Edge Cloud Environment Research Articles

A Task Orchestration Strategy in a Cloud-Edge Environment Based on Intuitionistic Fuzzy Sets

In the context of the burgeoning cloud-edge collaboration paradigm, powered by advancements in the Internet of Things (IoT), cloud computing, and 5G technology, this paper proposes a task orchestrating strategy for cloud-edge collaborative environments based on intuitionistic fuzzy sets. The proposed strategy prioritizes efficient resource utilization, minimizes task failures, and reduces service time. First, WAN bandwidth, edge server virtual machine utilization, delay sensitivity of the task, and task length are used to determine whether the task should be executed on the cloud or edge device. Then, the cloud-edge collaborative decision-making algorithm is used to select the task’s target edge servers (either the local edge servers or the neighboring edge servers). Finally, simulation experiments are conducted to demonstrate the effectiveness and efficacy of the proposed algorithm.

Collaborative Cloud-Edge-End Task Offloading in MEC-Based Small Cell Networks With Distributed Wireless Backhaul

Collaborative cloud-edge-end computing is a promising solution to support computation-intensive and latency-sensitive tasks by utilizing rich computing resources of cloud datacenters and low access delay of mobile edge computing (MEC) servers. Compared with traditional cloud computing and MEC, the cloud-edge environment has a stronger heterogeneity of servers and networks, resulting in significant differences between servers in the computation speed and access delay. However, few studies on cloud-edge-end task offloading focused on the characteristic of 5G heterogeneous networks in the cloud-edge environment. In this paper, we study the task offloading problem for collaborative cloud-edge-end computing in MEC-enabled small cell networks with low-cost distributed wireless backhaul. We aim to minimize the energy consumption of all user devices (UDs) via jointly optimizing the offloading decision, UDs’ transmission power, and the allocation of spectrum and computation resources. To solve the non-convex problem, we decouple the original problem into three subproblems, and design an efficient method with solving these three subproblems iteratively to obtain a high-quality solution. The simulation results indicate that our proposed method can lead to significant reduction in the energy consumption of all UDs compared with other conventional methods.

Reliable and efficient webserver management for task scheduling in edge-cloud platform

<span lang="EN-US">The development in the field of cloud webserver management for the execution of the workflow and meeting the quality-of-service (QoS) prerequisites in a distributed cloud environment has been a challenging task. Though, internet of things (IoT) of work presented for the scheduling of the workflow in a heterogeneous cloud environment. Moreover, the rapid development in the field of cloud computing like edge-cloud computing creates new methods to schedule the workflow in a heterogenous cloud environment to process different tasks like IoT, event-driven applications, and different network applications. The current methods used for workflow scheduling have failed to provide better trade-offs to meet reliable performance with minimal delay. In this paper, a novel web server resource management framework is presented namely the reliable and efficient webserver management (REWM) framework for the edge-cloud environment. The experiment is conducted on complex bioinformatic workflows; the result shows the significant reduction of cost and energy by the proposed REWM in comparison with standard webserver management methodology.</span>

PMP: A partition-match parallel mechanism for DNN inference acceleration in cloud–edge collaborative environments

To address the challenges of delay-sensitive deep learning tasks, Deep Neural Network (DNN) models are often partitioned and deployed to the cloud–edge environment for parallel and collaborative inference. However, existing parallel coordination mechanisms are not suitable for the cloud–edge environment, as the high inter-layer dependence of DNNs can increase transmission latency and wait times for inference, which contradicts the advantage of low latency in edge computing. To resolve this contradiction, the PMP mechanism takes into account the inter-layer transfer dependence of partitioning solutions and employs a multi-objective equalization algorithm to derive DNN model partitioning strategies suitable for multi-way parallel computing. Moreover, the mechanism establishes a DNN inference time prediction model based on these partitions and utilizes an iterative matching algorithm to approximate an optimal DNN inference workflow. Extensive evaluations of the proposed mechanism are conducted using various DNN models, and the results demonstrate its superiority over existing schemes, including local, CoEdge, and EdgeFlow. Notably, PMP achieves significant reductions in total inference latency compared to these schemes, with reductions of 80.9%, 37.9%, and 9.1%, respectively.

