and surveillance cameras, often do
not have sufficient resources to process computation-intensive and
latency-critical services. Hence, the constituent parts of services can be
offloaded to nearby Edge/Fog resources for processing and storage. However,
making offloading decisions for complex services in highly stochastic and
dynamic environments is an important, yet difficult task. Recently, Deep
Reinforcement Learning (DRL) has been used in many complex service offloading
problems; however, existing techniques are most suitable for centralized
environments, and their convergence to the best-suitable solutions is slow. In
addition, constituent parts of services often have predefined data dependencies
and quality of service constraints, which further intensify the complexity of
service offloading. To solve these issues, we propose a distributed DRL
technique following the actor-critic architecture based on Asynchronous
Proximal Policy Optimization (APPO) to achieve efficient and diverse
distributed experience trajectory generation. Also, we employ PPO clipping and
V-trace techniques for off-policy correction for faster convergence to the most
suitable service offloading solutions. The results obtained demonstrate that
our technique converges quickly, offers high scalability and adaptability, and
outperforms its counterparts by improving the execution time of heterogeneous
services
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Mobile Edge Computing
This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists
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