Nowadays, data centers contribute to a large portion of global carbon emissions due to the growing demand for cloud services. Traditional green data center technologies have utilized all kinds of methods to reduce energy consumption, e.g., optimizing HVAC (Heating, Ventilation, and Air Conditioning), shutting down idle servers or switches and using renewable energy, but they typically target a single data center and have pushed the optimization space to its limits. However, carbon emissions vary wildly across data centers due to differences in local energy sources, and delay-tolerant tasks can be flexibly allocated to data centers with lower emissions. This opens new opportunities for carbon-aware task scheduling across geographical locations. However, carbon-aware scheduling faces key challenges, including the fluctuation of carbon intensity due to weather and power dynamics, and the dynamic nature of task arrivals with diverse QoS (Quality of Service) requirements. To address this, we propose CEDTS-RL (Carbon-Emission Driven Task Scheduling based on Reinforcement Learning), a real-time cross-geographical scheduling framework based on reinforcement learning, which unlocks new potential for reducing carbon emissions by relaxing the location restriction during scheduling. The evaluation is conducted using real-world datasets from Google, Alibaba, and Microsoft, along with solar and meteorological data from NASA. Results demonstrate that CEDTS-RL significantly outperforms both traditional and recent scheduling algorithms.

The rapid growth of the Industrial Internet of Things (IIoT) requires a secure meta-computing environment to support applications like industrial monitoring and remote control. However, this environment faces major security challenges, especially the risk of source address forgery, which can enable DDoS and botnet attacks, disrupting operations and compromising equipment. Current Internet infrastructure forwards packets based only on destination addresses, lacking the capability for source address verification. Although edge-based solutions like firewalls and systems, such as source address validation architecture (SAVA) and source address validation improvement (SAVI), are deployed, they fall short of comprehensive source address validation (SAV), allowing malicious traffic to propagate through core networks. To enhance security, a collaborative approach based on meta-computing principles is needed, allowing routers to verify source addresses cooperatively. Given the impracticality of fully upgrading routers, incremental deployment is essential. We show that optimizing incremental SAV deployment is NP-hard. To address this, we propose collaborative optimized source address filtering (COSAF), a heuristic algorithm that uses a sink-tree structure to effectively filter attack flows and optimize resource allocation. COSAF also takes SAV table capacity into account to improve resource utilization. Extensive simulations demonstrate that COSAF outperforms traditional methods.

5G communication performance is highly correlated with the locations of cellular base stations (BSs). Many previous works have studied the placement of BSs, however, millimeter-wave-based transmission and MEC (Mobile Edge Computing) technology brought by 5G makes the BS placement problem more complex in the 5G scenario. 5G communication enhances transmission rates, but it limits transmission distance. Besides, user experience are sensitive to MEC locations, which is also highly related to BS placement. In this paper, we design a 5G BS placement model and propose an EMBP (Efficient MEC BS placement) algorithm to compute the 5G BS locations and MEC locations. Using the historical location and computational task information of users, EMBP can choose BS and MEC deployment locations which will optimize QoE (Quality of Experience) performance in the future. The simulation results show that EMBP greatly outperforms traditional algorithms in terms of QoE and coverage. We also conducted a case study with real-world data collected, and the results validated our conclusions.

Nowadays, load balancing is more and more important for huge data centers. Most data centers usually adopt a software-based load balancer (LB), which consumes too many server resources and does not scale with the increasing traffic volume. However, there is an increased demand for layer-4 load balancers, which need to check more bits in packet headers. Although hardware-based load balancers can meet the requirements, they are much more expensive. During the past years, data centers frequently update their devices, including kinds of switches. Thus, there exists a huge number of obsolete switches, some of which are in good condition and equipped with high-performance storage like SRAM (Static Random Access Memory). In this paper, we proposed BCLB (Bit-based Collaborative Load Balancer), a new cooperative LB, which builds more powerful load balancing based on existing switches. Different with previous cooperative mechanisms that distribute rules to different LBs, BCLB lets switches cooperate based on bits. Many switches along the data path check different bits, and cooperatively check all bits in 5-tuple (which would be 296 bits in IPv6) for layer-4 packet header. In this way, rule updating will not influence the load balancing in BCLB. To optimize the performance of BCLB, we formulate the problem as an optimization problem, then we propose a dynamic programming algorithm to solve it. Finally, we conduct comprehensive simulations using both real-world traffic datasets and a P4-based prototype, the results show that BCLB performs much better than previous rule-based cooperative schemes.

