Experimentation tools facilitate exploration of Tor performance and security research problems and allow researchers to safely and privately conduct Tor experiments without risking harm to real Tor users. However, researchers using these tools configure them to generate network traffic based on simplifying assumptions and outdated measurements and without understanding the efficacy of their configuration choices. In this work, we design a novel technique for dynamically learning Tor network traffic models using hidden Markov modeling and privacy-preserving measurement techniques. We conduct a safe but detailed measurement study of Tor using 17 relays (~2% of Tor bandwidth) over the course of 6 months, measuring general statistics and models that can be used to generate a sequence of streams and packets. We show how our measurement results and traffic models can be used to generate traffic flows in private Tor networks and how our models are more realistic than standard and alternative network traffic generation~methods.
more »
« less
On the Accuracy of Tor Bandwidth Estimation
The Tor network estimates its relays’ bandwidths using relay self-measurements of client traffic speeds. These estimates largely determine how existing traffic load is balanced across relays, and they are used to evaluate the network’s capacity to handle future traffic load increases. Thus, their accuracy is important to optimize Tor’s performance and strategize for growth. However, their accuracy has never been measured. We investigate the accuracy of Tor’s capacity estimation with an analysis of public network data and an active experiment run over the entire live network. Our results suggest that the bandwidth estimates underestimate the total network capacity by at least 50% and that the errors are larger for high-bandwidth and low-uptime relays. Our work suggests that improving Tor’s bandwidth measurement system could improve the network’s performance and better inform plans to handle future growth.
more »
« less
- Award ID(s):
- 1925497
- NSF-PAR ID:
- 10287723
- Date Published:
- Journal Name:
- Passive and Active Measurement (PAM)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Edge data centers are an appealing place for telecommunication providers to offer in-network processing such as VPN services, security monitoring, and 5G. Placing these network services closer to users can reduce latency and core network bandwidth, but the deployment of network functions at the edge poses several important challenges. Edge data centers have limited resource capacity, yet network functions are re-source intensive with strict performance requirements. Replicating services at the edge is needed to meet demand, but balancing the load across multiple servers can be challenging due to diverse service costs, server and flow heterogeneity, and dynamic workload conditions. In this paper, we design and implement a model-based load balancer EdgeBalance for edge network data planes. EdgeBalance predicts the CPU demand of incoming traffic and adaptively distributes flows to servers to keep them evenly balanced. We overcome several challenges specific to network processing at the edge to improve throughput and latency over static load balancing and monitoring-based approaches.more » « less
-
null (Ed.)Virtual Network Functions (VNFs) are software implementation of middleboxes (MBs) (e.g., firewalls) that provide performance and security guarantees for virtual machine (VM) cloud applications. In this paper we study a new flow migration problem in VNF-enabled cloud data centers where the traffic rates of VM flows are constantly changing. Our goal is to minimize the total network traffic (therefore optimizing the network resources such as bandwidth and energy) while considering that VNFs have limited processing capability. We formulate the flow migration problem and design two efficient benefit-based greedy algorithms. The simulations show that our algorithms are effective in reducing the network traffic as well as in achieving load balance among VNFs. In particular, our flow migration algorithms can reduce upto 15% network traffic compared to the case without flow migration.more » « less
-
Future tactical communications involves high data rate best effort traffic working alongside real-time traffic for time-critical applications with hard deadlines. Unavailable bandwidth and/or untimely responses may lead to undesired or even catastrophic outcomes. Ethernet-based communication systems are one of the major tactical network standards due to the higher bandwidth, better utilization, and ability to handle heterogeneous traffic. However, Ethernet suffers from inconsistent performance for jitter, latency and bandwidth under heavy loads. The emerging Time-Triggered Ethernet (TTE) solutions promise deterministic Ethernet performance, fault-tolerant topologies and real-time guarantees for critical traffic. In this paper we study the TTE protocol and build a TTTech TTE test bed to evaluate its performance. Through experimental study, the TTE protocol was observed to provide consistent high data rates for best effort messages, determinism with very low jitter for time-triggered messages, and fault-tolerance for minimal packet loss using redundant networking topologies. In addition, challenges were observed that presented a trade-off between the integration cycle and the synchronization overhead. It is concluded that TTE is a capable solution to support heterogeneous traffic in time-critical applications, such as aerospace systems (eg. airplanes, spacecraft, etc.), ground-based vehicles (eg. trains, buses, cars, etc), and cyber-physical systems (eg. smart-grids, IoT, etc.).more » « less
-
The Dragonfly networks have been adopted in the current supercomputers, and will be deployed in future generation supercomputers and data centers. Effective routing on Dragonfly is challenging. Universal Globally Adaptive Load-balanced routing (UGAL) is the state-of-the-art routing algorithm for Dragonfly. For each packet, UGAL selects either a minimal path or a non-minimal path based on their estimated latencies. Practical UGAL makes routing decisions with local information, deriving the estimated latency for each path from the local queue occupancy and path hop count information. In this work, we develop techniques to improve the accuracy of the latency estimation for UGAL with local information, which results in more effective routing decisions. In particular, our schemes are able to proactively mitigate the potential network congestion with imbalanced network traffic. Extensive simulation experiments using synthetic traffic patterns and application workloads demonstrate that our enhanced UGAL schemes significantly improve the routing performance for many common traffic conditions.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-72582-2_28
