Automatic Task Provisioning and Routing Framework for Carrier Robots in Smart Factory with Edge Computing

The Savvy Manufacturing plant could be a concept that communicates the conclusion goal of fabricating digitization. A Smart Factory, within the most common sense, profoundly digitized shop floor that collects and offers information persistently through associated computers, gadgets, and generations. In this work, the factory is represented by five types of sensors. The reading of the sensor values is sent to one of the Edge servers and cloud computing. One Edge server is selected based on calculating the time it takes for each server. The highest least time priority is chosen to receive the data coming from the sensors. This paper suggests a way to reduce the delay by using the edge server in addition to cloud computing by using methods that overcome any malfunction in one of the servers via another one that can work without the need to stop the factory systems. Index Terms— Smart Factory, edge computing, delay in IoT.

Edge Computing is a distributed computing architecture where the computing process takes place near the user’s physical location or at the source where the data originates. By placing the edge servers (ES) closer to the user’s location, the services received are faster and more reliable while benefiting from edge computing. Various Internet of Vehicle (IoV) applications in smart cities including assisted and autonomous driving, real-time accidents monitoring require huge data processing and low-latency communication. Since the user’s devices are resource constrained, an effective approach to address this constraint is to offload their tasks to nearby ES. So, an adaptive placements (to respond continuously changing environment) of these edge computing devices play a major role in the performance of various IoV applications. So, an efficient placement of ES is considered a critical issue in vehicular edge computing (VEC). To address the efficient and cost aware dynamic ES placement problem (CADEP), we developed two greedy algorithms. First, is cost aware and vehicle density based deployment of ES (static) that ensures that each vehicle’s demand is covered by at least by one ES (coverage constraint), called Greedy_static. Second, is based on vehicle density and is dynamic as per changing environment, called Greedy_dynamic which updates ES locations periodically based on change in the environment. To minimize the relocation cost, we formulated an optimization problem and used Hungarian matching to find optimal cost. For various vehicle densities, we found that our algorithms outperform uniform strategies in terms of cost-effectiveness and ES utilization. Further, for dynamic relocation of ES, we have shown that the cost required to relocate ES randomly is more as compared to our proposed algorithm Greedy_dynamic.

ABSTRACTWith the rapid development of intelligent scenarios such as intelligent transportation and urban perception, delay‐sensitive and computationally intensive applications continue to grow, especially in tasks such as anomaly detection based on machine learning, which puts higher requirements on the real‐time processing capabilities and resource scheduling efficiency of edge computing systems. Mobile edge computing (MEC), as a key supporting architecture, plays a core role in ensuring quality of service (QoS). On one hand, the occupation and release of server resources during task processing lead to dynamic changes in system resources within edge computing networks. Since system resources are often difficult to effectively replenish, services that are well adapted to the current time point may fail to accommodate new service requests at subsequent time points. On the other hand, due to the heterogeneous nature of tasks, resource consumption varies significantly across different task processing, causing some servers to easily become overloaded and unable to meet the processing demands of new tasks continuously. To tackle the challenges presented by the intensified dynamic changes in edge server resources due to task heterogeneity and the difficulty of processing new task requests under high‐load conditions in edge computing scenarios, we first devise a collaborative scheduling and offloading strategy for heterogeneous tasks across multiple edge servers. A task sorting mechanism and priority algorithm based on time groups and score values are designed. Then, with the optimization objective of minimizing task processing latency, a regional resource optimization algorithm based on Deep‐Q‐Network (DQN) is proposed to enable the effective processing of tasks. Finally, extensive experimental results show that this strategy can effectively achieve edge node load balancing, significantly reduce system processing delay, improve overall resource utilization, and has good heterogeneous task adaptability, which is suitable for multiple intelligent scene requirements including anomaly detection.