Ocean network communication is more and more important nowadays. However, it faces significant challenges due to its heterogeneity, low reliability, and narrow bandwidth. Compared with traditional networks, the problem worsens under severe weather conditions because communication channels, e.g., microwave links, may degrade badly due to weather changes. While many ocean applications put higher QoS (Quality of Service) requirements on communications, it is difficult to meet them due to fluctuating weather changes. Thus, it is more challenging to model and operate the ocean network as more factors may influence the performance.Currently, artificial intelligence opens up new possibilities to meet the challenges because it can adapt to the dynamic changes of the network. In this paper, we formulate the problem by establishing a model between network performance and weather conditions. We try to optimize network utility given the traffic matrix and important weather factors, such as rain, atmospheric absorption, and clouds. To solve the problem, we propose a two-stage and multi-agent optimization algorithm named MAEON (Multi-Agent Efficient Ocean Network). We conduct a comprehensive simulation using generated network topology traffic and real-world weather datasets. We also carry out a case study with datasets from a real-world scenario. The results show that MAEON can improve performance by 21.5% compared with traditional algorithms.

Edge Computing is promising for latency-sensitive applications. However, current edge resource scheduling is inefficient. Deep Learning as a Service (DLaaS) provides deep learning methods to optimize the resource scheduling problem, but faces great challenges of security and reliability. On one hand, DLaaS training agents and raw data are exposed to various adversarial attacks. On the other hand, dishonest DLaaS trainers can generate poisoned models to attack the DLaaS system. In this article, we proposeSAPE, aSecure and decentralized DLAaSPlatform inEdge computing. SAPE allows users to submit their tasks, which will be scheduled to the appropriate edge clusters to minimize the task execution time. We formulate the resource scheduling problem and develop the federated deep reinforcement learning (DRL) method to optimize the problem and resist the adversarial attacks of DLaaS. We utilize blockchain and propose a consortium-based verification scheme to improve the reliability of the federated training process, protecting the DLaaS models from being poisoned and compromised. We conduct experiments to evaluate the latency and security performance of SAPE and the federated DRL scheduling policy. The results show that SAPE outperforms the traditional schemes when defending against adversarial attacks towards the DLaaS platform in edge computing.

The current Internet makes forwarding decisions based on only destination addresses, leading to a prevalence of IP source address spoofing. To mitigate the risks posed by IP spoofing, Source Address Validation in Intra-domain and Interdomain Networks (SAVNET) has been recently proposed and become a hot topic in both academia and industry. While all network equipment can not be upgraded to support SAVNET during one night, incremental deployment is needed for network operators. However, SAVNET can not defend against all spoofing attacks under partial deployment. Thus, we need to carefully choose nodes to be deployed, to improve incentive benefits during incremental deployment. In this paper, we formulate the incremental problem and prove that the problem is NP-Complete. To efficiently solve the problem, we propose a heuristic deployment scheme named SOID (SAV protocol optimized incremental deployment). The intuitive idea of SOID is using the sink-tree to get the detectable flow sets of each router. Then, it selects the routers which have the maximum total weight during each iteration. To evaluate the performance of the proposed algorithm, we conduct comprehensive simulations with generated and real topologies. The simulation results show that SOID performs much better compared with traditional schemes, such as random deployment and minimum vertex cover algorithms, with a manageable overhead.

Video cameras have been deployed widely today. Although existing systems aim to optimize live video analytics from a variety of perspectives, they are agnostic to the workload dynamics in real-world. We propose EC-MASS, an edge computing-based video scheduling system achieving both cost and performance optimization with multiple cameras and edge data centers . The intuition behind EC-MASS is to adaptively map cameras to different edge data centers according to dynamically updated configurations of cameras. We prove that generating the optimal mapping scheduling scheme is NP-Complete, and develop the scheduling algorithm by leveraging the insights of the economy consideration of camera allocation. Using the algorithm, EC-MASS is able to balance the workload among edge data centers while reducing the cost of video analytics system . We evaluate EC-MASS with datasets of video configurations from real-world cameras which randomly generate configurations for cameras, with a testbed that consists of 60 cameras and 4 edge data centers . Our results show that EC-MASS consistently outperforms the status quo in terms of cost and performance stability.

