An edge server deployment method based on optimal benefit and genetic algorithm

There have been many studies focusing on edge server deployment and service placement in mobile-edge computing (MEC), respectively, but rare works took both of them into consideration. However, edge server deployment and service placement are coupling issues in practice, where the former affects the latter. Besides, the economic benefit of the MEC platform is also a consideration. Due to different service request rates and prices, appropriate service placement solutions are needed to increase the overall profit. In this article, we propose a complete process combining edge server and service placement, where service placement explicitly takes into account the structure of current edge server placement and different service request rates and prices. We design a joint edge server deployment and service placement model with the goal of maximizing the overall profit of all edge servers under the constraints of the number of edge servers, the relationship among edge servers and base stations, the storage capacity, and the computing capacity of each edge server. We propose a two-step method including the clustering algorithm and nonlinear programming to solve the formulated problem. Extensive evaluations based on the real-world data set demonstrate that the proposed algorithm outperforms the baseline methods.

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.

The study proposes an edge server deployment method based on the widely used N-1 security criterion in power systems to improve the security and reliability of edge computing systems in the event of single-point failures.The N-1 security criterion requires the system to remain operational without triggering broader system issues in the event of any single equipment failure.This paper designs redundancy mechanisms and backup server schemes to ensure that even if an edge server fails, its workload can be quickly and seamlessly transferred to a backup server, thereby avoiding negative impacts on service quality, especially in terms of latency and performance.This method effectively reduces the security risks that could arise from single-point failures in edge computing systems.Simulation results show that, compared with traditional server deployment methods, the N-1 security criterion-based approach performs significantly better in terms of system reliability, stability, and fault tolerance, substantially improving the security and service continuity of edge computing systems.Additionally, considering that the random nature of the initialization phase in traditional K-Means clustering algorithms may lead to instability in the final results and that servers may face overload issues, this study further proposes an improved K-Means algorithm.By optimizing the selection of initial cluster centres' and adjusting the clustering process, the new algorithm more effectively reduces communication latency and balances the load between servers.Experimental results indicate that the improved K-Means algorithm outperforms existing algorithms, including DBCA, K-Means, Top-K, and Random algorithms, in terms of reducing communication latency and achieving load balancing.Moreover, the deployment strategy based on the N-1 security criterion significantly enhances system robustness and security, ensuring stable system operation in the event of a single edge server failure.

With the rapid development of the Internet of Things, a large number of smart devices are being connected to the Internet while the data generated by these devices have put unprecedented pressure on existing network bandwidth and service operations. Edge computing, as a new paradigm, places servers at the edge of the network, effectively relieving bandwidth pressure and reducing delay caused by long-distance transmission. However, considering the high cost of deploying edge servers, as well as the waste of resources caused by the placement of idle servers or the degradation of service quality caused by resource conflicts, the placement strategy of edge servers has become a research hot spot. To solve this problem, an edge server placement method orienting service offloading in IoT called EPMOSO is proposed. In this method, Genetic Algorithm and Particle Swarm Optimization are combined to obtain a set of edge server placements strategies, and Simple Additive Weighting Method is utilized to determine the most balanced edge server placement, which is measured by minimum delay and energy consumption while achieving the load balance of edge servers. Multiple experiments are carried out, and results show that EPMOSO fulfills the multiobjective optimization with an acceptable convergence speed.

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.

With the exponential growth of mobile devices and data traffic, mobile edge computing has become a promising technology, and the placement of edge servers plays a key role in providing efficient and low-latency services. In this paper, we investigate the issue of edge server placement and user allocation to reduce transmission delay between base stations and servers, and balance the workload of individual servers. To this end, we propose a graph clustering-based edge server placement model by fully considering the constraints such as the distance, coverage area and number of channels of base stations. The model mainly consists of a two-layer graph convolutional network (GCN) component and a differentiable version of K-means clustering component, which transforms the server placement problem into an end-to-end learning optimization problem on a graph. It trains the GCN network to achieve the best clustering results with the expectation of average delay and load balancing as the loss function to obtain the edge server placement and user assignment scheme. We conducted experiments based on the Shanghai Telecom dataset, and the results show the effectiveness of our approach in both latency reduction and load balancing.

Mobile edge computing(MEC), as an emerging computing paradigm, pushes services away from centralized remote cloud to distributed edge servers deployed by multiple service providers(SPs), improving user experience and reducing the communication burden on core network. However, this distributed computing architecture also brings some new challenges to the network. In multi-SP MEC system, a SP prefers to use edge servers deployed by itself instead of others, which not only improves service quality but also reduces processing cost. The service placement and request scheduling strategies directly affect the revenue of SPs. Since the service popularity changes over time and the resources of edge servers are limited, the network system needs to make decisions about service placement and request scheduling dynamically to provide better service for users. Owing to the lack of long-term prior knowledge and involving binary decision variables, how to place services and schedule requests to boost the profit of SPs is a challenging problem. We formally formalize this joint optimization problem and propose an efficient online algorithm. First, we invoke Lyapunov optimization technology to convert the long-term optimization problem into a series of subproblems, then a dual-decomposition algorithm is utilized to solve the subproblem. Experimental results show that the algorithm proposed in this paper achieves nearly optimal performance, and it raises 25% and 70% profit compared to greedy and Top-K algorithms, respectively.

