7 research outputs found
Towards delay-aware container-based Service Function Chaining in Fog Computing
Recently, the fifth-generation mobile network (5G) is getting significant attention. Empowered by Network Function Virtualization (NFV), 5G networks aim to support diverse services coming from different business verticals (e.g. Smart Cities, Automotive, etc). To fully leverage on NFV, services must be connected in a specific order forming a Service Function Chain (SFC). SFCs allow mobile operators to benefit from the high flexibility and low operational costs introduced by network softwarization. Additionally, Cloud computing is evolving towards a distributed paradigm called Fog Computing, which aims to provide a distributed cloud infrastructure by placing computational resources close to end-users. However, most SFC research only focuses on Multi-access Edge Computing (MEC) use cases where mobile operators aim to deploy services close to end-users. Bi-directional communication between Edges and Cloud are not considered in MEC, which in contrast is highly important in a Fog environment as in distributed anomaly detection services. Therefore, in this paper, we propose an SFC controller to optimize the placement of service chains in Fog environments, specifically tailored for Smart City use cases. Our approach has been validated on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our SFC controller has been implemented as an extension to the scheduling features available in Kubernetes, enabling the efficient provisioning of container-based SFCs while optimizing resource allocation and reducing the end-to-end (E2E) latency. Results show that the proposed approach can lower the network latency up to 18% for the studied use case while conserving bandwidth when compared to the default scheduling mechanism
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
journa
Towards end-to-end resource provisioning in Fog Computing over Low Power Wide Area Networks
Recently, with the advent of the Internet of Things (IoT), Smart Cities have emerged as a potential business opportunity for most cloud service providers. However, centralized cloud architectures cannot sustain the requirements imposed by many IoT services. High mobility coverage and low latency constraints are among the strictest requirements, making centralized solutions impractical. In response, theoretical foundations of Fog Computing have been introduced to set up a distributed cloud infrastructure by placing computational resources close to end-users. However, the acceptance of its foundational concepts is still in its early stages. A key challenge still to answer is Service Function Chaining (SFC) in Fog Computing, in which services are connected in a specific order forming a service chain to fully leverage on network softwarization. Also, Low Power Wide Area Networks (LPWANs) have been getting significant attention. Opposed to traditional wireless technologies, LPWANs are focused on low bandwidth communications over long ranges. Despite their tremendous potential, many challenges still arise concerning the deployment and management of these technologies, making their wide adoption difficult for most service providers. In this article, a Mixed Integer Linear Programming (MILP) formulation for the IoT service allocation problem is proposed, which takes SFC concepts, different LPWAN technologies and multiple optimization objectives into account. To the best of our knowledge, our work goes beyond the current state-of-the-art by providing a complete end-to-end (E2E) resource provisioning in Fog-cloud environments while considering cloud and wireless network requirements. Evaluations have been performed to evaluate in detail the proposed MILP formulation for Smart City use cases. Results show clear trade-offs between the different provisioning strategies. Our work can serve as a benchmark for resource provisioning research in Fog-cloud environments since the model approach is generic and can be applied to a wide range of IoT use cases
Service level agreement specification for IoT application workflow activity deployment, configuration and monitoring
PhD ThesisCurrently, we see the use of the Internet of Things (IoT) within various domains
such as healthcare, smart homes, smart cars, smart-x applications, and smart
cities. The number of applications based on IoT and cloud computing is projected
to increase rapidly over the next few years. IoT-based services must meet
the guaranteed levels of quality of service (QoS) to match users’ expectations.
Ensuring QoS through specifying the QoS constraints using service level agreements
(SLAs) is crucial. Also because of the potentially highly complex nature
of multi-layered IoT applications, lifecycle management (deployment, dynamic
reconfiguration, and monitoring) needs to be automated. To achieve this it is
essential to be able to specify SLAs in a machine-readable format.
currently available SLA specification languages are unable to accommodate
the unique characteristics (interdependency of its multi-layers) of the IoT domain.
Therefore, in this research, we propose a grammar for a syntactical structure
of an SLA specification for IoT. The grammar is based on a proposed conceptual
model that considers the main concepts that can be used to express the requirements
for most common hardware and software components of an IoT application
on an end-to-end basis. We follow the Goal Question Metric (GQM) approach to
evaluate the generality and expressiveness of the proposed grammar by reviewing
its concepts and their predefined lists of vocabularies against two use-cases
with a number of participants whose research interests are mainly related to IoT.
The results of the analysis show that the proposed grammar achieved 91.70% of
its generality goal and 93.43% of its expressiveness goal.
To enhance the process of specifying SLA terms, We then developed a toolkit
for creating SLA specifications for IoT applications. The toolkit is used to simplify
the process of capturing the requirements of IoT applications. We demonstrate
the effectiveness of the toolkit using a remote health monitoring service (RHMS)
use-case as well as applying a user experience measure to evaluate the tool by
applying a questionnaire-oriented approach. We discussed the applicability of our
tool by including it as a core component of two different applications: 1) a contextaware
recommender system for IoT configuration across layers; and 2) a tool for
automatically translating an SLA from JSON to a smart contract, deploying it
on different peer nodes that represent the contractual parties. The smart contract
is able to monitor the created SLA using Blockchain technology. These two
applications are utilized within our proposed SLA management framework for IoT.
Furthermore, we propose a greedy heuristic algorithm to decentralize workflow
activities of an IoT application across Edge and Cloud resources to enhance
response time, cost, energy consumption and network usage. We evaluated the
efficiency of our proposed approach using iFogSim simulator. The performance
analysis shows that the proposed algorithm minimized cost, execution time, networking,
and Cloud energy consumption compared to Cloud-only and edge-ward
placement approaches