Edge Cloud Environment Research Articles

Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach

Edge Cloud (EC) empowers the beyond 5G (B5G) wireless networks to cope with large-scale and real-time traffics of Internet-of-Things (IoT) by minimizing the latency and providing compute power at the edge of the network. Due to a limited amount of resources at the EC compared to the back-end cloud (BC), intelligent resource management techniques become imperative. This paper studies the problem of multi-resource allocation (MRA) in terms of compute and wireless resources in an integrated EC and BC environment. Machine learning-based approaches are emerging to solve such optimization problems. However, it is challenging to adopt traditional discrete action space-based methods due to their high dimensionality issue. To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. TFLAN combines convolution, gated recurrent unit and attention layers together to mine local and long term temporal information from the task sequences for excellent function approximation. A novel heuristic-based priority experience replay (hPER) method is formulated to accelerate the convergence speed. Further, a pruning principle helps the TFLAN agent to significantly reduce the computational complexity and balance the load among base stations and servers to minimize the rejection-rate. Lastly, data parallelism technique is adopted for distributed training to meet the needs of a high-volume of IoT traffic in the EC environment. Experimental results demonstrate that the distributed training approach suites well to the problem scale and can magnify the speed of the learning process. We validate the proposed framework by comparing with five state-of-the-art RL agents. Our proposed agent converges fast and achieves up to 28% and 72% reduction in operational cost and rejection-rate, and achieves up to 32% gain in the quality of experience on average, compared to the most advanced DDPG agent.

Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

Reliability enhancement algorithm based on budget level in cloud-edge environments

Reliability enhancement algorithm based on budget level in cloud-edge environments

Reliability enhancement algorithm based on budget level in cloud-edge environments

Reliability enhancement algorithm based on budget level in cloud-edge environments

Attribute-Based Encryption With Blockchain Protection Scheme for Electronic Health Records

In medical scenarios supported by edge clouds, it is difficult for patients to truly gain ownership of their electronic health records (EHRs). However, it is easy for doctors to modify hospital data to deny incorrect treatment records, which makes it difficult to protect the rights of patients. To improve patient control over EHRs, an attribute-based encryption protection scheme named CEC-ABE for EHRs combined with a blockchain is proposed to protect EHRs in edge cloud environments. In this scheme, the agreement process between the patient and the hospital is completed before the ABE stage, and the treatment information, including the treatment time, treatment doctor and additional information, is confidentially transmitted through an encryption algorithm. By storing the uploaded encrypted data in the blockchain in the form of transaction records, the integrity of the data is guaranteed, which facilitates the traceability of EHR generation. Access to EHRs is controlled by the ABE scheme of the outsourced ciphertext policy, and fine-grained attribute revocation can be employed to ensure the security of the ciphertext. The CEC-ABE algorithm, CP-ABE algorithm and other algorithms are experimentally tested, and the computational cost of each stage of the algorithms and the computational delay of each role are compared. CEC-ABE can significantly improve performance in key generation, outsourced decryption and other stages. Compared with the algorithm whose performance is second only to CEC-ABE, it reduces the computational overhead by 1.73% and 5.2%. The results show that the overall comprehensive performance of the CEC-ABE algorithm is better than that of the other algorithms.

Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the edge-end. In this connection, a key issue is to determine when and where services should be deployed. Common service deployment strategies used to be static based on the rules defined at the design time. However, dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation, where the static service deployment solutions are not efficient. In this paper, we propose a dynamic service deployment mechanism based on the prediction of upcoming stream data. To effectively predict upcoming workloads, we combine the online machine learning methods with an online optimization algorithm for service deployment. A simulation-based evaluation demonstrates that, compared with those state-of-the art approaches, the approach proposed in this paper has a lower latency of stream processing.

Dynamic Bandwidth Slicing for Time-Critical IoT Data Streams in the Edge-Cloud Continuum

Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g., industrial control systems) that require time-critical communication guarantees. While edge computing can provide immediate analysis of streaming data from Internet of Things devices, those devices lack computing capabilities to guarantee reasonable performance for time-critical applications. To alleviate this critical problem, the prevalent trend is to offload these data analytic tasks from the edge devices to the cloud. However, existing offloading approaches are static in nature as they are unable to adapt varying workload and network conditions. To handle these issues, we present a novel distributed and quality of services based multilevel queue traffic scheduling system that can undertake semiautomatic bandwidth slicing to process time-critical incoming traffic in the edge-cloud environments. Our developed system shows a great enhancement in latency and throughput as well as reduction in energy consumption for edge-cloud environments.

