Edge Capacities Research Articles

Abnormal cascading dynamics in transportation networks based on Gaussian distribution of load

In the transportation network, we observe that the distance people travel by means of transportation follows a certain distribution. Statistical analysis shows that people’s travel distance is mainly concentrated in a medium range by the same vehicle, and they choose fewer destinations that are extremely close or far away. However, in previous studies, the impact of distance on the distribution of load flow within the network has often been neglected, or at best, addressed with overly simplistic assumptions. Therefore, we quantify the load flow distribution based on the Gaussian distribution of distances between the nodes. On this basis, a new cascading failure model is proposed using the shortest path strategy to calculate the initial load of the edge. Through the simulation of three real traffic networks and two artificially constructed networks with similar structural characteristics of traffic networks, we found the following interesting anomalies: First, increasing the load-bearing capacity of edges within the network does not necessarily lead to enhanced robustness. Second, we observed that removing more edges does not necessarily lead to a decrease in network robustness; conversely, the network robustness can be higher when a moderate number of edges are removed compared to fewer edges. To better understand the two anomalous dynamics phenomena we observed, we ran simulations on a small-scale network extracted from a real traffic network. We found that, under certain circumstances, the premature failure of some edges may isolate certain regions from the network, which may be responsible for this paradox.

Sink location problems in dynamic flow grid networks

A dynamic flow network consists of a directed graph, where nodes called sources represent locations of evacuees, and nodes called sinks represent locations of evacuation facilities. Each source and each sink are given supply representing the number of evacuees and demand representing the maximum number of acceptable evacuees, respectively. Each edge is given capacity and transit time. Here, the capacity of an edge bounds the rate at which evacuees can enter the edge per unit time, and the transit time represents the time which evacuees take to travel across the edge. The evacuation completion time is the minimum time at which each evacuee can arrive at one of the evacuation facilities. Given a dynamic flow network without sinks, once sinks are located on some nodes or edges, the evacuation completion time for this sink location is determined. We then consider the problem of locating sinks to minimize the evacuation completion time, called the sink location problem. The problems have been given polynomial-time algorithms only for limited networks such as paths [1–3], cycles [1], and trees [4–6], but no polynomial-time algorithms are known for more complex network classes. In this paper, we prove that the 1-sink location problem can be solved in polynomial-time when an input network is a grid with uniform edge capacity and transit time.

The universal federator: A third-party authentication solution to federated cloud, edge, and fog

Cloud, Edge, and Fog computing provide computational services to different end users. A federation among these computing paradigms is beneficial, as it enhances the capability, capacity, coverage, and services of cloud, edge, and fog. An authentication method is needed to realize such a federation among cloud, edge, and fog so that a user belonging to one of these computing paradigms can use the services offered by other computing paradigms in the federation without creating a new account. This paper proposes a standard-compliant universal federator that transparently provides third-party authentication among different protocols, used by cloud, edge, and fog, such as 3GPP EPS-AKA, OpenID Connect (OIDC), and 802.1x. The federator provides transparency by using a controller and modules that act as virtual counterparts of the authentication entities in EPS-AKA, OIDC, and 802.1x. These virtual counterparts play multiple roles, depending upon the involved protocols. We deployed a testbed, published our implementation on GitHub, and tested third-party authentication for 16 scenarios across EPS-AKA, OIDC, and 802.1x. The results show that our federator successfully provides third-party authentication while taking 4.07–51.8% of the total authentication time, which ranges between 1.193–3.825 s for 16 scenarios. Some scenarios involving 802.1x take considerably longer due to the bottleneck caused by the 802.1x switch. We also conducted a security analysis to show that our proposed federator fulfills multiple security requirements.

Identifying critical demand scenarios for the robust capacitated network design problem using principal component analysis

AbstractIn this paper, we consider the single‐commodity robust network design problem. Given an undirected graph with capacity installation costs on its edges and a set of scenarios with associated flow balance vectors that represent different scenarios of node supplies and demands, the goal is to find integer edge capacities that minimize the total installation cost and permit a feasible single commodity flow for each scenario. This problem arises, for example, in the design of power networks, which are dimensioned to accommodate many different load scenarios. We propose a new method to identify a small subset of the given scenarios, such that solving the robust network design problem for the smaller scenario set leads to almost the same capacities as solving it for the full scenario set . By considering only the scenarios in , the size of the model that needs to be solved can be reduced substantially, while the error introduced by neglecting the remaining scenarios is kept very small. Our method only employs simple techniques from statistical data analysis, namely principal component analysis (PCA), and convex hull computations in low dimensions. Thus, its computational effort is very small and it is easily applicable to more complex network design problems. We evaluate the effectiveness of the method in computational experiments for instances stemming from offshore power grid planning and telecommunication networks. Our results show that the proposed techniques are indeed well suited to identify small scenario subsets that lead to significantly reduced models with high quality solutions.