A Novel Lyapunov based Dynamic Resource Allocation for UAVs-assisted Edge Computing

While it is well understood that edge computing can significantly facilitate IoT-related applications by deploying edge servers close to IoT devices, it also faces many challenges with numerous IoT devices connected and interacted. One of the most important issues is how to efficiently deploy edge servers under a certain budget with the explosive growth of data scale and user base. Existing studies for edge server placement fail to consider user’s query preferences since individual users may be interested in events in particular regions and are keen to receive up-to-date data streams that originate in regions of interest. In this article, we present a preference-aware edge server placement approach that offers better workload distribution in terms of both minimizing query latency and balancing the load of edge servers. To achieve this, we formulate edge server placement with multiobjective optimization as a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -center problem and design two progressive approaches. We first propose quadratic integer programming (QIP) for small-scale data sets. Since the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula> -center problem is an NP-hard problem, we thus propose a heuristic algorithm named TAKG (TAbu search with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means and Genetic algorithm) for large-scale data sets. To evaluate the utility of the proposed models, we have conducted a comprehensive evaluation on a large data set that is collected by more than 1900 IoT devices during 30 days. Experimental results indicate our approaches outperform all baselines significantly in terms of both query latency and load balancing.

Edge computing, which locates edge servers with limited computing and storage resources at the edge of networks, is expected as a novel architecture for low-latency applications. In edge computing, users offload a task to edge servers due to poor computing resources and batteries of mobile devices so that the edge servers execute the offloaded task and return the result of it to users. Users can thus enjoy various applications without depending on the limitations of mobile devices. However, when the edge server’s load is too heavy, a large number of tasks will be offloaded to distant cloud servers. The long distance between users and cloud servers significantly degrades the quality of mobile applications. To prevent this problem, we propose flow splitting and aggregation schemes to improve offload delay to cloud servers in edge computing. This scheme splits TCP connections between users and cloud servers at the edge server, and then aggregates TCP connections between the edge server and cloud servers. We show the effectiveness of our scheme through simulation evaluations.

Mobile edge computing (MEC) is an emerging computing and communication model that brings extremely resourceful servers at a network's edge. This reduces the latency associated with cloud computing as applications executing on mobile devices do not always need to communicate with a cloud server because most of the functionalities provided by the cloud server are available on an edge server. Moreover, with the emergence of 5G really high amount of bandwidth is available to a mobile device, hence communication latency between the edge server and a mobile device is low. The combination of 5G and edge computing has a potential to better enable many interesting time-critical and/or computationally expensive computing and communication use cases, for example, connected cars. The stated combination enables development of computationally expensive applications for mobile devices and sensors nodes as these devices can offload their computational tasks to the edge server. Task offloading is a promising feature of edge computing, therefore here priority-class-based methods are presented to allocated CPU cycles to an offloaded task at the edge server. In one of the presented methods, a mobile device or a senor node assign their task to one of the available priority classes, and then offload the task to the edge server. Afterwards, the edge server assigns CPU cycles to such a task depending upon its priority class. Similarly, another method is presented that assigns priority to each device based on the type of tasks the device is offloading. While assigning CPU cycles to different offloaded tasks, the edge server uses their device priority. The simulation results demonstrate that the presented methods are capable of providing service differentiation.

Mobile edge computing (MEC) plays a pivotal role in the Internet of Vehicles and the Internet of Things. Edge server deployment is the initial step in establishing edge computing systems, which impact the overall system performance significantly. Besides, the performance of an edge computing system is also contingent upon the type of service deployed on servers, in the case of the same server deployment, different deployment of services will bring different profits. Most current studies concentrate solely on the former aspect, neglecting the optimization of service deployment in MEC system. In this article, we proposed a two-step method KPSOP for edge server and edge service deployment, aiming to reduce time delay, balance load, and improve the profit of MEC system, and KPSOP includes clustering algorithm and heuristic algorithm. We considered the location distribution of base stations, the task requests of vehicle users, the resource limitations of edge servers, etc. First, the edge server deployment was completed with the goal of minimizing time delay and load balancing. Second, the service deployment was completed with the goal of maximizing edge server profit. The experiments were based on real world base station information. The simulation results validate that our algorithm is more stable and converges faster. In addition, compared to other algorithms, it performs better in load balance and increasing profit.