Design of Secure Authentication Handover Protocol for Innovative Mobile Multimedia Services in 5G MEC Environments

<p>5G advanced technology has introduced innovative multimedia services, thus propelling the rise of the mobile multimedia market. While the ubiquity of mobile multimedia services has boosted customer convenience, it has also unlocked the way to possible security concerns that potentially damage users’ privacy and service providers’ properties. Several standards and researches have been proposed to counter these threats, notably those concentrating on authentication between users and service applications. Unfortunately, these studies are limited to address a new challenge, secure handover among distributed application functions in 5G Multi-access Edge Cloud (MEC) environments. Motivated by this, we present a handover authentication protocol with push-key and pull-key optimization options in 5G mobile multimedia application services. The proposed protocol provides mutual authentication, secure key exchange, confidentiality, and integrity. Furthermore, it also supports perfect forward secrecy, optimal handover, and anonymity, which were not previously considered in prior studies. While formal verification through BAN Logic and Scyther proved that the protocol is secure against attacks that violate the supported security requirements, comparative analysis of the proposed protocol against existing studies demonstrates that the protocol is simultaneously efficient.</p> <p> </p>

Deep Reinforcement Learning Based Resource Allocation Strategy in Cloud-Edge Computing System.

The rapid development of mobile device applications put tremendous pressure on edge nodes with limited computing capabilities, which may cause poor user experience. To solve this problem, collaborative cloud-edge computing is proposed. In the cloud-edge computing, an edge node with limited local resources can rent more resources from a cloud node. According to the nature of cloud service, cloud service can be divided into private cloud and public cloud. In a private cloud environment, the edge node must allocate resources between the cloud node and the edge node. In a public cloud environment, since public cloud service providers offer various pricing modes for users’ different computing demands, the edge node also must select the appropriate pricing mode of cloud service; which is a sequential decision problem. In this stydy, we model it as a Markov decision process and parameterized action Markov decision process, and we propose a resource allocation algorithm cost efficient resource allocation with private cloud (CERAI) and cost efficient resource allocation with public cloud (CERAU) in the collaborative cloud-edge environment based on the deep reinforcement learning algorithm deep deterministic policy gradient and P-DQN. Next, we evaluated CERAI and CERAU against three typical resource allocation algorithms based on synthetic and real data of Google datasets. The experimental results demonstrate that CERAI and CERAU can effectively reduce the long-term operating cost of collaborative cloud-side computing in various demanding settings. Our analysis can provide some useful insights for enterprises to design the resource allocation strategy in the collaborative cloud-side computing system.

Achieving fuzzy matching data sharing for secure cloud-edge communication

In this paper, we propose a novel fuzzy matching data sharing scheme named FADS for cloud-edge communications. FADS allows users to specify their access policies, and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously. Specifically, we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments. Then, we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies. If the matching succeeds, the data can be decrypted. Otherwise, nothing will be revealed. In addition, FADS allows users to dynamically specify the policy for each time, which is an urgent demand in practice. A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack (IND-CCA) in random oracle model against probabilistic polynomial-time (PPT) adversary, and the desirable security properties of privacy and authenticity are achieved. Extensive experiments provide evidence that FADS is with acceptable efficiency.

Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge–cloud computing environments

The wide-spread embracement and integration of Internet of Things (IoT) has inevitably lead to an explosion in the number of IoT devices. This in turn has led to the generation of massive volumes of data that needs to be transmitted, processed and stored for efficient interpretation and utilization. Edge computing has emerged as a viable solution which complements cloud thereby enabling the integrated edge–cloud paradigm to successfully satisfy the design requirements of IoT applications. A vast majority of existing studies have proposed scheduling frameworks for individual tasks and only very few works have considered the more challenging problem of scheduling complex workloads such as workflows across edge–cloud environments. Workflow scheduling is an NP hard problem in distributed infrastructures. It is further complicated when scheduling framework needs to coordinate workflow executions across resource constrained and highly distributed edge–cloud environments. In this work, we leverage Deep Reinforcement Learning for designing a workflow scheduling framework capable of overcoming the aforementioned challenges. Different from all existing works we have designed a novel hierarchical action space for promoting a clear distinction between edge and cloud nodes. Coupled with this a hybrid actor–critic based scheduling framework enhanced with proximal policy optimization technique is proposed to efficiently deal with the complex workflow scheduling problem in edge–cloud environments. Performance of the proposed framework was compared against several baseline algorithms using energy consumption, execution time, percentage of deadline hits and percentage of jobs completed as evaluation metrics. Proposed Deep Reinforcement Learning technique performed 56% better with respect to energy consumption and 46% with respect to execution time compared to time and energy optimized baselines, respectively. This was achieved while also maintaining the energy efficiency in par with the energy optimized baseline and execution time in par with the time optimized baseline. The results thus demonstrate the superiority of the proposed technique in establishing the best-trade off between the conflicting goals of minimizing energy consumption and execution time.

Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

Background : To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement. Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment). This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement. Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing. Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models. Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively. Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model. Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment. On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model. Keywords: Cloud Computing; EdgeCloud; DAPEEO; Energy Efficiency; Throughput; Cost

Service deployment design in latency-critical multi-cloud environment

The advent of virtualization-based infrastructure needed for 5G brought new challenges in utilizing the existing physical network infrastructure effectively. Designing physical routes between virtual network functions in a multi-cloud environment is usually handled as a maximum flow problem, separately considering latency requirements. This paper presents an algorithm designed for low latency edge-cloud environments, where results meet both bandwidth and latency requirements of the planned traffic in one step. The proposed algorithm is much faster compared to the wide-spread linear program based flow maximization approach as the incorporated filtering of route candidates by latency allows more efficient search. The paper demonstrates the operation of the algorithm by a detailed example.

DSM pricing method based on A3C and LSTM under cloud-edge environment

Demand-side management (DSM) could realize “peak cutting and valley filling” of power load and improve the stability and efficiency of power system. With the development of information systems, more smart devices are being deployed on the demand side. It has become a challenge for DSM service providers to take full use of the edge computing capacity and demand-side information to improve the accuracy of DSM decision-making, without revealing user privacy. This paper proposes a distributed DSM pricing method for service provider, based on asynchronous advantage actor-critic (A3C) algorithm and long short-term memory (LSTM) network under cloud-edge environment. The on-site utilization of user information is realized through distributed training and centralized decision-making structure of A3C algorithm. The training process is accelerated by LSTM based virtual environment, which greatly reduces the training cost of the algorithm. Case study results shows that the proposed method is able to make pricing decision for DSM service provider under cloud-edge environment. Moreover, through the combination of LSTM based virtual environment and A3C algorithm, the proposed method requires less historical data than other methods and improves the profit of service providers.

Comprehensive Study On EDGE-Cloud Collaborative Computing for Optimal Task Scheduling

In recent years, Cloud and edge computing have got much attention because of the ever-increasing demands. There are many future technologies and advantages for systems to move towards clouds based on information keep methods. This includes a simple IT substructure and administration, and an effective distant approach from any place in the global with the steady computer network connections and efficient cost that cloud engineering can give. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud edge based environment is resource management, which typically revolves around resource allocation, resource provisioning, task scheduling and improve performance. Aiming at the future problem of simulating service requests and optimal task scheduling during the operation of the cloud computing/edge computing environment, the real-time optimization scheduling technology of computing resources is studied, and elastic resource optimization scheduling is realized through data feature (quality) mining analysis, and collaborative resource management. Ensure that the simulation service quality meets the mission requirements and provide support. The main goal of this paper is to provide the better and deeper understanding regarding the scheduling approaches in the Edge-Cloud environment that covers the way in the scheduling approaches.

A wholistic optimization of containerized workflow scheduling and deployment in the cloud–edge environment

Digital Twin in Industry 4.0 utilizes Internet of Things (IoT) to collect real-life data and combine it with simulation models for product design and development. The simulation process can be executed as a workflow, consisting of tasks with precedence constraints. In a container-based workflow execution system, each task in the workflow is executed in a container within a virtual machine (VM). In this paper, a three-step scheduling model is proposed to combine scheduling of container-based workflows and the deployment of containers on a cloud–edge environment. In the first step, virtual CPU (vCPU) is allocated for each container to enable vCPU sharing among different containers. Next, two-step resource deployment is used to schedule the containers onto VM, and VM onto the physical machines in either edge or cloud environment. Multiple objectives are considered, including minimizing makespan, load imbalance, and energy consumption, from the perspective of cloud–edge resources as well as containerized workflows. To obtain a set of non-dominated solutions, three evolution strategies are designed and combined with two multi-objective algorithm frameworks — co-evolution strategy (CES), basic non-co-evolution strategy (B-NCS), and hybrid non-co-evolution strategy (H-NCS). Simulation results demonstrate that the proposed model outperforms the existing two-step scheduling model and H-NCS performs better than other strategies.

Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

End-to-end person re-identification: Real-time video surveillance over edge-cloud environment

In this work, a real-time end-to-end person re-identification (Re-ID) system is developed for a realistic unconstrained open-world environment. The raw surveillance videos are analyzed to provide the ranked list of person images matched with the query at the user-end. Two frameworks based on distributed-computing and edge-based computing that utilize the NVIDIA Jetson Nano and TX2, respectively have been developed to realize the objective. Various practical aspects of deploying an end-to-end Re-ID system over edge-cloud environment, such as resource constraints at the edge, hardware allocation to individual tasks and their acceleration, communication costs and scalability are dealt with. An improvised omni-scale network (iOSNet) with a powerful feature aggregation scheme has been proposed to handle more complex variations in the appearance of a person. The performance of the system has been evaluated on our own dataset and compared with other existing datasets, where state-of-the-art accuracy is achieved on all the datasets.

Modified firefly algorithm for workflow scheduling in cloud-edge environment.

Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users—to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives—cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results’ quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.

Computation offloading for object-oriented applications in a UAV-based edge-cloud environment

Computation offloading for object-oriented applications in a UAV-based edge-cloud environment