CNN Partitioning and Offloading for Vehicular Edge Networks in Web3

Web3, an emerging blockchain-based decentralized network, grants users ownership and enhances the collaboration among devices under monitoring. Benefiting from decentralization and in-memory computing, vehicular edge networks can process tasks such as road object detection distributedly without being attacked. Recently, to provide intelligent service for Web3 users, artificial intelligence applications have been booming, thus generating enormous deep learning models. These models are supposed to be deployed in the edge due to their massive computation. Furthermore, edge servers may face overload and intolerable delay for the high concurrency of offloaded deep learning tasks. How to determine an optimal offloading decision in the highly dynamic and heterogeneous edge-cloud environment is still a challenge. To tackle the mentioned challenge, a dynamic offloading strategy based on game theory combined with convolutional neural network (CNN) partition for vehicular edge networks, named GPOV, is proposed. Specifically, CNN partition can utilize resources more efficiently and reduce the delay with parallelism. The game theoretic offloading decision strategy can determine the optimal offloading policy according to the realtime environment. The performance of our strategy is validated in the final part of this paper.

Security Energy Efficiency Analysis of CR-NOMA Enabled IoT Systems for Edge-cloud Environment

With the development of the Internet of Things (IoT), more and more devices are connected in edge-cloud environment, and spectrum scarcity has become a major bottleneck for the employment of IoT devices in the end side of cloud computing. In addition, due to the broadcast nature of RF link, the potential eavesdropper may be presented in the wireless environment. To improve spectrum efficiency (SE) and ensure safe transmission of the users, in this paper, the two-way relay cooperative Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) with massive multiple-in multiple-out (MIMO) is proposed. Our objective is to maximize the security energy efficiency (SEE) of the cognitive system subject to the quality of service (QoS) of users. Specifically, the multi-objective optimization problem is decomposed into three subproblems, i.e., the optimization of transmit power, power allocation, and antenna selection, respectively. The Lagrange dual algorithm based on the first-order Taylor series expansion function is proposed to solve the non-convex problem. Moreover, a novel orthogonal antenna selection algorithm is proposed to decrease the cost of radio frequency chains and limit the interference to the primary user. The simulation results show that the proposed scheme has significant performance gain on SEE. It can effectively increase the number of access for cloud computing edge IoT users. In addition, due to the impact of correlation between the antennas, as the number of relay antennas increases, the SEE converges to a specific value, which exists the optimal number of antenna.

Enhanced multi-objective gorilla troops optimizer for real-time multi-user dependent tasks offloading in edge-cloud computing

This research paper investigates Mobile Edge Computing (MEC) networks, which involve numerous wireless devices transferring their computation tasks to various edge servers and a single cloud server. The study considers various real-time dependent computation tasks composing an application running on each connected device. This study aims to determine whether to process each user task locally on the device or to offload and process it on one of the nearby MEC servers or the central cloud server. Despite the continued efforts to address the issue of task offloading, it remains a difficult area of research, especially in the edge-cloud environment. Improving the Quality of Service (QoS) and achieving optimized performance and resource utilization are still necessary goals. New approaches either focus on a single objective, are computationally demanding, depend only on the cloud or MEC for offloading, not both or disregard the interdependence between tasks. This paper proposes an enhanced multi-objective version of the Gorilla Algorithm (EMGA) for offloading interdependent tasks in edge-cloud environments, with three main objectives: 1-reducing the application's execution latency, 2-lowering energy consumption for connected devices, and 3-minimizing imposed cost for using servers' resources. The proposed approach assumes that each MEC and cloud server can support multiple cost levels to increase system flexibility. A customized mutation operation was introduced in EMGA, which extends the functionality of the Standard Gorilla algorithm to improve search strategies. Comparative analyses were conducted between EMGA and other optimizers in the problem domain, and the results indicate the superiority of EMGA.