A Research on Hybrid Cloud Computing

Dual distributed computing has arisen as a significant answer for present day ventures trying to use the benefits of both public and confidential cloud foundations. This theoretical digs into the pith of crossover distributed computing, inspecting its design, advantages, challenges, and arising patterns molding its reception. Cross breed cloud engineering consistently incorporates public and confidential cloud conditions, permitting associations to improve responsibility position in view of elements like security, consistence, execution, and cost-proficiency. By mixing the versatility and adaptability of public mists with the control and customization of private mists, dual models engage endeavors to draftsman modern IT environments custom-made to their extraordinary necessities. The advantages of mixed distributed computing are complex. Associations can powerfully scale assets, moving responsibilities between conditions to successfully oblige vacillations sought after. Furthermore, mixture mists work with information sway, empowering endeavors to hold delicate information on-premises while utilizing the dexterity of public mists for less basic responsibilities. This flexibility encourages development and speeds up computerized change drives, enabling organizations to remain deft and serious in a quickly developing scene. In any case, hybrid cloud reception likewise presents difficulties. Overseeing different cloud conditions presents intricacy, requiring powerful administration structures, robotization devices, and gifted faculty to guarantee consistent organization and security across the mixture framework. Interoperability issues, information coordination concerns, and potential seller secure in additional highlight the significance of fastidious preparation and vital dynamic in crossover cloud arrangements. Looking forward, a few patterns are reshaping the hybrid cloud scene. Edge registering is acquiring conspicuousness, driving the decentralization of information handling and stockpiling to the organization edge, subsequently enlarging mixed models with edge capacities. Besides, progressions in containerization advances, like Kubernetes, are working with compactness and responsibility portability across mixed conditions, upgrading spryness and asset use. All in all, dual distributed computing addresses a change in outlook in IT framework the executives, offering a strong mix of adaptability, versatility, and control. While reception involves difficulties, the essential reconciliation of public and confidential mists can open unmatched open doors for advancement and effectiveness in the computerized time. Embracing mixed cloud structures enables undertakings to explore intricacy, mitigate risk, and benefit from arising advancements to drive feasible development and upper hand.

Deadline-constrained multi-commodity flow routing and scheduling optimization with consideration of edge lengths and capacities

With the emergence of satellite communications, meeting deadline requirements for data flow has become more challenging due to the trade-off between route lengths and capacities, as traversal delay becomes more prominent in satellite networks. To address the transmission optimization problems of multi-commodity flow with deadlines in satellite-based networks like networks considering edge lengths and capacities, we propose a new traffic model of flow conservation with the delay of edge lengths and then construct the deadline-constrained multi-commodity flow routing and scheduling optimization based on the proposed traffic model. Besides, for priority scheduling of multi-commodity flow, admission control is studied to schedule the commodities according to the priority level based on the proposed deadline-constrained flow optimization model. To solve the proposed models, we utilize the time-slicing approach and decomposition methods. Flow optimizations and admission control are discretized into linear programming and mixed 0–1 linear programming, respectively, which are suitable for Benders and Dantzig–Wolfe decomposition. We provide numerical examples using the Iridium satellite network to demonstrate the effectiveness of our routing and scheduling methods compared to those without consideration of edge delays.

Research on Distributed Renewable Energy Power Measurement and Operation Control Based on Cloud-Edge Collaboration

This paper examines how we can combine two big trends in solar energy: the spread of solar panels and wind turbines to renew the power grid, and cloud and edge computing technology to improve the way the grid works. Our study introduces a new strategy that is based on a means to exploit the power of cloud computing’s big data handling ability, together with the capacity of edge computing to provide real-time data processing and decision making. The method is designed to address major challenges in renewables systems making the system bigger and more reliable, and cutting the time delays in deciding how the system should respond. These are the kinds of changes that will be necessary so that we can blend solar and wind power into our current power grid, whether we are ready to say goodbye to coal or natural gas power. Our paper presents a way in which we believe that renewables systems can work more smoothly and effectively. This includes making it easier to measure how much power is being generated, to control these systems so that they function much like traditional power plants, and hence, to allow renewable energy to be part of a reliable and efficient part of our electricity supply. These are all crucial steps in using technology to make more of the green power from the sun – which we must do for our energy usage to be more earth friendly.

