Fog Platforms Research Articles

Multi-objective discrete Cuckoo search algorithm for optimization of bag-of-tasks scheduling in fog computing environment

Internet of Things (IoT) applications are mostly time-sensitive and require reliable executions. The majority of IoT applications experience low quality of service (QoS) because of the intrinsic delay in utilizing the far-distant cloud infrastructures. To experience better QoS, fog computing as a complementary facility for cloud infrastructure is extended near the IoT devices. The bag of tasks (BoTs) IoT applications include a number of independent tasks each of which needs a certain amount of processing time so it is favorable to experience the lowest total execution time, makespan, for all applications. In addition, reliable execution guarantees the business continuity of IoT applications. Therefore, this paper presents a fog computing scheduler for the execution of BoT IoT applications in the fog platforms to minimize makespan and maximize the reliability execution at the same time. To this end, a new reliability model is presented. Then, the BoTs scheduling issue is formulated into a multi-objective discrete optimization problem with makespan and reliability optimality viewpoints. To solve this combinatorial problem, a multi-objective discrete cuckoo search algorithm (MoDCSA) is extended. The proposed MoDCSA optimization algorithm takes the benefit of several discrete operators each of which is smartly invoked. The quality of gained results from simulations of extensive scenarios verifies the effectiveness of the proposed MoDCSA scheduling algorithm which has 2.27%, 3.89%, 5.45%, 5.40%, and 4.55% improvement in terms of makespan and 1.99%, 5.11%, 6.39%, 7.95%, and 7.78% improvement in terms of reliability against MoBLA, MoGA, MoGWO, MoSA, and MoGSA comparative algorithms respectively.

Microservice instances selection and load balancing in fog computing using deep reinforcement learning approach

Fog-native computing is an emerging paradigm that makes it possible to build flexible and scalable Internet of Things (IoT) applications using microservice architecture at the network edge. With this paradigm, IoT applications are decomposed into multiple fine-grained microservices, strategically deployed on various fog nodes to support a wide range of IoT scenarios, such as smart cities and smart farming. Nonetheless, the performance of these IoT applications is affected by their limited effectiveness in processing offloaded IoT requests originating from multiple IoT devices. Specifically, the requested IoT services are composed of multiple dependent microservice instances collectively referred to as a service plan (SP). Each SP comprises a series of tasks designed to be executed in a predefined order, with the objective of meeting heterogeneous Quality of Service (QoS) requirements (e.g., low service delays). Different from the cloud, selecting the appropriate service plan for each IoT request can be a challenging task in dynamic fog environments due to the dependency and decentralization of microservice instances, along with the instability of network conditions and service requests (i.e., change quickly over time). To deal with this challenge, we study the microservice instances selection problem for IoT applications deployed on fog platforms and propose a learning-based approach that employs Deep Reinforcement Learning (DRL) to compute the optimal service plans. The latter optimizes the delay of application requests while effectively balancing the load among microservice instances. In our selection process, we carefully address the plan-dependency to efficiently select valid service plans for every request by introducing two distinct approaches; an action masking approach and an adaptive action mapping approach. Additionally, we propose an improved experience replay to address delayed action effects and enhance our model training efficiency. A series of experiments were conducted to assess the performance of our Microservice Instances Selection Policy (MISP) approach. The results demonstrate that our model reduces the average failure rate by up to 65% and improves load balance by up to 45% on average when compared to the baseline algorithms.

