Misconfiguration is frequently cited as a leading cause of service disruptions and outages. To prevent misconfiguration, we introduce network contracts—lightweight configuration checks that run efficiently, localize errors to specific lines, and require no heavyweight modeling of network protocols. We develop a tool Concord to learn contracts automatically from example network configurations. By checking these learned contracts against new or changed configurations, Concord finds likely configuration bugs before they can impact the network. Key to our approach is a scalable algorithm for learning "relational" contracts that capture complex dependencies between configuration settings. We deployed Concord as part of a cloud-based configuration management service and evaluated its scalability, coverage, precision, and utility on two large real-world configuration datasets.

Raha is the first general tool that can analyze probable degradation of traffic engineered networks under arbitrary failures and traffic shifts to prevent outages. Raha addresses a significant gap in prior work which consider only (1) ≤ k failures; (2) specific traffic engineering schemes; and (3) the maximum impact of failures irrespective of the network design point.

Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them more efficiently. Heuristic analyzers enable operators to find when and by how much their heuristics underperform. However, these tools do not provide enough detail for operators to mitigate the heuristic's impact in practice: they only discover a single input instance that causes the heuristic to underperform (and not the full set) and they do not explain why.

We propose a performance analysis tool for learning-enabled systems that allows operators to uncover potential performance issues before deploying DNNs in their systems. The tools that exist for this purpose require operators to faithfully model all components (a white-box approach) or do inefficient black-box local search. We propose a gray-box alternative, which eliminates the need to precisely model all the system's components. Our approach is faster and finds substantially worse scenarios compared to prior work. We show that a state-of-the-art learning-enabled traffic engineering pipeline can underperform the optimal by 6× --- a much higher number compared to what the authors found.

Cloud operators utilize collective communication optimizers to enhance the efficiency of the single-tenant, centrally managed training clusters they manage. However, current optimizers struggle to scale for such use cases and often compromise solution quality for scalability. Our solution, TE-CCL, adopts a traffic-engineering-based approach to collective communication. Compared to a state-of-the-art optimizer, TACCL, TE-CCL produced schedules with 2× better performance on topologies TACCL supports (and its solver took a similar amount of time as TACCL's heuristic-based approach). TECCL additionally scales to larger topologies than TACCL. On our GPU testbed, TE-CCL outperformed TACCL by 2.14× and RCCL by 3.18× in terms of algorithm bandwidth.

Configuration errors remain a major cause of system failures and service outages. One promising approach to identify configuration errors automatically is to learn common usage patterns (and anti-patterns) using data-driven methods. However, existing data-driven learning approaches analyze only simple configurations ( e.g. , those with no hierarchical structure), identify only simple types of issues ( e.g. , type errors), or require extensive domain-specific tuning. In this paper, we present D iffy , the first push-button configuration analyzer that detects likely bugs in structured configurations. From example configurations, D iffy learns a common template, with "holes" that capture their variation. It then applies unsupervised learning to identify anomalous template parameters as likely bugs. We evaluate D iffy on a large cloud provider’s wide-area network, an operational 5G network testbed, and MySQL configurations, demonstrating its versatility, performance, and accuracy. During D iffy ’s development, it caught and prevented a bug in a configuration timer value that had previously caused an outage for the cloud provider.

We discuss the potential improvement large language models (LLM) can provide in incident management and how they can overhaul the ways operators conduct incident management today. We propose a holistic framework for building an AI helper for incident management and discuss the several avenues of future research needed to achieve it.

Motivated by recent results that show that Internet protocols can be surprisingly complex and, in particular, that BGP is Turing complete, we ask the same question for the Domain Name System (DNS). DNS is at least as pervasive and essential as BGP in the global Internet infrastructure. Besides the scientific interest, the complexity of DNS can have implications for new applications (that can utilize the unsuspected power of DNS), and for verification (to understand basic complexity limits and suggest new verification algorithms). In this paper, we show that using the power of DNAME record type, DNS can express regular languages and pushdown systems. The first result can be used to build a system for controlling domain access (of which parental control is a special case). The second result shows that verification of DNS zone files is likely to take time that is at least cubic in the number of records.

We present a new approach for debugging two router configurations that are intended to be behaviorally equivalent. Existing router verification techniques cannot identify all differences or localize those differences to relevant configuration lines. Our approach addresses these limitations through a _modular_ analysis, which separately analyzes pairs of corresponding configuration components. It handles all router components that affect routing and forwarding, including configuration for BGP, OSPF, static routes, route maps and ACLs. Further, for many configuration components our modular approach enables simple _structural equivalence_ checks to be used without additional loss of precision versus modular semantic checks, aiding both efficiency and error localization. We implemented this approach in the tool Campion and applied it to debugging pairs of backup routers from different manufacturers and validating replacement of critical routers. Campion analyzed 30 proposed router replacements in a production cloud network and proactively detected four configuration bugs, including a route reflector bug that could have caused a severe outage. Campion also found multiple differences between backup routers from different vendors in a university network. These were undetected for three years, and depended on subtle semantic differences that the operators said they were "highly unlikely" to detect by "just eyeballing the configs."

The Domain Name System (DNS) plays a vital role in today's Internet but relies on complex distributed management of records. DNS misconfiguration related outages have rendered popular services like GitHub, HBO, LinkedIn, and Azure inaccessible for extended periods. This paper introduces GRoot, the first verifier that performs static analysis of DNS configuration files, enabling proactive and exhaustive checking for common DNS bugs; by contrast, existing solutions are reactive and incomplete. GRoot uses a new, fast verification algorithm based on generating and enumerating DNS query equivalence classes. GRoot symbolically executes the set of queries in each equivalence class to efficiently find (or prove the absence of) any bugs such as rewrite loops. To prove the correctness of our approach, we develop a formal semantic model of DNS resolution. Applied to the configuration files from a campus network with over a hundred thousand records, GRoot revealed 109 bugs within seconds. When applied to internal zone files consisting of over 3.5 million records from a large infrastructure service provider, GRoot revealed around 160k issues of blackholing, initiating a cleanup. Finally, on a synthetic dataset with over 65 million real records, we find GRoot can scale to networks with tens of millions of records.

IEEE 802.11ac supports Dynamic Bandwidth Channel Access (DBCA), where a wireless station dynamically selects the channel bandwidth based on the availability of the secondary channels. Although DBCA reduces the possibility of starvation due to non-availability of secondary channels, however, to the best of our knowledge, no existing works look into the performance benefits of IEEE 802.11ac DBCA based on theoretical modeling. In this paper, we develop a two dimensional Markov chain approach to model the performance of DBCA under various channel bonding conditions. We validate the proposed model based on a real testbed implementation. From the thorough analysis of the numerical results obtained from the model, we show that although DBCA improves channel utilization for secondary channels, it requires proper channel allocations and bonding level distributions across the wireless channels for reducing unfairness in the network. We observe that under certain circumstances, the secondary channel users can affect the throughput of primary channel users, which may introduce a short-term unfairness and a significant performance drop in the network.