Vehicular applications in smart cities, such as assisted and autonomous driving, require sophisticated data processing, low latency, and high throughput data transmission. Edge Computing is a leading approach designed to meet those application requirements. By deploying Edge servers at the network's edge, close to the vehicles, such applications can be successfully delivered while adhering to low-latency and high-throughput requirements. However, optimal placement of Edge servers is challenging since it necessitates a trade-off between quality of service and deployment cost. Latency can be reduced by placing as many Edge servers as feasible close to the vehicles, however, this results in significant deployment costs. This work addresses the problem of optimal Edge server placement. It solves this problem using integer linear programming, considering the relation between delay and cost, as well as the capacity of Edge servers in realistic road traffic scenarios. The proposed generic methodology is designed to reduce the cost of deploying Edge servers by combining the achievement of the desired latency threshold with workload balancing between Edge servers. We evaluate the efficiency of the proposed solution mathematically and through simulations based on open data from real vehicles traffic on roadways of Bordeaux, France. The obtained results demonstrate that our solution outperforms existing Edge server placement approaches, especially on workload balancing.

Emerging mobile applications, such as cognitive assistance based on deep neural network (DNN), require low latency as well as high computation power. To meet these requirements, edge computing (also called fog computing) has been proposed, which offloads computations to edge servers located near mobile clients. This paradigm shift from cloud to edge requires new computing infrastructure where edge servers are pervasively distributed over a region.This paper presents PerDNN, a system that executes DNNs of mobile clients collaboratively with pervasive edge servers. PerDNN dynamically partitions DNN computation between a client and an edge server to minimize execution latency. It predicts the next edge server the client will visit, calculates a speculative partitioning plan, and transfers the server-side DNN layers to the predicted server in advance, which reduces the initialization overhead needed to start offloading, thus avoiding cold starts. We do not incur excessive network traffic between edge servers, though, by migrating only a tiny fraction of the server-side DNN layers with negligible performance loss. We also use GPU statistics of edge servers for DNN partitioning to deal with the resource contention caused by multi-client offloading. In the simulation with human trace datasets and execution profile of real hardware, PerDNN reduced the occurrence of cold starts by up to 90%, achieving 58% higher throughput when clients change their offloading servers, compared to a baseline without proactive DNN transmission.

Edge computing promises a dramatic reduction in the network latency and the traffic volume, where many edge servers are placed at the edge of the Internet. Furthermore, those edge servers cache data to provide services for edge users. The data sharing among those edge servers can effectively shorten the latency to retrieve the data and further reduce the network bandwidth consumption. The key challenge is to construct an efficient data indexing mechanism no matter how the data is cached in the edge network. Although this is essential, it is still an open problem. Moreover, existing methods such as the centralized indexing and the DHT indexing in other fields fail to meet the performance demand of edge computing. This paper presents a COordinate-based INdexing (COIN) mechanism for the data sharing in edge computing. COIN maintains a virtual space where switches and data indexes are associated with their coordinates. Then, COIN distributes data indexes to indexing edge servers based on those coordinates. The COIN is effective because any query request from an edge server can be responded when the data has been stored in the edge network. More importantly, COIN is efficient in both routing path lengths and forwarding table sizes for publishing/querying data indexes. We implement COIN in a P4 prototype. Experimental results show that COIN uses 59% shorter path length and 30% less forwarding table entries to retrieve data indexes compared to using Chord, a well-known DHT solution.

