Prediction-Based Resource Deployment and Task Scheduling in Edge-Cloud Collaborative Computing

The real-time communication requirement of the Internet of Things (IoT) applications promotes the convergence of IoT and Mobile Edge Computing (MEC). The MEC paradigm greatly shortens the IoT service delay by leveraging cloudlets (edge servers) of MEC in the proximity of IoT devices. Considering limited computing and storage resources in an MEC network, it is challenging to provide efficient IoT-enabled service provisioning in such a network. In this article, we study the service home identification problem of service provisioning for multi-source IoT applications in an MEC network, by identifying a service home (cloudlet) of each multi-source IoT application for its data processing, querying and storage. Each multi-source IoT application consists of multiple sources located at different geographical locations and each source uploads its data stream via a gateway (its nearby access point) to the MEC network and the uploaded data then is aggregated with the stream data of the other sources of the IoT application at the service home. We here focus on two novel service home identification problems: the service operational cost minimization problem with the aim to minimize the total service operational cost by admitting as many multi-source IoT applications as possible, and the online throughput maximization problem with the aim to maximize the number of multi-source IoT application requests admitted. We first show that both the problems are NP-hard. We then formulate an Integer Linear Programming (ILP) solution to the service operational cost minimization problem, and propose a randomized algorithm with high probability and a deterministic approximation algorithm respectively, at moderate resource capacity violations. We third develop an efficient heuristic algorithm for the problem without any resource violation. Furthermore, we deal with the online throughput maximization problem under an assumption that multi-source IoT application requests arrive one by one without the knowledge of future arrivals, for which we formulate an Integer Linear Programming (ILP) solution to its offline version, followed by devising an online algorithm with competitive ratio. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising, and outperform their comparison counterparts.

A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

The enormous growth in Internet of Things (IoT) has caused large-scale transformation in data acquisition and communication mechanism for conventional IoT systems. The continuously increasing requirements for delay-tolerant delivery of services in IoT applications has led to the emergence of more scalable and energy-efficient computing platforms like edge computing. However, the massive growth in volume of data being offloaded from low-powered IoT devices to the edge has imposed challenges on edge servers in terms of traffic bottlenecks, latency, and wastage of energy. In this view, a Local Data Reduction (LDR) framework is proposed which addresses the latency issues and cost constraints to facilitate energy-efficient processing of IoT data. We exploit the Markovian birth-death process to model edge-based IoT systems and derive performance metrics for the proposed LDR model. We also provide explicit analytical solution for the total expected cost function incurred pertaining to the LDR and without LDR (WLDR) models. Through extensive numerical illustrations we validate our findings and observe that the proposed LDR model outperforms the WLDR model. Hence, the LDR model operates well to meet the Quality of Service (QoS) requirements for real-time IoT systems by favouring green computing paradigms.

The rise of successful cutting-edge Internet of Things (IoT) applications for urban development has inspired the industry and research community. Although the industry verticals such as automotive, robotics, e-health, and entertainment applications create new business opportunities for the service providers, they pose challenges in terms of deployment cost, reliability, and latency requirements. For example, self-driving vehicles require ultralow latency and reliable data processing to make split-second decisions. Unfortunately, cloud-based solutions for such IoT applications are not suitable due to the end-to-end latency and connection reliability. To meet these challenging requirements, edge computing paradigm has emerged as a solution where computational resources are brought to the proximity of the end users. Now, advanced data analytics, machine learning, and cognitive techniques can also be deployed at the edge of the network. Together, edge computing, data analytics, and machine learning empower service providers with true and intelligently automated infrastructure for IoT applications. In this chapter, we investigate the relevant use cases of edge-enabled IoT applications for smart urban development. To this end, we also provide a comprehensive study of the recent trends and the state of the art in accommodating all these emerging technologies in the acceleration of smart urban development.

With the growing prevalence of Internet of Things (IoT) devices and technology, a burgeoning computing paradigm namely mobile edge computing (MEC) is delicately proposed and designed to accommodate the application requirements of IoT scenario. In this paper, we focus on the problems of dynamic task scheduling and resource management in MEC environment, with the specific objective of achieving the optimal revenue earned by edge service providers. While the majority of task scheduling and resource management algorithms are formulated by an integer programming (IP) problem and solved in a dispreferred NP-hard manner, we innovatively investigate the problem structure and identify a favorable property namely totally unimodular constraints. The totally unimodular property further helps to design an equivalent linear programming (LP) problem which can be efficiently and elegantly solved at polynomial computational complexity. In order to evaluate our proposed approach, we conduct simulations based on real-life IoT dataset to verify the effectiveness and efficiency of our approach.

