Tie Ma

We present MAESTRO, an evaluation suite for the testing, reliability, and observability of LLM-based MAS. MAESTRO standardizes MAS configuration and execution through a unified interface, supports integrating both native and third-party MAS via a repository of examples and lightweight adapters, and exports framework-agnostic execution traces together with system-level signals (e.g., latency, cost, and failures). We instantiate MAESTRO with 12 representative MAS spanning popular agentic frameworks and interaction patterns, and conduct controlled experiments across repeated runs, backend models, and tool configurations. Our case studies show that MAS executions can be structurally stable yet temporally variable, leading to substantial run-to-run variance in performance and reliability. We further find that MAS architecture is the dominant driver of resource profiles, reproducibility, and cost-latency-accuracy trade-off, often outweighing changes in backend models or tool settings. Overall, MAESTRO enables systematic evaluation and provides empirical guidance for designing and optimizing agentic systems.

Edge computing platforms are increasingly deployed to support delay-sensitive and resource-intensive applications. However, current task scheduling strategies, which rely on user-requested resources, often lead to low resource utilization and reduced platform profit due to users’ tendency to over-request resources. Overbooking, widely adopted in industries such as airlines and hotels, can improve utilization but introduce risk under uncertain task resource usage. This paper applies resource overbooking to edge clouds, focusing on task scheduling optimization under uncertainty. We formulate the problem as a stochastic mixed integer program, which is proven to be NP-hard. To this end, a risk evaluation scheme is proposed to accurately quantify the risk of overload without assuming specific resource usage distributions, which has an additive error guarantee. Based on this scheme, we transform the problem into a more deterministic form and prove that the objective function is submodular. This enables us to design a greedy algorithm that achieves a ((1-1/e))-approximation ratio with lower complexity. Extensive experiments on a real-world dataset demonstrate that the proposed algorithm significantly improves profit by 0.23(\times)-3.35(\times) and resource utilization by 0.16(\times)-0.75(\times) while accurately controlling the risk associated with overbooking.

Currently, one of the simplest and most effective ways for people to gain an in-depth understanding of computer networks is through hands-on practice and experimentation on software platforms. While education is important for the field of computer networks, existing platforms are inadequate in usability and scalability, failing to fully meet all the teaching needs of computer networking education. This paper describes our experiences in designing and using Klonet, an emulation platform for computer networking education. Klonet is easy-to-use for both students and tutors, which has been carefully designed to lower the barrier to use, thus making the practice more efficient. Klonet also demonstrates good scalability. It adopts a container-based distributed architecture and a virtual network embedding algorithm customized for this platform. Evaluation experiments show that Klonet exhibits better scalability, such as supporting more students with fewer hardware resources (i.e., servers) and deploying virtual network topologies more quickly. Furthermore, to ensure stability during teaching, Klonet enhances the robustness of its upper orchestrator and underlying virtual networks. So far, Klonet has been adopted in 3 universities and 4 courses, serving more than 800 students. We showcase Klonet’s usefulness in networking education with real use cases, including a scenario with  10,000 emulated routers. We also share our lessons learned from the 4 years of Klonet development and 2 years of operations.

Measuring traffic metrics is indispensable in virtual networks as it is the basis for a wide range of applications, such as network diagnostics and performance evaluation of the network algorithms. However, existing measurement schemes fail to have all these excellent characteristics simultaneously: 1) fine-grained, i.e. to obtain per packet level information. 2) lightweight, namely low CPU and bandwidth overhead. 3) network-wide, which means obtaining metrics of the whole network, e.g. per packet path. 4) easy-to-deploy, which refers to deployment without additional modification of Maximum Transmission Units (MTUs). We design vNetRadar, a virtual network measurement system, which has these excellent characteristics simultaneously. Specifically, vNetRadar 1) identifies each packet without increasing the size of each packet, to obtain network-wide metrics without MTU modification, 2) allocates each packet an area in memory, called backpack, and carries metadata in it to largely reduce bandwidth overhead. vNetRadar is implemented based on the extended Berkeley Packet Filter (eBPF) and is mainly in kernel space, avoiding the CPU overhead of copying packets to user space when performing the fine-grained measurement. Evaluation results show that the easy-to-deploy vNetRadar can get fine-grained network-wide metrics with low CPU and bandwidth overhead.

As the key infrastructure for emerging 5G and Internet-of-Things (IoT) applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication on demand. Accordingly, reducing delivery time is crucial for the optimization of service delay and system utilization. To accelerate the delivery, this article proposes a multisource-aware adaptive data transmission solution, Parallel Push (PPUSH), by leveraging the fact that data objects in the cloud are generally replicated among servers by design. At the high level, PPUSH achieves efficient delivery of multisource data by launching multiple push flows in parallel; and at the low level, it decouples transfers from different sources by encoding data objects with rateless RaptorQ code, and further employing novel congestion controls to prioritize the bandwidth allocation of concurrent tasks respecting their remaining sizes. Fluid model analysis along with Mininet-based test and packet-level simulation shows that, unlike DCTCP and other proposals, push is robust to packet loss and achieves provable prioritized bandwidth allocation. Extensive simulation results imply that, with above advantages, PPUSH could achieve very efficient data delivery by making use of all available data sources: for instance, compared with the straightforward design of equal-size task split and fair bandwidth allocation, its adaptive task assignment and prioritized traffic scheduling reduce the average task completion time in a tested scenario by 1.495× and 1.329×, respectively, demonstrating a total improvement of 1.586×, when enabled at the same time.