A Secure Data-Sharing Scheme for Privacy-Preserving Supporting Node–Edge–Cloud Collaborative Computation

The node–edge–cloud collaborative computation paradigm has introduced new security challenges to data sharing. Existing data-sharing schemes suffer from limitations such as low efficiency and inflexibility and are not easily integrated with the node–edge–cloud environment. Additionally, they do not provide hierarchical access control or dynamic changes to access policies for data privacy preservation, leading to a poor user experience and lower security. To address these issues, we propose a data-sharing scheme using attribute-based encryption (ABE) that supports node–edge–cloud collaborative computation (DS-ABE-CC). Our scheme incorporates access policies into ciphertext, achieving fine-grained access control and data privacy preservation. Firstly, considering node–edge–cloud collaborative computation, it outsources the significant computational overhead of data sharing from the owner and user to the edge nodes and the cloud. Secondly, integrating deeply with the “node–edge–cloud” scenario, the key distribution and agreement between all entities embedded in the encryption and decryption process, with a data privacy-preserving mechanism, improve the efficiency and security. Finally, our scheme supports flexible and dynamic access control policies and realizes hierarchical access control, thereby enhancing the user experience of data sharing. The theoretical analysis confirmed the security of our scheme, while the comparison experiments with other schemes demonstrated the practical feasibility and efficiency of our approach in node–edge–cloud collaborative computation.

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

Edge-cloud applications are rapidly prevailing in recent years and pose the challenge of using both resource-strenuous edge devices and elastic cloud resources under dynamic workloads. Efficient resource allocation on edge-cloud jobs via cluster schedulers (e.g. Kubernetes/Volcano scheduler) is essential to guarantee their performance, e.g. tail latency, and such allocation is sensitive to scheduler configurations such as applied scheduling algorithms and task restart/discard policy. Deep reinforcement learning (DRL) is increasingly applied to optimize scheduling decisions. However, DRL faces the conundrum of achieving high rewards at a dauntingly long training time (e.g. hours or days), making it difficult to tune the scheduler configurations online in accordance to dynamically changing edge-cloud workloads and resources. For such an issue, this paper proposes EdgeTuner, a fast scheduler configuration tuning approach that efficiently leverages DRL to reduce tail latency of edge-cloud jobs. The enabling feature of EdgeTuner is to effectively simulate the execution of edge-cloud jobs under different scheduler configurations and thus quickly estimate these configurations’ influence on job performance. The simulation results allow EdgeTuner to timely train a DRL agent in order to properly tune scheduler configurations in dynamic edge-cloud environment. We implement EdgeTuner in both Kubernetes and Volcano schedulers and extensively evaluate it on real workloads driven by Alibaba production traces. Our results show that EdgeTuner outperforms prevailing scheduling algorithms by achieving much lower tail latency while accelerating DRL training speed by an average of 151.63x.

Survey on Multiple Methods for Task Offloading in Mobile Cloud-Edge Environment

Mobile edge computing (MEC) is an emerging technology that extends cloud computing to the edge of the network. It offloads computing intensive tasks to the edge server to solve the problem of insufficient computing power and resource of the terminal device, meets the low energy consumption and low latency requirements of the mobile user for application task computing, and greatly releases the pressure of the cloud center. Cloud-edge integration architecture has become a trend. How to perform optimal offload scheduling for terminal tasks has always been one of the key issue in the field of MEC research. This paper reviews the research directions and achievements of task offloading technology in today’s cloud-edge environment. First, the development status of MEC environment and task offloading technology are introduced, and the concepts and applications of classic schemes such as heuristic algorithm, metaheuristic algorithm, and reinforcement learning are elaborated. Based on the mainstream solutions in the research literature, two types of task offloading solutions are summarized: traditional task offloading solutions based on algorithm optimization and interactive task offloading solutions based on reinforcement learning. Second, the research literature on the two mainstream directions mentioned above is summarized and organized from aspects such as problem constraints, optimization objectives, and contributions made. Meanwhile, schemes are compared through the classic algorithms and innovations they used. Finally, the current challenges and future research objectives in this field are summarized to provide reference and help for the follow-up research.