Edge computing architectures can help us quickly process the data collected by Internet of Things (IoT) and caching files to edge nodes can speed up the response speed of IoT devices requesting files. Blockchain architectures can help us ensure the security of data transmitted by IoT. Therefore, we have proposed a system that combines IoT devices, edge nodes, remote cloud, and blockchain. In the system, we designed a new algorithm in which blockchain-assisted compressed algorithm of federated learning is applied for content caching, called CREAT to predict cached files. In the CREAT algorithm, each edge node uses local data to train a model and then uses the model to learn the features of users and files, so as to predict popular files to improve the cache hit rate. In order to ensure the security of edge nodes’ data, we use federated learning (FL) to enable multiple edge nodes to cooperate in training without sharing data. In addition, for the purpose of reducing communication load in FL, we will compress gradients uploaded by edge nodes to reduce the time required for communication. What is more, in order to ensure the security of the data transmitted in the CREAT algorithm, we have incorporated blockchain technology in the algorithm. We design four smart contracts for decentralized entities to record and verify the transactions to ensure the security of data. We used MovieLens data sets for experiments and we can see that CREAT greatly improves the cache hit rate and reduces the time required to upload data.

The smart construction site is developing rapidly with the intelligentization of industrial management. Intelligent devices are being widely deployed in construction industry to support artificial intelligence applications. Video surveillance is a core function of smart construction, which demands both high accuracy and low latency. The challenge is that the computation and networking resources in a construction site are often limited, and the inefficient scheduling policies create congestions in the network and bring additional delay that is unbearable to realtime surveillance. Adaptive video configuration and edge computing have been proposed to improve accuracy and reduce latency with limited resources. However, optimizing the video configuration and task scheduling in edge computing involves several factors that often interfere with each other, which significantly decreases the performance of video surveillance. In this article, we present an edge-based solution of video surveillance in the smart construction site assisted by a graph neural network. It leverages the distributed computing model to realize flexible allocation of resources. A graph-assisted hierarchical reinforcement learning algorithm is developed to illustrate the feature of the mobile-edge network and optimize the scheduling policy by the Deep-$Q$Network. We implement and test the proposed solution in the commercial residential buildings of a fortune global 500 real estate company and observe that the proposed algorithm is efficient to maintain a reliable accuracy and keep lower delay. We further conduct a case study to demonstrate the superiority of the proposed solution by comparing it with traditional mechanisms.

The Internet of Medical Things is developing rapidly in recent years. However, the timeliness and security of healthcare applications challenge its adoption. In this article, we propose a blockchain-assisted edge computing platform that timely and securely processes time-sensitive healthcare applications. We propose a blockchain-assisted framework that leverages distributed edge servers to achieve fast data processing. We design smart contracts to verify the identity and data credibility of network entities. We formulate the problem as a directed acyclic graph organized scheduling model and develop online orchestrating algorithms to meet the timeliness requirement. We implement the blockchain prototype and evaluate the performance of the proposed algorithm under extensive configurations. Results show that fast blockchain-assisted edge computing platform for healthcare achieves a significant timeliness guarantee, and at the same time outperforms conventional schemes from the latency perspective.

Edge computing is promising to solve the latency issue in the Internet of Vehicles (IoV). However, due to decentralization, traditional edge computing suffers in management, deployment, and security. Containerization relaxes resource deployment and migration problems, but current container scheduling policies are inefficient to process complicated tasks based on directed acyclic graph or DAG structures. In this article, we design a containerized edge computing platform CUTE, which provides low-latency computation services for the Internet of Vehicles. The centralized controller is empowered with resource management and orchestration, and containers are scheduled to appropriate edge servers to optimize the computation delay. CUTE is also integrated with blockchain to improve network security. We formulate the vehicle task offloading and container scheduling problems and develop a heuristic container scheduling algorithm for DAG-based computation tasks submitted by vehicles remotely. We implement and deploy CUTE into the China Mobile Network, and conduct comprehensive experiments and a case study. The experiment results show that CUTE can provide low-latency computation services for vehicular applications and that the heuristic algorithm outperforms traditional container scheduling policies.