Outdoor Internet of Things (IoT) applications usually exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices. In this context, we propose a new dynamic programming algorithm for the optimization of the scheduling of the tasks in IoT devices that harvest energy by means of a solar panel. We show that the problem is NP-hard and that the algorithm finds the optimum solution in a pseudo-polynomial time. Furthermore, we show that the algorithm can be executed with a small overhead on three popular IoT platforms (namely TMote, Raspberry PI, and Arduino) and, by simulation, we show the behavior of the algorithm with different settings and at different conditions of energy production.

Internet of Things (IoT) applications are generally developed in a vertical manner, i.e., each IoT application is developed for a certain scenario which generally does not share data with IoT applications for recommendation services. This leads to an inefficient exploitation of other IoT service applications. In fact, such recommendation services can be achieved with the help of Social IoT (SIoT) by using data generated by various IoT applications. SIoT builds a profile of objects based on IoT applications data that can be exchanged with SIoT network for accessible to other IoT applications. In this manner, the SIoT network provides recommendation services for reusability of IoT applications' data among various IoT applications, as well as adapting IoT services according to users' needs which improve user experience. Additionally, the profiles built by a SIoT network can also help a single IoT application by looking for similar conditions that have been addressed in the past for the same IoT application. We propose a concept for exploiting the SIoT for recommendation services among various IoT applications with the help of a sample application scenario that highlights how the SIoT can help by providing recommendations. We also provide some implementation challenges for this concept.

In the context of Edge Computing (EC) and Internet of Things (IoT), numerous tasks are offloaded from mobile users and sensor devices to edge nodes for further processing to reduce delay and solve the problem of insufficient local computation resources. These tasks can be mainly divided into delay-sensitive and computation-oriented tasks. The former tasks depend on the service provided by the container, while the latter tasks are submitted as a batch with task dependencies. Considering the heterogeneity of edge nodes, joint task scheduling can effectively improve resource utilization. However, relatively few researches consider the different characteristics of tasks like container constraints and task dependencies in joint task scheduling in EC. In order to fill in this gap, we propose a deep deterministic policy gradient (DDPG) based online joint task scheduling (OJTS) algorithm. Specifically, 1) We first model the problem of joint scheduling of delay-sensitive and computation-oriented tasks in resource-constrained EC scenario with the goals of maximizing system utility and minimizing system cost (weighted sum of the number and duration of unfinished tasks). 2) Then, we propose a deep reinforcement learning (DRL) algorithm to solve the above problem and make appropriate adjustments to the original network structure according to the scheduling decision. 3) Through validation on real-world trace, OJTS can improve the system utility by 26.0% and overall reward by 51.2% compared with baselines and meet real-time decision-making requirements.

SAAS parallel task scheduling based on cloud service flow load algorithm

With the rapid increase of Internet of Things (IoT) devices and applications, the ordinary cloud computing paradigm soon becomes outdated. Fog computing paradigm extends services provided by a cloud to the edge of network in order to satisfy requirements of IoT applications such as low latency, locality awareness, low network traffic, mobility support, and so forth. Task scheduling in a Cloud-Fog environment plays a great role to assure diverse computational demands are met. However, the quest for an optimal solution for task scheduling in the such environment is exceedingly hard due to diversity of IoT applications, heterogeneity of computational resources, and multiple criteria. This study approaches the task scheduling problem with aims at improving service quality and load balancing in a merged computing system of Edge-Fog-Cloud. We propose a Multi-Objective Scheduling Algorithm (MOSA) that takes into account the job characteristics and utilization of different computational resources. The proposed solution is evaluated in comparison to other existing policies named LB, WRR, and MPSO. Numerical results show that the proposed algorithm improves the average response time while maintaining load balancing in comparison to three existing policies. Obtained results with the use of real workloads validate the outcomes.