A new service recommendation method for agricultural industries in the fog-based Internet of Things environment using a hybrid meta-heuristic algorithm

Regardless of public perceptions of the agricultural techniques, the fact is that the current agriculture industry is more data-driven, accurate, and intelligent than before. Therefore, novel technologies such as fog computing and the Internet of Things (IoT) can provide a flexible and real-time platform to meet the data-driven requirements of the current agricultural decision-makers. Smart agriculture is a new idea since IoT sensors and fog platforms can provide information about agriculture and then operate on them based on user feedback. Also, with the rapid growth of IoT, the importance of recommender systems is increased in this domain. Therefore, the main goals of this study are to improve the accuracy of agricultural service recommendations and decrease the Mean Absolute Error (MAE) in the IoT-based fog systems using collaborative filtering and artificial Artificial Bee Colony (ABC). However, many of the current methods suffer from low accuracy of recommendations of the agricultural services. The present article suggested a collaborative filtering-based approach based on the ABC and genetic operators to design an effective recommender scheme in fog-based IoT systems. The results showed that the accuracy and MAE are optimized compared to some state-of-the-art methods. They also revealed that compared to other methods, the proposed method improves ranking score by 5.8 %, precision by 5%, recall by 5.7%, intra similarity by 13%, and hamming distance by 4.8%.

Towards Building Cyberphysical Systems with Agile Architecture

The current stage of technology development is characterized by an increase in the complexity of the created anthropogenic systems, a constant expansion of the scope of information technologies, an increase in the intelligence level of the created systems, and the appearance of new paradigms for building information-oriented systems such as cyber-physical systems, the Internet of things, and cloud and fog systems. Modern information-oriented systems very often have dynamic structure, implement complex adaptive behavior, and can be considered as systems with agile architecture. The article discusses one of the possible approaches for building cyberphysical systems with agile architecture on fog platforms. The idea of the proposed approach is to accumulate knowledge about the current state of the observed cyberphysical systems in the form of knowledge graphs. As a model, it is proposed to use multilevel relatively finite state operating automaton at the upper level and knowledge graphs at the lower level. A distinctive feature of the developed approach is that models that describe the current state of the observed system can be built automatically.

A Review of Blockchain-enabled Fog Computing in the Cloud Continuum Context

This article surveys the literature in search of systems and components that use Blockchain or Smart Contracts to manage computational resources, store data, and execute services using the Cloud paradigm. This paradigm has extended from warehouse-scale data centres to the edge of the network and in between, giving rise to the domains of Edge and Fog Computing. The Cloud Continuum encompasses the three fields and focuses on the management of applications composed of connected services that span from one end to the other of the computational spectrum. Several components that are commanded by Smart Contracts are identified and compared concerning their functionality. Two important research directions are the experimental evaluation of the identified platforms and the identification of standards that can accelerate the adoption of Blockchain-based Fog platforms.

AuctionWhisk: Using an auction‐inspired approach for function placement in serverless fog platforms

AbstractThe Function‐as‐a‐Service (FaaS) paradigm has a lot of potential as a computing model for fog environments comprising both cloud and edge nodes, as compute requests can be scheduled across the entire fog continuum in a fine‐grained manner. When the request rate exceeds capacity limits at the resource‐constrained edge, some functions need to be offloaded toward the cloud. In this article, we present an auction‐inspired approach in which application developers bid on resources while fog nodes decide locally which functions to execute and which to offload in order to maximize revenue. Unlike many current approaches to function placement in the fog, our approach can work in an online and decentralized manner. We also present our proof‐of‐concept prototype AuctionWhisk that illustrates how such an approach can be implemented in a real FaaS platform. Through a number of simulation runs and system experiments, we show that revenue for overloaded nodes can be maximized without dropping function requests.

A Two Stage Task Scheduler for Effective Load Optimization in Cloud – FoG Architectures

In recent times, computing technologies have moved over to a new dimension with the advent of cloud platforms which provide seamless rendering of required services to consumers either in static or dynamic state. In addition, the nature of data being handled in today’s scenario has also become sophisticated as mostly real time data acquisition systems equipped with High-Definition capture (HD) have become common. Lately, cloud systems have also become prone to computing overheads owing to huge volume of data being imparted on them especially in real time applications. To assist and simplify the computational complexity of cloud systems, FoG platforms are being integrated into cloud interfaces to streamline and provide computing at the edge nodes rather at the cloud core processors, thus accounting for reduction of load overhead on cloud core processors. This research paper proposes a Two Stage Load Optimizer (TSLO) implemented as a double stage optimizer with one being deployed at FoG level and the other at the Cloud level. The computational complexity analysis is extensively done and compared with existing benchmark methods and superior performance of the suggested method is observed and reported.