Edge computing, especially multiaccess edge computing, is seen as a promising technology to improve the Quality of user Experience (QoE) of many artificial intelligence (AI) applications in the evolution toward Internet-of-Things (IoT) infrastructure. However, the management and deployment of massive edge data centers bring new challenges for the current network. In this article, we propose a new edge-based IoT platform for AI (EBI-PAI), based on software-defined network (SDN) and serverless technology. EBI-PAI provides a unified service calling interface and schedules the resources automatically to satisfy the QoE requirements of users. To optimize performances during incremental deployment, we formulate the deployment problem, prove its complexity, and design heuristic algorithms to solve it. We implement EBI-PAI based on an opensource serverless project and deploy it in real networks. To evaluate EBI-PAI, we conduct comprehensive simulations based on the generated and real-world network topology, and real-world base station data set. The simulation results show that EBI-PAI can greatly improve QoE with the same budget and save the budget to achieve similar QoE. We finally carry out a case study with real user demands, and it further validates the simulation results.

Currently, video content accounts for the majority of network traffic. With increased live streaming, rigorous requirements have been introduced for better Quality of Experience (QoE). It is challenging to meet satisfactory QoE in live streaming, where the aim is to achieve a balance between 1) enhancing the video quality and stability and 2) reducing the rebuffering time and end-to-end delay, under different scenarios with various network conditions and user preferences, where the fluctuation in the network throughput degrades the QoE severely. In this paper, we propose an approach to improve the QoE for live video streaming based on Deep Reinforcement Learning (DRL). The new approach jointly adjusts the streaming parameters, including the video bitrate and target buffer size. With the basic DRL framework, TCLiVi can automatically generate the inference model based on the playback information, to achieve the joint optimization of the video quality, stability, rebuffering time and latency parameters. We evaluate our framework on real-world data in different live streaming broadcast scenarios, such as a talent show and a sports competition under different network conditions. We compare TCLiVi with other algorithms, such as the Double DQN, MPC and Buffer-based algorithms. The simulation results show that TCLiVi significantly improves the video quality and decreases the rebuffering time, consequently increasing the QoE score by 40.84% in average. We also show that TCLiVi is self-adaptive in different scenarios.

With the development of Internet of Things (IoT), edge computing becomes more and more prevalent currently. However, edge computing needs to deploy a large number of edge servers to reduce the communication latency, which will bring additional costs to the system. Although there exist some idle computing resources at the edge, the owners distrust each other and lack the incentives to contribute to the system. In this article, we propose a new edge computing platform decentralized and trusted platform for edge computing (DeTEC), which provides a unified interface to users, resolves the user's requests to the most appropriate edge server through domain name server, and returns the computational results to the IoT user. To build a trustworthy system, DeTEC integrates the blockchain technology with edge computing, such that the contributions of each participant could be accounted and rewarded. We formulate the task allocation problem, taking both node capacity and reward fairness into consideration, and solve it through a heuristic algorithm. Finally, to guarantee the trustworthiness of computational results, we utilize a police patrol model and try to optimize the system overall reward. We implement DeTEC based on an open source project and conduct comprehensive experiments to test its performance. The results show that our DeTEC system works well in the IoT scenario.

Industrial Internet of Things (IIoT) has been widely used in many fields. Meanwhile, blockchain is considered promising to address the issues of the IIoT. However, the current blockchains have a limited throughput. In this article, we devise an efficient and secure blockchain protocol compacted directed acyclic graph (CoDAG) based on a compacted directed acyclic graph, where blocks are organized in levels and width. New-generated blocks in the CoDAG will be placed appropriately and point to those in the previous level, making it a well-connected channel. Transactions in the network will be confirmed in a deterministic period, and the CoDAG keeps a simple data structure at the same time. We also illustrate the attack strategies by adversary, and it is proved that our protocols are resistant to these attacks. Furthermore, we design a CoDAG-based IIoT architecture to improve the efficiency of the IIoT system. Experimental results show that the CoDAG achieves 164× Bitcoin's throughput and 77× Ethererum's throughput.

Security is one of the critical issues in traditional networks. Software-Defined Networking (SDN) improves the security aspect by separating the control plane and the data plane of networks. To improve the performance of SDN, researchers have designed many advanced controller prototypes and considered the controller placement problem. However, link failures are critical security issues in networks and greatly impact SDN's security. The controller placement problem for link failures is still challenging today. In this paper, we study the SDN controller placement problem for single-link and multi-link failures, respectively. For single-link failures, we develop a heuristic algorithm to address the controller placement problem. For multi-link failures, we introduce the Monte Carlo Simulation to reduce the computational overhead. We conduct experiments with real network topologies, and the simulation results show that the heuristic algorithm can save significantly more time than the optimal algorithm, while achieving good performance.