The correct implementation and behavior of Internet of Things (IoT) applications are seldom investigated in the literature. This paper shows how the simulation mechanism can be integrated well into an IoT application development platform for correct implementation and behavior investigation. We use an IoT application development platform called IoTtalk as an example to describe how the simulation mechanism called SimTalk can be built into this IoT platform. We first elaborate on how to implement the simulator for an input IoT device (a sensor). Then we describe how an output IoT device (an actuator) can be simulated by an animated simulator. We use a smart farm application to show how the simulated sensors are used for correct implementation. We use applications including interactive art (skeleton art and water dance) and the pendulum physics experiment as examples to illustrate how IoT application behavior investigation can be achieved in SimTalk. As the main outcome of this paper, the SimTalk simulation codes can be directly reused for real IoT applications. Furthermore, SimTalk is integrated well with an IoT application verification tool in order to formally verify the IoT application configuration. Such features have not been found in any IoT simulators in the world.

Introduction: Nowadays, fog computing has emerged as a promising solution for handling the prompt processing of tasks in Internet of Things (IoT)-based applications. One of the key advantages of fog computing is that it reduces service completion time by offloading tasks from IoT devices to the fog server. Therefore, scheduling of tasks becomes vital, where emergency and non-emergency tasks can be prioritized to offload data to the nearby fog servers, which improves the Quality of Service (QoS). Due to the dynamic nature of the IoT environment, traffic load varies over time, making it difficult to select the optimal fog server for task offloading. Objectives: This research introduces a novel task offloading for the Fog Cloud scenario using the improved Coati Optimization Algorithm based on Genetic Operators (ICOA-GO) algorithm. Methods: Initially, the Delay aware Four Queue model with Fuzzy logic (DFQM-Fuzz) is designed to queue incoming tasks into four different priorities. In the DFQM-Fuzz, Highly urgent (HU) and Urgent (U) tasks are considered as first and second priorities and those tasks are offloaded through the Fog Server. The Non Urgent (NU) and No Deadline (ND) tasks are offloaded through the Cloud Server. Furthermore, when there is no resource to offload through the Fog Server, the task is offloaded through the Cloud Server. Results: The proposed improved Coati Optimization Algorithm based on Genetic Operators (ICOA-GO) algorithm optimizes the selection of Cloud and Fog Servers. The analysis based on Energy Utilization, Task Rejection Ratio, and Makespan yielded values of 93.589, 1, and 1.96073. Conclusions: By integrating the DFQM-Fuzz and ICOA-GO improves task prioritizing and offloading efficiency while reducing execution time, energy consumption, and cost.

As an extension of the cloud, a fog computing environment facilitates the deployment of Internet of Things (IoT) applications by shifting the computing, storage and networking services closer to the IoT devices, thus satisfying the delay and response time requirements. This paper aims to improve the overall task execution efficiency of IoT applications by appropriately selecting customized real-time tasks for execution at the fog layer. Specifically, we propose a fuzzy logic based task scheduling algorithm to divide the tasks between the fog and cloud layers in a fog-cloud computing framework. The algorithm selects appropriate processing units to execute the submitted tasks in the fog layer with heterogeneous resources, by exploiting the task requirements (e.g., computation, storage, bandwidth) and their constraints (e.g., deadline, data size). Simulation experiments demonstrate the efficacy of the proposed algorithm and its superior performance as compared to other existing algorithms in terms of success ratio of the tasks, makespan, average turnaround time, and delay rate.

Enhanced Dynamic Deep Q-Network for Federated Learning scheduling policies on IoT devices using explanation-driven trust

As a major agricultural province in China, Shandong Province has always played an important role. At the same time, it is also closely related to the strong support of science and technology. As a new technology, the Internet of things (IoT) has made positive contributions. Agricultural IoT is a mode that uses information technology, wireless communication technology and radio frequency automatic identification technology to share the identified agricultural information to the network. The application of agricultural IoT is a major change in agricultural development, which provides convenient conditions for the development of agriculture, provides a favorable environment for the agricultural products, promotes the transformation of agricultural technology, realizes the informatization of agricultural production, improves the efficiency of rural land, integrates agricultural resources and provides a foundation for food safety. In this paper, the application of IoT in the field of agriculture in Shandong Province is described from two practical cases. One is about how to use wireless sensor network technology to transmit data in the big data platform based on the Bohai granary project. The other describes how to use IoT for food quality and safety traceability. It takes pigs as the research object, runs through all links of the whole industrial chain of pig breeding, breeding, slaughtering, processing, logistics and sales, collects the data of the whole industrial chain with the help of IoT, and realizes the information tracking and traceability of the whole industrial chain. At the end of the paper, it discusses the countermeasures.