Verifiable outsourcing EMRs scheme with attribute-based encryption in cloud-edge environments

With emerging of cloud and edge computing, it is popular to store Electronic Medical Records (EMRs) on a remote cloud. Furthermore, to share the EMRs stored in the cloud, attribute-based encryption (ABE) can provide a fine-grained access control strategy. However, in ABE, the finer the access control is, the bigger the number of required attributes is. Accordingly, it requires the higher computation costs. In this paper, to reduce the burden of patients, we first present a verifiable outsourced ABE scheme with the help of edge servers and fog nodes, in which edge servers are fully responsible for the encryption of ABE while fog nodes assist data users to perform the corresponding decryption. Furthermore, to protect the privacy of data owner, we introduced the proxy re-encryption algorithms, which still can achieve dual access control for EMRs. What's more, to detect the honesty of edge servers and the cloud, we adopt an aggregated digital signature to guarantee the correctness of the EMRs initially encrypted by data owner. The experimental results and security proofs show that our scheme has higher computational efficiency and achieves more fine-grained access control.

Resource Utilization of Distributed Databases in Edge–Cloud Environment

A benchmark study of modern distributed databases (e.g., Cassandra, MongoDB, Redis, and MySQL) is an important source of information for selecting the right technology for managing data in edge-cloud deployments. While most of the existing studies have investigated the performance and scalability of distributed databases in cloud computing, there is a lack of focus on resource utilization (e.g., energy, bandwidth, and storage consumption) of workload offloading for distributed databases deployed in edge-cloud environments. For this purpose, we conducted experiments on various physical and virtualized computing nodes including variously powered servers, Raspberry Pi, and hybrid cloud (OpenStack and Azure). Our extensive experimental results reveal insights into which database under which offloading scenario is more efficient in terms of energy, bandwidth, and storage consumption.

Dynamic Multiworkflow Offloading and Scheduling Under Soft Deadlines in the Cloud-Edge Environment

Computing offloading and scheduling are emerging, accompanied by smart city services, Internet of Things, and other rich cloud services. Cloud-assisted mobile edge computing provides malleable resources but introduces considerable overhead. Due to the geographical distributions and distances of the edge, cloud, and mobile terminal, promoting cost efficiency and reducing the makespan of applications are urgent targets. For multiple mobile applications, the soft deadline constraint for each application is common in the edge-cloud environment. When multiple users initiate application requests, scheduling on the target offloaded platform commences. Some workflows, especially newly arrived workflows, might suffer from “starving to death,” which has the risk of being missed. This article describes a dynamic, multiworkflow offloading, and scheduling, adaptive heterogeneous earliest-finish-time-based algorithm, which can lightly run on a distributed network. We explore an innovative model composed of a multiobjective makespan and cost for a soft deadline constraint optimization problem. Abundant data are adopted to test the offloading and scheduling effect on a variety of platform parameters, and comparative experiments demonstrate the validity and efficiency of the adaptive system.

Task offloading optimization mechanism based on deep neural network in edge-cloud environment

With the rise of edge computing technology and the development of intelligent mobile devices, task offloading in the edge-cloud environment has become a research hotspot. Task offloading is also a key research issue in Mobile CrowdSourcing (MCS), where crowd workers collect sensed data through smart devices they carry and offload to edge-cloud servers or perform computing tasks locally. Current researches mainly focus on reducing resource consumption in edge-cloud servers, but fails to consider the conflict between resource consumption and service quality. Therefore, this paper considers the learning generation offloading strategy among multiple Deep Neural Network(DNN), proposed a Deep Neural Network-based Task Offloading Optimization (DTOO) algorithm to obtain an approximate optimal task offloading strategy in the edge-cloud servers to solve the conflict between resource consumption and service quality. In addition, a stack-based offloading strategy is researched. The resource sorting method allocates computing resources reasonably, thereby reducing the probability of task failure. Compared with the existing algorithms, the DTOO algorithm could balance the conflict between resource consumption and service quality in traditional edge-cloud applications on the premise of ensuring a higher task completion rate.