Native blue elderberry in hedgerows bridges revenue and conservation goals

Field edge hedgerows have long been promoted by UC Cooperative Extension and other organizations as a way to bring needed biodiversity to California farms. However, adoption of hedgerow planting still falls far short of available edge capacity. Our study explores a new multifunctional model of hedgerows that combines production with environmental conservation goals by considering the revenue potential of harvesting blue elderberry. Blue elderberry is a drought-tolerant native species well adapted to multiple microclimates in California and the western United States. The growth of elderberry herbal products and specialty foods markets is currently skyrocketing nationally and globally, and blue elderberry may offer a promising entry into these markets. Blue elderberry is also sought out by Indigenous people as one important component in efforts to restore cultural and food sovereignty. A field demonstration trial in the southern Sacramento Valley found that elderberry yields from 1,000-foot-long multi-species hedgerows could potentially provide from $2,000 to $3,000 in net annual revenue within 3 to 5 years, with much more possible as the hedgerows mature.

Learning-augmented maximum flow

We propose a framework for speeding up maximum flow computation by using predictions. A prediction is a flow, i.e., an assignment of non-negative flow values to edges, which satisfies the flow conservation property, but does not necessarily respect the edge capacities of the actual instance (since these were unknown at the time of learning). We present an algorithm that, given an m-edge flow network and a predicted flow, computes a maximum flow in O(mη) time, where η is the ℓ1 error of the prediction, i.e., the sum over the edges of the absolute difference between the predicted and optimal flow values. Moreover, we prove that, given an oracle access to a distribution over flow networks, it is possible to efficiently PAC-learn a prediction minimizing the expected ℓ1 error over that distribution. Our results fit into the recent line of research on learning-augmented algorithms, which aims to improve over worst-case bounds of classical algorithms by using predictions, e.g., machine-learned from previous similar instances. So far, the main focus in this area was on improving competitive ratios for online problems. Following Dinitz et al. (2021) [6], our results are among the firsts to improve the running time of an offline problem.

Approximating Sparsest Cut in Low-treewidth Graphs via Combinatorial Diameter

The fundamental Sparsest Cut problem takes as input a graph G together with edge capacities and demands and seeks a cut that minimizes the ratio between the capacities and demands across the cuts. For n -vertex graphs G of treewidth k , Chlamtáč, Krauthgamer, and Raghavendra (APPROX’10) presented an algorithm that yields a factor- \(2^{2^k}\) approximation in time \(2^{O(k)} \cdot n^{O(1)}\) . Later, Gupta, Talwar, and Witmer (STOC’13) showed how to obtain a 2-approximation algorithm with a blown-up runtime of \(n^{O(k)}\) . An intriguing open question is whether one can simultaneously achieve the best out of the aforementioned results, that is, a factor-2 approximation in time \(2^{O(k)} \cdot n^{O(1)}\) . In this article, we make significant progress towards this goal via the following results: (i) A factor- \(O(k^2)\) approximation that runs in time \(2^{O(k)} \cdot n^{O(1)}\) , directly improving the work of Chlamtáč et al. while keeping the runtime single-exponential in k . (ii) For any \(\varepsilon \in (0,1]\) , a factor- \(O(1/\varepsilon ^2)\) approximation whose runtime is \(2^{O(k^{1+\varepsilon }/\varepsilon)} \cdot n^{O(1)}\) , implying a constant-factor approximation whose runtime is nearly single-exponential in k and a factor- \(O(\log ^2 k)\) approximation in time \(k^{O(k)} \cdot n^{O(1)}\) . Key to these results is a new measure of a tree decomposition that we call combinatorial diameter , which may be of independent interest.

Exact and heuristic approaches for maximizing flows in UAV-enabled wireless cellular networks with multi-hop backhauls

This paper investigates the problem of data routing in backhaul networks using Unmanned Aerial Vehicles (UAVs) to relay data from Small Cells (SCs) to the core network. The objective is to maximize the total fulfilled demand of data to be routed, while ensuring technical requirements such as hop constraints and edge capacity. The problem is formulated using a compact mixed-integer programming model, which can solve small- and medium-sized topologies. In addition, a fast constructive heuristic based on a maximal tree is developed to solve large-scale topologies, resulting in a significant reduction in CPU time. The quality of the heuristic is evaluated by using column generation for solving the linear programming relaxation of an exponential formulation. The computational study shows the effectiveness and value of the proposed compact model and constructive heuristic for various topology sizes. Furthermore, experiments demonstrate that by keeping the network setup constant and updating the demand vector only, the computational time of the compact model can be drastically reduced for all topology sizes.

