The rapid advancement of the satellite industry offers unprecedented opportunities for enabling Internet of Everything (IoE) applications over satellite networks. A key characteristic of such applications is that computation cannot begin until the entire application data has been fully received at the destination. To meet strict end-to-end delay constraints, minimizing the total application delay is essential. However, this requirement violates the optimal substructure property commonly assumed in traditional shortest path routing problems. Existing routing solutions often overlook these unique computation constraints and rely on substructure-preserving heuristics, resulting in suboptimal delay performance. Moreover, they lack reliability in producing delay-guaranteed routing solutions, which leads to low task completion ratios under stringent application deadlines. To overcome this problem, we propose FlexSatIoEa routing scheme that allows for flexible buffering data over satellite networks. FlexSatIoE formulates this routing problem as an integer linear programming (ILP) problem, to provide the optimal solution. As the network scales, considering the computational intractability of ILP, FlexSatIoE further modifies the storage time-aggregated graph to comprehensively model the satellite networks’ compute, storage and transmission resources. Based on the graph extension, FlexSatIoE designs an efficient routing algorithm, enabling flexible use of buffer resources by using a flow reassignment mechanism. We conduct extensive experiments over the setting of real-world satellite networks. The results show that FlexSatIoE reduces the average delay and increases the number of completed tasks by up to 50% and 40% respectively, as compared to the existing schemes, demonstrating the superior capability and reliability of FlexSatIoE in ensuring deterministic application delays.

Time-Triggered Ethernet (TTEthernet) has been widely applied in many scenarios such as industrial internet, automotive electronics, and aerospace, where offline routing and scheduling for TTEthernet has been largely investigated. However, predetermined routes and schedules cannot meet the demands in some agile scenarios, such as smart factories, autonomous driving, and satellite network switching, where the transmission requests join in and leave the network frequently. Thus, we study the online joint routing and scheduling problem for TTEthernet. However, balancing efficient and effective routing and scheduling in an online environment can be quite challenging. To ensure high-quality and fast routing and scheduling, we first design a time-slot expanded graph (TSEG) to model the available resources of TTEthernet over time. The fine-grained representation of TSEG allows us to select a time slot via selecting an edge, thus transforming the scheduling problem into a simple routing problem. Next, we design a dynamic weighting method for each edge in TSEG and further propose an algorithm to co-optimize the routing and scheduling. Our scheme enhances the TTEthernet throughput by co-optimizing the routing and scheduling to eliminate potential conflicts among flow requests, as compared to existing methods. The extensive simulation results show that our scheme runs >400 times faster than standard solutions (i.e., ILP solver), while the gap is only 2% to the optimally scheduled number of flow requests. Besides, as compared to existing schemes, our method can improve the successfully scheduled number of flows by more than 18%.

The rapid growth of Internet-of- Things (IoT) systems demands higher throughput to process sensor data. Existing data processing platforms use simple heuristics for task placement, which perform poorly. We proposed a Permutation-based Task Placement Optimizer (PTPO) that constructs a set of valid task placement permutations to formulate a mixed-integer linear programming problem. PTPO enables efficient real-time task placement for multiple dynamic applications. Our study highlights three key design factors: joint consideration of compute and network constraints, accurate profiling of resource needs, and fine-grained splitting of tasks across nodes. We demonstrate more than 80% throughput gain compared to state-of-the-art schemes using real-world IoT Applications.

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

How to allocate network paths and their resources to minimize the delivery time of data transfer tasks over time-varying networks? Solving this MDDT (Minimizing Data Delivery Time) problem has important applications from data centers to delay-tolerant networking. In particular, with the rapid deployment of satellite networks in recent years, an efficient MDDT solver will serve as a key building block there.The MDDT problem can be solved in polynomial time by finding the maximum flow in a time-expanded graph. A binary-search-based solver incurs O(N•log N•Γ) time complexity, where N corresponds to time horizon and Γ is the time complexity to solve a maximum flow problem for one snapshot of the network. In this work, we design a one-pass solver that progressively expands the graph over time until it reaches the earliest time interval n to complete the delivery. By reusing the calculated maximum flow results from earlier iterations, it solves the MDDT problem while incurring only O(nΓ) time complexity for algorithms that can apply our technique. We apply the one-pass design to Ford-Fulkerson algorithm and evaluate our solver using a network of 184 satellites from Starlink constellations. We demonstrate >75× speed-up in the running time and show that our solution can also enable advanced applications such as preemptive scheduling.

Virtual Reality (VR) applications delivered over wireless networks have attracted interest from academia and industry. The delay of VR applications is mainly composed of computing delay and communication delay. Although cloud computing centers have adequate computing power, accessing them requires long communication delay. Mobile edge computing (MEC), which offloads the computing power from the cloud computing center to the edge, is regarded as a feasible way to alleviate communication delay. However, due to the differences in the capability and location of MEC nodes, the selection of MEC nodes will affect both the computing delay and communication delay. In this paper, we focus on the joint representation of computing and communication resources and the selection of the optimal MEC node. First, we adopt graph-based joint computing and communication resources (GCC) model for VR applications routing and formulate the VR routing problem as an ILP problem. Then we design a Computing Nodes Expanded (CNE) algorithm, which allows us to use the Dijkstra algorithm to quickly obtain the optimal computing node and the path of shortest total delay. Finally, we run numerical experiments to evaluate the performance of the proposal algorithm. Simulation shows that the CNE algorithm can reduce the total delay by 42.9% and increase the delay satisfaction ratio by 23.3% compared to other benchmark algorithms.

Earth observation satellites using asynchronous ground-assisted federated learning (FL) can avoid transmitting massive raw image data to ground. However, current FL approach uses only satellite-to-ground-station links, causing long delay for model parameter transfer. A promising direction to reduce delay is to use inter-satellite links. We identify that ground-assisted asynchronous FL requires a satellite to send all data of its model parameter to ground before ground station can start to update the model. This new feature prevents current routing algorithms (e.g., CGR) from being applicable. Therefore, we propose an FL task-aware routing and resource reservation (FLRRS) scheme to optimize the delay of FL model parameter transfer. First, we formulate the problem as an integer linear programming (ILP) problem, which is non-convex and intractable. Thus, we enhance the storage time-aggregated graph to model computing, storage and transmission resources of satellite network, and propose a graph-based routing and resource reservation algorithm. The numerical simulation based on a real-world satellite network shows that FLRRS runs much faster than CVXPY solver. Besides, FLRRS also significantly improves average delay and number of completed tasks, as compared to current routing algorithms.

Earth observation systems play important roles in many critical applications. The rapid increase of the number of satellites and their sensing capability, however, makes it challenging to send the massive amount of observed data back to the Earth. One promising direction to enhance the earth observation throughput is to use inter-satellite communication. Towards this, we identify two key design factors: 1) the capability to support on-demand scheduling of inter-satellite communication; and 2) the capability to co-optimize the scheduling of observation and transmission missions. For both, rigorous study is needed to determine whether they provide sufficient throughput gain to justify their additional complexity. Our work formulates a generic earth observation and transmission problem to study the maximum network throughput under different settings. By succinctly modeling the different constraints using a generalized time-varying graph representation, we can efficiently find the optimal scheduling solutions. We conduct an extensive study, which shows that using 40 relay satellites from the “starlink” constellation can increase the throughput of 10 sensing satellites from the “Gaofen” constellation by more than 400%. In particular, on-demand scheduling under heavy load and co-optimization of observation/transmission under light but time-critical load can improve the throughput by more than 180% and 100%, respectively.