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Sam Bayless

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5 papers

2 author rows

AIJ Journal 2020 Journal Article

Scalable constraint-based virtual data center allocation

Sam Bayless
Nodir Kodirov
Syed M. Iqbal
Ivan Beschastnikh
Holger H. Hoos
Alan J. Hu

Constraint-based techniques can solve challenging problems arising in highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To address this problem, we introduce Netsolver, a system for VDC allocation that is based on constraint solving. Netsolver represents a major improvement over existing approaches: it is sound, complete, and scalable, providing support for end-to-end, multi-path bandwidth guarantees across all the layers of hosting infrastructure, from servers to top-of-rack switches to aggregation switches to access routers. Netsolver scales to realistic data center sizes and VDC topologies, typically requiring just seconds to allocate VDCs of 5–15 virtual machines to physical data centers with 1000+ servers, maintaining this efficiency even when the data center is nearly saturated. In many cases, Netsolver can allocate 150 % − 300 % as many total VDCs to the same physical data center as previous methods. Finally, we show how Netsolver can be extended with additional optimization constraints, such as VM affinity and hotspot minimization, demonstrating the flexibility of our approach. The performance and flexibility of Netsolver are made possible by our formalization of the VDC allocation problem in terms of multi-commodity flows, and the corresponding efficient handling of network flow problems in the underlying constraint solvers. This shows the importance of supporting flow-based constraints, which are more mature in ILP- vs. SMT-based constraint solving.

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IJCAI Conference 2017 Conference Paper

Scalable Constraint-based Virtual Data Center Allocation

Sam Bayless
Nodir Kodirov
Ivan Beschastnikh
Holger H. Hoos
Alan J. Hu

Constraint-based techniques can solve challenging problems arising from highly diverse applications. This paper considers the problem of virtual data center (VDC) allocation, an important, emerging challenge for modern data center operators. To solve this problem, we introduce NETSOLVER, which is based on the general-purpose constraint solver MONOSAT. NETSOLVER represents a major improvement over existing approaches: it is sound, complete, and scalable, providing support for end-to-end, multi-path bandwidth guarantees across all the layers of hosting infrastructure, from servers to top-of-rack switches to aggregation switches to access routers. NETSOLVER scales to realistic data center sizes and VDC topologies, typically requiring just seconds to allocate VDCs of 5–15 virtual machines to physical data centers with 1000+ servers, maintaining this efficiency even when the data center is nearly saturated. In many cases, NETSOLVER can allocate 150%−300% as many total VDCs to the same physical data center as previous methods. Essential to our solution efficiency is our formulation of VDC allocation using monotonic theories, illustrating the practical value of the recently proposed SAT modulo monotonic theories approach.

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AIJ Journal 2017 Journal Article

The Configurable SAT Solver Challenge (CSSC)

Frank Hutter
Marius Lindauer
Adrian Balint
Sam Bayless
Holger Hoos
Kevin Leyton-Brown

It is well known that different solution strategies work well for different types of instances of hard combinatorial problems. As a consequence, most solvers for the propositional satisfiability problem (SAT) expose parameters that allow them to be customized to a particular family of instances. In the international SAT competition series, these parameters are ignored: solvers are run using a single default parameter setting (supplied by the authors) for all benchmark instances in a given track. While this competition format rewards solvers with robust default settings, it does not reflect the situation faced by a practitioner who only cares about performance on one particular application and can invest some time into tuning solver parameters for this application. The new Configurable SAT Solver Competition (CSSC) compares solvers in this latter setting, scoring each solver by the performance it achieved after a fully automated configuration step. This article describes the CSSC in more detail, and reports the results obtained in its two instantiations so far, CSSC 2013 and 2014.

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AAAI Conference 2015 Conference Paper

SAT Modulo Monotonic Theories

Sam Bayless
Noah Bayless
Holger Hoos
Alan Hu

Boolean satisfiability (SAT) solvers have been successfully applied to a wide variety of difficult combinatorial problems. Many further problems can be solved by SAT Modulo Theory (SMT) solvers, which extend SAT solvers to handle additional types of constraints. However, building efficient SMT solvers is often very difficult. In this paper, we define the concept of a Boolean monotonic theory and show how to easily build efficient SMT solvers, including effective theory propagation and clause learning, for such theories. We present examples showing useful constraints that are monotonic, including many graph properties (e. g. , shortest paths), and geometric properties (e. g. , convex hulls). These constraints arise in problems that are otherwise difficult for SAT solvers to handle, such as procedural content generation. We have implemented several monotonic theory solvers using the techniques we present in this paper and applied these to content generation problems, demonstrating major speed-ups over SAT, SMT, and Answer Set Programming solvers, easily solving instances that were previously out of reach.

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SAT Conference 2012 Conference Paper

Single-Solver Algorithms for 2QBF - (Poster Presentation)

Sam Bayless
Alan J. Hu

Abstract 2QBF is a restriction of QBF, in which at most one quantifier alternation is allowed. This simplifying assumption makes the problem easier to reason about, and allows for simpler unit propagation and clause/cube learning procedures. We introduce two new 2QBF algorithms that take advantage of 2QBF specifically. The first improves upon earlier work by Ranjan, Tang, and Malik (2004), while the second introduces a new ‘free’ decision heuristic that doesn’t need to respect quantifier order. Implementations of both new algorithms perform better than two state-of-the-art general QBF solvers on formal verification and AI planning instances.

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