With increasing demands for more flexible services, the routing policies in enterprise networks become much richer. This has placed a heavy burden to the current router forwarding plane in support of the increasing number of policies, primarily due to the limited capacity in TCAM, which further hinders the development of new network services and applications. The scalable forwarding table structures for enterprise networks have therefore attracted numerous attentions from both academia and industry. To tackle this challenge, in this paper we present the design and implementation of a new forwarding table structure. It separates the functions of TCAM and SRAM, and maximally utilizes the large and flexible SRAM. A set of schemes are progressively designed, to compress storage of forwarding rules, and maintain correctness and achieve line-card speeds of packet forwarding. We further design an incremental update algorithm that allows less access to memory. The proposed scheme is validated and evaluated through a realistic implementation on a commercial router using real datasets. Our proposal can be easily implemented in the existing devices. The evaluation results show that the performance of forwarding tables under the proposed scheme is promising.

With wide adoption of Internet of Things (IoT) across the world, the IoT devices are facing more and more intensive computation task nowadays. However, the IoT devices are usually limited by their computing capability and battery lifetime. Mobile edge computing provides new opportunities for developments of IoT, since edge computing servers which are close to devices can provide more powerful computing resources. The IoT devices can offload the intensive computing tasks to edge computing servers, while saving their own computing resources and reducing energy consumption. However, the benefits come at the cost of higher latency, mainly due to additional transmission time, and it may be unacceptable for many IoT applications. In this paper, we try to find a tradeoff between the energy consumption and latency, in order to satisfy user demands of various IoT applications. We formalize the problem into a constrained multiobjective optimization problem and find the optimal solutions by a modified fast and elitist nondominated sorting genetic algorithm (NSGA-II). To improve the performance of the algorithm, we propose a novel problem-specific encoding scheme and genetic operators in the proposed modified NSGA-II. We also conduct extensive simulation experiments to evaluate the proposed algorithm and its sensitivity under certain major parameters. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

The core concept of software-defined network (SDN) is the separation between control plane and date plane. SDN provides a programmatic interface to network control, significantly simplifies network management and improves the efficiency of network utilization. To further improve network performance, scalability and reliability, it is recommended to deploy multiple controllers in SDN. However, network performance would degrade if operators randomly deploy the controllers, especially in the case of failure, e.g., router crashes, fiber cuts, etc. In this paper, we try to optimally place controllers while taking network failures into account. First, we formally define two problems, 1) Controller Placement under Comprehensive Network States (CPCNS) problem; and 2) Controller Placement under Single Link Failure (CPSLF) problem. Secondly, We propose a network states traversal based algorithm, which optimally solve the problem; and further propose another greedy-based algorithm, which can solve the problem in polynomial time. Finally, we evaluate the algorithms using real topologies and empirical data. The results indicate that the new controller placement strategies can significantly improve the performance when link failures happen.

With increasing demands for more flexible services, the routing policies in enterprise network becomes much richer. This has placed a heavy burden to the current router forwarding plane to support the increasing number of policies, primarily due to the limited capacity in TCAM. This hinders the development of new network services. In this paper, we present the design and implementation of a new forwarding table structure. It separates the functions of TCAM and SRAM and maximally utilizes the large & flexible SRAM. We progressively design a set of schemes, to maintain correctness, compress storage, and achieve line-card speeds. We also design incremental update algorithms that bring less accesses to memory. We present implementation designs and evaluate our scheme with a real implementation on a commercial router using real data sets. Our design does not require new devices. The evaluation results show that the performance of our forwarding tables is promising.

Source address filtering is used as an important mechanism to prevent malicious traffic. Currently, most networks store filters in hardware such as TCAM, which has limited capacity, high power consumption and high cost. Although software can accommodate large number of filters, it needs multiple accesses to memory on the border router, which bears much more additional burden than other routers. In this paper, we propose a software-based mechanism for source address filtering. In our mechanism, we only need to check a few bits in source addresses on each router, rather than checking all bits on the ingress router. Through cooperation among routers, our mechanism ensures that malicious traffic will be filtered in the network. We formulate this problem as finding a cooperative scheme such that the loads on all routers are optimally balanced. We show that the problem can be optimally solved by dynamic programming. We evaluate our algorithms using comprehensive simulations with BRITE generated topologies and real world topologies. We conduct a case study on China Education and Research Network 2 (CERNET2) configurations, a large IPv6 network. Compared to checking 128-bit IP addresses on ingress routers, our algorithm checks at most 40 bits on each router.