A Knapsack-based Metaheuristic for Edge Server Placement in 5G networks with heterogeneous edge capacities

The rapid proliferation of low-latency and high-bandwidth applications has brought edge computing to the forefront of mobile network architectures. However, the strategic placement of edge servers plays a vital role in balancing price-performance trade-offs significantly. Existing works addressing the Edge Server Placement Problem have assumed homogeneous computational capabilities across ESs, which is not a pragmatic assumption considering variations in user densities and workload fluctuations across typical cityscapes. This work proposes a solution to the Edge Server Placement Problem with heterogeneous ES capacities and introduces a novel scheme to evaluate the workload of ESs for 5G networks. Additionally, this paper also proposes a novel Knapsack-based Metaheuristic for allocating base stations to edge servers, turning the Edge Server Placement Problem into a 0-1 Knapsack problem. Experimental evaluation using popular 5G traffic demand datasets has found that the proposed approach improves workload balance by 40.79%, utilisation rates by 57.58%, and reduces energy consumption by 44.68% approximately vis-à-vis homogeneous counterparts.

RCA-GAN: An Improved Image Denoising Algorithm Based on Generative Adversarial Networks

Image denoising, as an essential component of image pre-processing, effectively reduces noise interference to enhance image quality, a factor of considerable research importance. Traditional denoising methods often lead to the blurring of image details and a lack of realism at the image edges. To deal with these issues, we propose an image denoising algorithm named Residual structure and Cooperative Attention mechanism based on Generative Adversarial Networks (RCA-GAN). This algorithm proficiently reduces noise while focusing on preserving image texture details. To maximize feature extraction, this model first employs residual learning within a portion of the generator’s backbone, conducting extensive multi-dimensional feature extraction to preserve a greater amount of image details. Secondly, it introduces a simple yet efficient cooperative attention module to enhance the representation capacity of edge and texture features, further enhancing the preservation of intricate image details. Finally, this paper constructs a novel loss function—the Multimodal Loss Function—for the network training process. The experimental results were evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) as evaluation metrics. The experimental results demonstrate that the proposed RCA-GAN image denoising algorithm has increased the average PSNR from 24.71 dB to 33.76 dB, achieving a 36.6% improvement. Additionally, the average SSIM value has risen from 0.8451 to 0.9503, indicating a 12.4% enhancement. It achieves superior visual outcomes, showcasing the ability to preserve image texture details to a greater extent and excel in edge preservation and noise suppression.

Leveraging artificial intelligence and mutual authentication to optimize content caching in edge data centers

Available online Edge data centers are designed to meet the stringent QoE requirements of delay-sensitive and computationally intensive services in Content Delivery Network (CDN) and 5G networks. The primary purpose of this paper was to formulate and solve the problem of optimizing many control variables jointly: (i) what contents to store by taking into consideration edge capacity, and (ii) what contents to recommend to each Internet of Everything (IoE) item, based on identity and access management (IAM). In reactive caching policy, we proposed a new Two-Factor Authentication (2FA) scheme founded upon the Elliptic Curve Cryptography (ECC) and one-way hash function for access control. More interestingly, we use Non-negative Matrix Factorization (NMF), Fuzzy C-Means (FCM), Random Forest (RF) and Pearson Correlation (PC) to improve the accuracy and latency of traditional data filtering models. The intelligent recommendation engine we propose is designed to be implemented by cloud for caching and prefetching contents at the edge. The experimental results validate the theoretical guarantees of the proposed solution and its ability to achieve significant performance gains compared to common baseline models.

Using submodularity in solving the robust bandwidth packing problem with queuing delay guarantees

This paper addresses the robust bandwidth packing problem with queuing delay guarantees. The queuing delays are approximated using the M/G/1 model. The problem involves nonlinear constraints that aim to limit the total queuing delays and satisfy edge capacities. We present two types of reformulations for this intractable problem. The first type consists of conic quadratically constrained or linear programs with integer variables, which can be implemented using commercial solvers. The second type is mixed-integer linear programs with an exponential number of constraints. To address the nonlinear constraints, we reformulate them into multiple linear constraints by combining polymatroid or submodular inequalities with the supporting hyperplanes of a concave function. The computational results demonstrate the effectiveness of the proposed formulations.