Microservice-Based Approach to Simulating Environmentally Friendly Equipment of Infrastructure Objects Taking into Account Meteorological Data

Nowadays, simulation modeling is a relevant and practically significant means in the field for research of infrastructure object functioning. It forms the basis for studying the most important components of such objects represented by their digital twins. Applying meteorological data, in this context, becomes an important issue. In the paper, we propose a new microservice-based approach for organizing simulation modeling in heterogeneous distributed computing environments. Within the proposed approach, all operations related to data preparing, executing models, and analyzing the obtained results are implemented as microservices. The main advantages of the proposed approach are the parameter sweep computing within simulation modeling and possibility of integrating resources of public access supercomputer centers with cloud and fog platforms. Moreover, we provide automated microservice web forms using special model specifications. We develop and apply the service-oriented tools for studying environmentally friendly equipment of the objects at the Baikal natural territory. Among such objects are recreation tourist centers, children’s camps, museums, exhibition centers, etc. As a result, we have evaluated the costs for the possible use of heat pumps in different operational and meteorological conditions for the typical object. The provided comparative analysis has confirmed the aforementioned advantages of the proposed approach.

DeepFogSim: A Toolbox for Execution and Performance Evaluation of the Inference Phase of Conditional Deep Neural Networks with Early Exits Atop Distributed Fog Platforms

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox.

Influence of Montoring: Fog and Edge Computing

The evolution of the Internet of Things (IoT) has augmented the necessity for Cloud, edge and fog platforms. The chief benefit of cloud-based schemes is they allow data to be collected from numerous services and sites, which is reachable from any place of the world. The organizations will be benefited by merging the cloud platform with the on-site fog networks and edge devices and as result, this will increase the utilization of the IoT devices and end users too. The network traffic will reduce as data will be distributed and this will also improve the operational efficiency. The impact of monitoring in edge and fog computing can play an important role to efficiently utilize the resources available at these layers. This paper discusses various techniques involved for monitoring for edge and fog computing and its advantages. The paper ends with a case study to demonstarte the need of monitoring in fog and edge in the healthcare system.

EiF: Toward an Elastic IoT Fog Framework for AI Services

The first generation of IoT was developed and deployed all over the world by connecting devices with common functionalities that were not sufficiently efficient or reliable for use in dynamic situations that require adaptive solutions. However, these fundamental IoT functions and services mainly targeted stable environments; there is consequently a strong need for the next generation of IoT services to be smarter, faster, and more reliable. We believe that the hyper-connected IoT ecosystem on fog platforms with contextual AI technologies is a promising solution. In this work, we introduce the EiF, a flexible fog computing framework that runs on IoT gateways with adaptive AI services fostered on the cloud. Our approach can be viewed as an integration of three emerging technologies, namely IoT, fog, and AI. Generally, EiF virtualizes an IoT service layer platform for fog nodes, and provides functions to manage and orchestrate various fog nodes; upon service virtualization and orchestration, AI services are fostered within both the federated cloud and distributed edge side and are deployed on fog nodes. We demonstrate the feasibility of EiF via the example of intelligent traffic flow monitoring and management.

D2D Communications for Large-Scale Fog Platforms: Enabling Direct M2M Interactions

To many, fog computing is considered the next step beyond the current centralized cloud that will support the forthcoming Internet of Things (IoT) revolution. While IoT devices will still communicate with applications running in the cloud, localized fog clusters will appear with IoT devices communicating with application logic running on a proximate fog node. This will add proximity-based machine-to-machine (M2M) communications to standard cloud-computing traffic, and it calls for efficient mobility management for entire fog clusters and energy-efficient communication within them. In this context, long-term evolution-advanced (LTE-A) technology is expected to play a major role as a communication infrastructure that guarantees low deployment costs, native mobility support, and plug-andplay seamless configuration.