Person:
Crouse, Michael

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Crouse

First Name

Michael

Name

Crouse, Michael

Filters

2015 - 20162

Settings

Author's Publications: search.filters.isAuthorOfPublication.69886372-bdb2-4bda-9fc4-13885f9268c7

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Nested Buddy System: A New Block Address Allocation Scheme for ISPs and IaaS Providers
    (IEEE, 2016) Crouse, Michael; Kung, H.
    We propose a novel block address allocation method, called the nested buddy system, which can make use of wasted areas in the classical buddy system due to internal fragmentation. While achieving high utilization of address space, our new scheme supports efficient address matching for routers in packet forwarding and for network middleboxes in packet filtering. Specifically, the scheme uses just one prefix rule for each allocated address block in a packet routing/filtering table. We show by analysis and simulation that the increased address utilization can lead to significant reduction in the probability of a denial-of-service under bursty address allocation requests. In contrast, the classical buddy system requires the aggregation of many requests over time to smooth out demand, resulting in service delays undesirable to end users. Our solution is applicable to ISPs in serving mobile users carrying many network connected IoT devices and IasS providers in the cloud in serving tenants with dynamically varying demands for network addresses.
  • Thumbnail Image
    Publication
    Taming Wireless Fluctuations by Predictive Queuing Using a Sparse-Coding Link-State Model
    (Association of Computing Machinery, 2015) Tarsa, Stephen; Comiter, Marcus; Crouse, Michael; McDanel, Bradley; Kung, H.
    We introduce State-Informed Link-Layer Queuing (SILQ), a system that models, predicts, and avoids packet delivery failures due to temporary wireless outages in everyday scenarios. By stabilizing connections in adverse link conditions, SILQ boosts throughput and reduces performance variation for network applications, for example by preventing unnecessary TCP timeouts caused by dead zones, elevators, and subway tunnels. SILQ makes predictions in real-time by actively probing links, matching measurements to an overcomplete dictionary of patterns learned offline, and classifying the resulting sparse feature vectors to identify those that precede outages. We use a clustering method called sparse coding to build our data-driven link model, and show that it produces more variation-tolerant predictions than traditional loss-rate, location-based, or Markov chain techniques. We present extensive data collection and field-validation of SILQ in airborne, indoor, and urban scenarios of practical interest. We show how offline unsupervised learning discovers link-state patterns that are stable across diverse networks and signal-propagation environments. Using these canonical primitives, we train outage predictors for 802.11 (Wi-Fi) and 3G cellular networks to demonstrate TCP throughput gains of 4x with off-the-shelf mobile devices. SILQ addresses delivery failures solely at the link layer, requires no new hardware, and upholds the end-to-end design principle, enabling easy integration across applications, devices, and networks.