Efficient resource usage in edge computing requires clever allocation of the workload of application components. In this paper, we show that under certain circumstances, the number of superfluous workload reallocations from one edge server to another may grow to a significant proportion of all user tasks—a phenomenon we present as a reallocation storm. We showcase this phenomenon on a city-scale edge server deployment by simulating the allocation of user task workloads in a number of scenarios capturing likely edge computing deployments and usage patterns. The simulations are based on a large real-world data set of city-wide Wi-Fi network connections, with more than 47M connections over ca. 560 access points. We study the occurrence of reallocation storms in three common edge-based reallocation strategies and compare the latency–workload trade-offs related to each strategy. As a result, we find that the superfluous reallocations vanish when the edge server capacity is increased above a certain threshold, unique for each reallocation strategy, peaking at ca. 35% of the peak ES workload. Further, while a reallocation strategy aiming to minimize latency consistently resulted in the worst reallocation storms, the two other strategies, namely a random reallocation strategy and a bottom-up strategy which always chooses the edge server with the lowest workload as a reallocation target, behave nearly identically in terms of latency as well as the reallocation storm in dense edge deployments. Since the random strategy requires much less coordination, we recommend it over the bottom-up one in dense ES deployments. Moreover, we study the conditions associated with reallocation storms. We discover that edge servers with the very highest workloads are best associated with reallocation storms, with other servers around the few busy nodes thus mirroring their workload. Further, we identify circumstances associated with an elevated risk of reallocation storms, such as summertime (ca. 4 times the risk than on average) and on weekends (ca. 1.5 times the risk). Furthermore, mass events such as popular sports games incurred a high risk (nearly 10 times that of the average) of a reallocation storm in a MEC-based scenario.

Recent advances in machine learning and deep learning have enabled many existing applications in smart cities, autonomous systems, and wearable devices. These applications often demand scalable real-time cognitive intelligence and on-the-spot decision making. Current computer systems have been customized for a cloud computing paradigm which often does not meet latency constraints and scalability requirements. To address the limitations of the cloud computing paradigm, the general trend is toward shifting the computation next to data producers at the edge. However, the edge computing paradigm is in the very early stages. Many system-level aspects of edge computing, including algorithms mapping and partitioning across edge computing resources (edge server, and edge nodes) are unknown. New research is required to understand and quantify design dimensions for edge computing.This paper presents a novel edge computing infrastructure for distributed real-time video analytics. This paper presents a holistic solution for co-designing application and edge infrastructure, including edge nodes and edge servers, to enable scalable real-time Artificial Intelligence (AI)/Deep Learning (DL) video analytics across many cameras. For experimental results and evaluation, we focus on the case study of object re-identification across many cameras, which is composed of object detection/classification (TinyYOLOv3), feature extraction, local re-identification, and global re-identification kernels. We evaluate the edge system under three different task mapping and resource allocation configurations. The results present that with the edge nodes (video cameras) more than 32, the only scalable solution is to perform detection/classification (TinyYOLOv3), feature extraction, local re-identification on the edge nodes next to cameras, and execute global re-identification on edge server.

Emergency task offloading strategy based on cloud-edge-end collaboration for smart factories

Mobile devices have certain restrictions such as processing power and network bandwidth. Edge computing can reduce load and delay by migrating the parts of application processing from mobile devices to edge servers. Edge computing is a method that extends cloud computing to the edge of network. Conventional schemes of face recognition based on edge computing can reduce the amount of network transmission data between the edge servers and the cloud servers by preprocessing data on the edge servers. However, the schemes execute each task on a certain resource, thus application response time may increase according to the conditions of computational resources and network. Hence, a task placement system considering the conditions of computational resources and network is necessary. This paper proposes a task placement system for face recognition applications in edge computing. The proposed system calculates estimated response time of possible task placements based on the task placement decision formula. The formula parameterizes power and CPU utilization of computational resources and delay and bandwidth of network. The task placement that minimizes estimated response time is determined. We implemented a prototype of the proposed task placement system and evaluated its performance through experiment.