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.

In this paper, we study key theoretical approaches such as modeling and identification of business process-oriented coupling structure problems from the perspective of resource allocation efficiency of computing, network and storage that can be provided by terminal sensing devices and edge servers of edge computing. A particle swarm intelligent optimization algorithm APSO is proposed for multi-terminal business sequencing. Based on the collected resource information of mobile terminals and edge servers, considering the interaction between multiple business activity requests, we study how to efficiently allocate the resources of edge servers, dynamically make the approximate optimal process sequencing for all applications, and make the average completion time of all business activities and the average energy consumption of mobile terminals and and edge servers are made to be approximately optimal. Finally, a series of experiments are conducted to evaluate the performance of APSO. The experimental results show that the proposed particle swarm optimization algorithm has a good advantage in terms of average completion time and average energy consumption in a multi-terminal business scenario with limited edge server resources.

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 makes up for the high latency of the central cloud network by deploying server resources in close proximity to users. The storage and other resources configured by edge servers are limited, and a reasonable cache replacement strategy is conducive to improving the cache hit ratio of edge services, thereby reducing service latency and enhancing service quality. The spatiotemporal correlation of user service request distribution brings opportunities and challenges to edge service caching. The collaboration between edge servers is often ignored in the existing research work for caching decisions, which can easily lead to a low edge cache hit rate, thereby reducing the efficiency of edge resource use and service quality. Therefore, this article proposes a diversified caching method to ensure the diversity of edge cache services, utilizing inter-server collaboration to enhance the cache hit rate. After the service request reaches the server, if it misses, the proposed algorithm will judge whether the neighbor node can provide services through the cache information of the neighbor node, and then the server and the neighbor node jointly decide how to cache the service. At the same time, the performance of the proposed diversified caching method is evaluated through a large number of simulation experiments, and the experimental results show that the proposed method can improve the cache hit rate by 27.01-37.43%, reduce the average service delay by 25.57-30.68%, and with the change of the scale of the edge computing platform, the proposed method can maintain good performance.

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.

Recently, Mobile/Multi-access Edge Computing (MEC) has attracted significant attention as a key component for executing cooperative driving systems that exhibit low latency. However, achieving low latency communication among evolved Node Bs (eNBs) is a critical challenge while implementing cooperative driving systems using MEC. The Edge Server (ES) deployed at the eNB is required to collect various sensor data from physically close vehicles, although the vehicles may not be connected to the eNB attached to the ES. If the vehicle and the ES belong to different mobile carriers, the sensor data of the vehicle is required to pass through a network of multiple carriers and the Internet. In this paper, we propose the ''Edge eXchange (EX).'' The EX is a conceptual eNB, which is equipped with ''wireless backhaul communication'' between eNBs regardless of the carriers. Here, the wireless backhaul communication indicates a direct communication between eNBs using wireless communication. To evaluate the EX, we propose an ES deployment method and consider two types of distances as alternatives to communication latency. The first is the communication distance between adjacent eNBs. The second is the communication distance between the vehicle and the closest computational nodes. Using the Japanese network model, we analyze the above distances and evaluate the effect of the EX. From the result of the analysis, the EX can dramatically suppress communication latency between eNBs for small range wireless backhaul communication. Furthermore, the EX can suppress the number of ESs required for achieving low latency by using the wireless backhaul communication.

Industrial Internet of Things (IIoT) ensures reliable and efficient data exchanges among the industrial processes using Artificial Intelligence (AI) within the cyber-physical systems. In the IIoT ecosystem, devices of industrial applications communicate with each other with little human intervention. They need to act intelligently to safeguard the data confidentiality and devices' authenticity. The ability to gather, process, and store real-time data depends on the quality of data, network connectivity, and processing capabilities of these devices. Pervasive Edge Computing (PEC) is gaining popularity nowadays due to the resource limitations imposed on the sensor-embedded IIoT devices. PEC processes the gathered data at the network edge to reduce the response time for these devices. However, PEC faces numerous research challenges in terms of secured communication, network connectivity, and resource utilization of the edge servers. To address these challenges, we propose a secured and intelligent communication scheme for PEC in an IIoT-enabled infrastructure. In the proposed scheme, forged identities of adversaries, i.e., Sybil devices, are detected by IIoT devices and shared with edge servers to prevent upstream transmission of their malicious data. Upon Sybil attack detection, each edge server executes a parallel Artificial Bee Colony (pABC) algorithm to perform optimal network configuration of IIoT devices. Each edge server performs the job migration to their neighboring servers for load balancing and better network performance, based on their processing and storage capabilities. The experimental results justify the efficiency of our proposed scheme in terms of Sybil attack detection, the convergence curves of our pABC algorithm, delay, throughput, and control overhead of data communication using PEC for IIoT.

As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.