UPOA: A User Preference Based Latency and Energy Aware Intelligent Offloading Approach for Cloud-Edge Systems

Task offloading has been widely used to extend the battery life of intelligent mobile devices. Existing task offloading approaches, focusing on perfecting the balance between latency and energy consumption, completely ignore the impacts of the user preference caused by low battery anxiety. The existence of low battery anxiety - mobile users&#x0027; common fear of losing battery energy, especially when the battery energy is already low - causes users to trade high latency for prolonged battery life. Taking into account the user preference impacts on task offloading, we propose a novel offloading approach called <i>UPOA</i> to obtain refined offloading policies between low latency and energy consumption based on user preferences. In UPOA, we start the study by defining a user preference rule that determines users&#x2019; offloading preferences according to battery energy status. Then, we build a fine-grained task offloading model to delineate the task distribution characteristics of each node in its offloading link. Guided by this model, we develop a task prediction algorithm based on the long-short-term-memory neural network model to provide task predictions that facilitate offloading policies. Lastly, we implement a particle-swarm-optimization-based online offloading algorithm. The offloading algorithm provides the best long-term offloading policies by incorporating the user preference determined by our user preference rule and the task predictions generated by our task prediction algorithm. To quantitatively evaluate the performance of UPOA, we conduct extensive experiments in a real-world cloud-edge environment. We compare UPOA with three state-of-the-art offloading approaches, DRA, DRL-E2D, and MUDRL under various conditions. Experimental results demonstrate that UPOA can make effective policies based on user preferences compared with the existing approaches. UPOA reduces average latency by 12.49&#x0025; when battery energy is sufficient and extends battery life by 20.14&#x0025; when battery energy is low.

Optimized scheduling with prioritization to enhance network sustainability in edge-cloud environment

Due to decentralized infrastructure in modern Internet-of-Things (IoT), the tasks should be shared around the edge devices via network resources and traffic prioritizations, which weaken the information interoperability. To solve this issue, a Minimized upgrading batch Virtual Machine (VM) Scheduling and Bandwidth Planning (MSBP) was adopted to reduce the amount of batches needed to complete the system-scale upgrade and allocate the bandwidth for VM migration matrices. But, the suboptimal use of VM and possible loss of tasks may provide inadequate Resource Allocation (RA). Hence, this article proposes an MSBP with the Priority-based Task Scheduling (MSBP-PTS) algorithm to allocate the tasks in a prioritized way and maximize the profit by deciding which request must handle by the edge itself or send to the cloud server. At first, every incoming request in its nearest fog server is allocated and processed by the priority scheduling unit. Few requests are reallocated to other fog servers when there is an inadequate resource accessible for providing the request within its time limit. Then, the request is sent to the cloud if the fog node doesn’t have adequate resources, which reduces the response time. However, the MSBP is the heuristics solution which is complex and does not ensure the global best solutions. Therefore, the MSBP-PTS is improved by adopting an Optimization of RA (MSBP-PTS-ORA) algorithm, which utilizes the Krill Herd (KH) optimization instead of heuristic solutions for RA. The simulation outcomes also demonstrate that the MSBP-PTS-ORA achieve a sustainable network more effectively than other traditional algorithms.

