Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment

Sami, Hani; Mourad, Azzam

doi:10.1109/tnsm.2019.2963643

Cited by 80 publications

(46 citation statements)

References 25 publications

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“…Intelligent container placement schemes have been proposed that assist in selection and distribution of services towards the edge data centers [20]. Unlike traditional 5G telco data centers, these are characterized by sporadic resources availability, mobility, and increased flexibility.…”

Section: Related Workmentioning

confidence: 99%

CASE: A Context-Aware Storage Placement and Retrieval Ecosystem

et al. 2022

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Emerging cloud-native technologies, such as container-runtime and container-orchestrator offer unprecedented agility in developing and running applications, especially when combined with microservice-style architecture. Several existing 5G-Telecom network products such as element Management System (e-MS), 5G-Core and 5G-Access are being redesigned to fit the microservice paradigm. Cloud environment allows enterprises to scale their application on-demand with minimum cost; however, it is often difficult to use containers without sacrificing many benefits that cloud-native technology offers. The e-MS is characterized to orchestrate 5G network elements (5GNEs) deployed nationwide, and systematically store terabytes of stateful data per second. Containers are characterized to have an ephemeral state, hence 'statefulness' aspect of e-MS makes orchestration complex. In this paper, different challenges around stateful storage selection, content placement and content retrieval operations within e-MS microservices are described. To overcome these challenges, to this end, we propose CASE -A Context-Aware Storage placement and retrieval Ecosystem -which enables context-based operations to be intrinsically supported by the underlying e-MS application. Our approach has been designed to maintain the location-independent philosophy of cloud-native by associating context information directly to 5GNE rather than fixed storage entities, thereby ensuring scalability. Through simulation with real data-set obtained from one of the world's largest terrestrial telecom operator, we show that based on such location-independent context information, CASE with e-MS can facilitate high performance despite dynamic 5GNE count agility, stateless e-MS replication and stateful storage scaling, while not posing a significant signaling burden on the cloud environment.

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Section: Related Workmentioning

confidence: 99%

CASE: A Context-Aware Storage Placement and Retrieval Ecosystem

et al. 2022

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“…Besides, most of them need a high number of iterations and may provide a local rather than global optimum [8]. In fact, they mainly use heuristics to yield feasible solutions, which makes them subject to two major limitations [25] [26] [27]:…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for the Computation Offloading in MIMO-based Edge Computing

Sadiki¹,

Dssouli²,

En‐Nouaary³

2021

Preprint

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Multi-access Edge Computing (MEC) has recently emerged as a potential technology to serve the needs of mobile devices (MDs) in 5G and 6G cellular networks. By offloading tasks to high-performance servers installed at the edge of the wireless networks, resource-limited MDs can cope with the proliferation of the recent computationally-intensive applications. In this paper, we study the computation offloading problem in a massive multiple-input multiple-output (MIMO)-based MEC system where the base stations are equipped with a large number of antennas. Our objective is to minimize the power consumption and offloading delay at the MDs under the stochastic system environment. To this end, we formulate the problem as a Markov Decision Process (MDP) and propose two Deep Reinforcement Learning (DRL) strategies to learn the optimal offloading policy without any prior knowledge of the environment dynamics. First, a Deep Q-Network (DQN) strategy to solve the curse of the state space explosion is analyzed. Then, a more general Proximal Policy Optimization (PPO) strategy to solve the problem of discrete action space is introduced. Simulation results show that the proposed DRL-based strategies outperform the baseline and state-of-the-art algorithms. Moreover, our PPO algorithm exhibits stable performance and efficient offloading results compared to the benchmark DQN strategy.

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“…Budgets have an additional hierarchy (encoded as nested JSON objects). The hierarchical structure lets us model how the resources are coupled together without adding adhoc optimization constraints, which is not the case in other works (e.g., [7], [19]). For example, if we ask for a processing tile comprised of a processor and a memory, we want them to be mapped on the same tile; however, asking for a processor and a memory without the notion of tile does not enforce this constraint.…”

Section: Brokering: Pareto Optimizationmentioning

confidence: 99%

“…Given the light-weight nature of embedded systems, as an example of resource-constrained platforms, only a subset of applications can execute on the system at a time and when tasks are no longer needed, they are removed. This dynamic presence of tasks is also seen in other contexts such as fog computing and IoT where services are dynamically deployed on fog nodes and edge devices [6], [7]. Application deployment includes the entire process required to prepare a software application to run and operate in a specific environment.…”

Section: Introductionmentioning

confidence: 99%

A Deployment Framework for Quality-Sensitive Applications in Resource-Constrained Dynamic Environments

Nikkhah

Geilen

Goswami

et al. 2021

2021 24th Euromicro Conference on Digital System Design (DSD)

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Dynamic On-Demand Fog Formation Offering On-the-Fly IoT Service Deployment

Cited by 80 publications

References 25 publications

CASE: A Context-Aware Storage Placement and Retrieval Ecosystem

CASE: A Context-Aware Storage Placement and Retrieval Ecosystem

Deep Reinforcement Learning for the Computation Offloading in MIMO-based Edge Computing

A Deployment Framework for Quality-Sensitive Applications in Resource-Constrained Dynamic Environments

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