112 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
A lightweight secure adaptive approach for internet-of-medical-things healthcare applications in edge-cloud-based networks
Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications' execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays
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Distributed resource distribution and offloading for resource-agnostic microservices in industrial IoT
Due to increase in real-time mobile applications and Industrial Internet-of-Things (IIoT) devices, the edge computing paradigm provides a systematic and eccentric platform for real-time Internet-of-Things applications. Though the paradigm provides an effective infrastructure, however the resource requirements of IIoT devices change radically with time, which is described as a resource-agnostic property. Therefore, the estimation of resource requirements of IIoT devices is a critical and resilient assignment. In addition, it requires an extensive amount of resources to process the data traffic flows and microservice offloading. Hence, we present RAISE, a novel resource-agnostic microservice offloading scheme for mobile IIoT devices. RAISE efficiently estimates the resource-agnostic nature of IIoT devices to maximize their resource utilization in the network. Based on the estimated resource requirement, we propose a resource-agnostic microservice offloading scheme to maximize the success rate. Extensive experiments show that RAISE provides better performance in terms of network throughput and Quality-of-Service (QoS) than the other existing methods, SDTO and DTOS, in terms of cost and reliability
Cloud-Edge Orchestration for the Internet-of-Things: Architecture and AI-Powered Data Processing
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordThe Internet-of-Things (IoT) has been deeply penetrated into a wide range of important and critical sectors, including smart city, water, transportation, manufacturing and smart factory. Massive data are being acquired from a fast growing number of IoT devices. Efficient data processing is a necessity to meet diversified and stringent requirements of many emerging IoT applications. Due to the constrained computation and storage resources, IoT devices have resorted to the powerful cloud computing to process their data. However, centralised and remote cloud computing may introduce unacceptable communication delay since its physical location is far away from IoT devices. Edge cloud has been introduced to overcome this issue by moving the cloud in closer proximity to IoT devices. The orchestration and cooperation between the cloud and the edge provides a crucial computing architecture for IoT applications. Artificial intelligence (AI) is a powerful tool to enable the intelligent orchestration in this architecture. This paper first introduces such a kind of computing architecture from the perspective of IoT applications. It then investigates the state-of-the-art proposals on AI-powered cloud-edge orchestration for the IoT. Finally, a list of potential research challenges and open issues is provided and discussed, which can provide useful resources for carrying out future research in this area.Engineering and Physical Sciences Research Council (EPSRC
Multi-Layer Latency Aware Workload Assignment of E-Transport IoT Applications in Mobile Sensors Cloudlet Cloud Networks
These days, with the emerging developments in wireless communication technologies, such as 6G and 5G and the Internet of Things (IoT) sensors, the usage of E-Transport applications has been increasing progressively. These applications are E-Bus, E-Taxi, self-autonomous car, ETrain and E-Ambulance, and latency-sensitive workloads executed in the distributed cloud network. Nonetheless, many delays present in cloudlet-based cloud networks, such as communication delay, round-trip delay and migration during the workload in the cloudlet-based cloud network. However, the distributed execution of workloads at different computing nodes during the assignment is a challenging task. This paper proposes a novel Multi-layer Latency (e.g., communication delay, roundtrip delay and migration delay) Aware Workload Assignment Strategy (MLAWAS) to allocate the workload of E-Transport applications into optimal computing nodes. MLAWAS consists of different components, such as the Q-Learning aware assignment and the Iterative method, which distribute workload in a dynamic environment where runtime changes of overloading and overheating remain controlled. The migration of workload and VM migration are also part of MLAWAS. The goal is to minimize the average response time of applications. Simulation results demonstrate that MLAWAS earns the minimum average response time as compared with the two other existing strategies.publishedVersio
A Decade of Research in Fog computing: Relevance, Challenges, and Future Directions
Recent developments in the Internet of Things (IoT) and real-time
applications, have led to the unprecedented growth in the connected devices and
their generated data. Traditionally, this sensor data is transferred and
processed at the cloud, and the control signals are sent back to the relevant
actuators, as part of the IoT applications. This cloud-centric IoT model,
resulted in increased latencies and network load, and compromised privacy. To
address these problems, Fog Computing was coined by Cisco in 2012, a decade
ago, which utilizes proximal computational resources for processing the sensor
data. Ever since its proposal, fog computing has attracted significant
attention and the research fraternity focused at addressing different
challenges such as fog frameworks, simulators, resource management, placement
strategies, quality of service aspects, fog economics etc. However, after a
decade of research, we still do not see large-scale deployments of
public/private fog networks, which can be utilized in realizing interesting IoT
applications. In the literature, we only see pilot case studies and small-scale
testbeds, and utilization of simulators for demonstrating scale of the
specified models addressing the respective technical challenges. There are
several reasons for this, and most importantly, fog computing did not present a
clear business case for the companies and participating individuals yet. This
paper summarizes the technical, non-functional and economic challenges, which
have been posing hurdles in adopting fog computing, by consolidating them
across different clusters. The paper also summarizes the relevant academic and
industrial contributions in addressing these challenges and provides future
research directions in realizing real-time fog computing applications, also
considering the emerging trends such as federated learning and quantum
computing.Comment: Accepted for publication at Wiley Software: Practice and Experience
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