Security-Aware and Time-Guaranteed Service Placement in Edge Clouds

Most of the emerging applications such as the Deep Neural Networks (DNN) based smart Internet of Things (IoT) systems need intensive and high-performance computing, which is contradictory to the limited resources of IoT/terminal devices. It is a big challenge to offload all tasks to the cloud due to the bandwidth limitation, processing overhead, and transmission costs. Edge computing as an extension of cloud computing that can provide abundant computing resources near the edge of the network and thereby can potentially improve the QoS of applications. However, offloading tasks to the edge servers is liable to external security threats. How to balance the response time and the security of application services is a big challenge for realizing good application service placement. This paper proposes a time and security efficient task scheduling framework in the edge-cloud environment. The corresponding computing models are established, such as the security-related model, time model, and the risk probability model. Then, a time-guaranteed and security-aware task scheduling algorithm is proposed including the domain construction, the security-aware task ranking, and the task dispatching. Extensive simulation experiments have been conducted. Results show that the proposed method has better performance than the other four compared methods in general.

Privacy-Preserving Microservices in Industrial Internet-of-Things-Driven Smart Applications

Machine learning (ML) algorithms can effectively perform analytics and inferences for building smart applications, such as early detection of diseases in the Industrial Internet of Things (IIoT) and smart healthcare systems. The main components of ML, including training and testing phases, can be decomposed into microservices to improve service quality, along with fast implementation and integration with the edge and cloud services. However, the execution of ML in an edge-cloud environment introduces privacy risks to data owners (e.g., patients). In this article, we present a privacy-preserving ML framework by leveraging microservice technology for safeguarding healthcare IIoT systems. More specifically, we develop a microservice-based distributed privacy-preserving technique using differential privacy (DP) and a radial basis function network (RBFN) to balance between privacy protection and model performance in edge networks. We conduct extensive experiments to evaluate the performance of the proposed technique. The results revealed that DP has a significant influence on the model’s performance and achieves more than 90% accuracy with an epsilon value over 0.4, enhancing data protection and analytics through the implementation of microservices.

A Hybrid Edge-Cloud System for Networking Service Components Optimization Using the Internet of Things

The need for data is growing steadily due to big data technologies and the Internet’s quick expansion, and the volume of data being generated is creating a significant need for data analysis. The Internet of Things (IoT) model has appeared as a crucial element for edge platforms. An IoT system has serious performance issues due to the enormous volume of data that many connected devices produce. Potential methods to increase resource consumption and responsive services’ adaptability in an IoT system include edge-cloud computation and networking function virtualization (NFV) techniques. In the edge environment, there is a service combination of many IoT applications. The significant transmission latency impacts the functionality of the entire network in the IoT communication procedure because of the data communication among various service components. As a result, this research proposes a new optimization technique for IoT service element installation in edge-cloud-hybrid systems, namely the IoT-based Service Components Optimization Model (IoT-SCOM), with the decrease of transmission latency as the optimization aim. Additionally, this research creates the IoT-SCOM model and optimizes it to choose the best deployment option with the least assured delay. The experimental findings demonstrate that the IoT-SCOM approach has greater accuracy and effectiveness for the difficulty of data-intensive service element installation in the edge-cloud environment compared to the existing methods and the stochastic optimization technique.

An intelligent model for supporting edge migration for virtual function chains in next generation internet of things

The developments on next generation IoT sensing devices, with the advances on their low power computational capabilities and high speed networking has led to the introduction of the edge computing paradigm. Within an edge cloud environment, services may generate and consume data locally, without involving cloud computing infrastructures. Aiming to tackle the low computational resources of the IoT nodes, Virtual-Function-Chain has been proposed as an intelligent distribution model for exploiting the maximum of the computational power at the edge, thus enabling the support of demanding services. An intelligent migration model with the capacity to support Virtual-Function-Chains is introduced in this work. According to this model, migration at the edge can support individual features of a Virtual-Function-Chain. First, auto-healing can be implemented with cold migrations, if a Virtual Function fails unexpectedly. Second, a Quality of Service monitoring model can trigger live migrations, aiming to avoid edge devices overload. The evaluation studies of the proposed model revealed that it has the capacity to increase the robustness of an edge-based service on low-powered IoT devices. Finally, comparison with similar frameworks, like Kubernetes, showed that the migration model can effectively react on edge network fluctuations.