Defense and Attack Game Strategies of Dual-Network Coupled CPPS with Communication Edge Failures

With the development of power technology and communication technology, the power grid and power communication network have become interdependent and closely coupled. The load shedding operation of the power grid is an important means to reduce the occurrence of chain faults and ensure the safe and stable operation of the power grid. Based on the transmission of load control services in the communication network, this paper establishes a model for a dual-network coupled cyber physical power system (CPPS). Considering communication edge faults, the associated load capacity of the communication edge and the expected load loss of the power grid are defined. On this basis, the paper proposes a complete information zero-sum game mechanism called “defense attack defense” for communication edge failures, which takes the expected loss of load from the power grid as the benefit. The paper studies the optimal attack and defense game strategies and provides the algorithm implementation process for the three stages of the game. Considering the bandwidth capacity of the communication edge, this paper uses the Dijkstra algorithm or k shortest paths (KSP) algorithm with the cost factor of the communication edge as the weight to plan the main and backup communication channels for multiple load control services. The simulation results show that the game mechanism proposed in this paper can effectively reduce the expected load loss from the power grid and improve the stability of the CPPS.

Models and Algorithms of Abstract Flows in Evacuation Planning

Flows over time generalize classical network flows by introducing a notion of time. Each arc is equipped with a transit time that specifies how long flow takes to traverse it, while flow rates may vary over time within the given edge capacities. Ford and Fulkerson’s original 1956 max flow/min cut paper formulated max flow in terms of flows on paths. In 1974, Hoffman pointed out that Ford and Fulkerson’s original proof was quite abstract, and applied to a wide range of max flow-like problems. In this abstract model we have capacitated elements and linearly ordered subsets of elements called paths that satisfy switching property. When two paths P and Q cross at an element (node) then there must be a path that is a subset of the first path up to the crossing element and a subset of the second path after the crossing element. Contraflow is a widely accepted solution approach that increases the flow and decreases the evacuation time making the traffic smooth during evacuation by reversing the required road directions from the risk areas to the safe places. In this paper, we integrate the abstract flow with contraflow, give mathematical formulations of these models and present efficient polynomial time algorithms for solving the abstract contraflow problems.

User-Oriented Edge Node Grouping in Mobile Edge Computing

In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.

Love of Variety Based Latency Analysis for High Definition Map Updating: Age of Information and Distributional Robust Perspectives

High definition (HD) map is a key technology that enables autonomous driving, which has the characteristic of frequent updates and low delay requirements. In order to minimize the HD map updating latency, this paper jointly considers the generation and transmission processes of HD map. The generation process of HD map relies heavily on the data captured by different types of sensors, which is modeled by the federated analytics process. Also, in order to quantify the diversity of utilized sensors, the concept of love of variety is introduced in this paper. Then, considering HD map is composed of dynamic and static layers that have different latency requirements during the transmission process, this paper proposes a method to allocate edge server capacity to each HD map layer such that the overall information staleness can be minimized. Firstly, the deterministic edge capacity case is discussed and the solution is derived by obtaining the Karush—Kuhn—Tucker conditions. Then, considering the practice that an edge server provides services to multiple attached devices simultaneously, contentions among these devices make available capacity for the autonomous vehicle variational. Therefore, an uncertain edge capacity case is discussed as well, where the uncertainty is described by Wasserstein metrics and the problem is reformulated into distributional robust chance constrained optimization problem. And for the low latency purpose, we utilize an inner approximation to reduce the complexity of the original problem and find a suboptimal solution. Finally, simulation results demonstrate the effectiveness of our proposed methods that achieves the lowest latency for HD map updating.

HPoC: A Lightweight Blockchain Consensus Design for the IoT

The research topics of this paper are the data security of the edge devices and terminals of the Internet of Things (IoT) and the consensus design of a lightweight blockchain for the Internet of Things. These devices have self-organization capabilities to overcome the bandwidth delay and service-congestion problems caused by excessive concentration in existing scenarios, but they face the challenges of limited computing, storage, and communication resources. As a result, a non- financial lightweight blockchain consensus design with low energy consumption, low latency, and greater stability should be investigated. We propose a hierarchical proof-of-capability (HPoC) consensus mechanism combined with the asynchronous proof-of-work (PoW) mechanism for improving the computing capacity, storage capacity, and communication capacity of IoT edge devices that can generate blocks with low latency, low power consumption, and strong stability in resource-constrained edge device nodes, while ensuring that the security of the edge devices is enhanced asynchronously. We simulated a smart-home scenario, with the number of device nodes ranging from 15 to 75, and conducted comparative experiments between HPoC and PoW based on different difficulty bits. The experimental results showed that HPoC is a consensus mechanism with scalability and stability that can flexibly adjust time consumption and accurately select nodes with strong capabilities to generate blocks in heterogeneous devices.