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Supply chain operations traditionally involve a variety of complex decision making problems. Over the last few decades, supply chains greatly benefited from advances in computation, which allowed the transition from manual processing to automation and cost-effective optimization. Nonetheless, business operators still need to spend substantial efforts in explaining and interpreting the optimization outcomes to stakeholders. Motivated by the recent advances in Large Language Models (LLMs), we study how this disruptive technology can help bridge the gap between supply chain automation and human comprehension and trust thereof. We design OptiGuide -- a framework that accepts as input queries in plain text, and outputs insights about the underlying optimization outcomes. Our framework does not forgo the state-of-the-art combinatorial optimization technology, but rather leverages it to quantitatively answer what-if scenarios (e.g., how would the cost change if we used supplier B instead of supplier A for a given demand?). Importantly, our design does not require sending proprietary data over to LLMs, which can be a privacy concern in some circumstances. We demonstrate the effectiveness of our framework on a real server placement scenario within Microsoft's cloud supply chain. Along the way, we develop a general evaluation benchmark, which can be used to evaluate the accuracy of the LLM output in other scenarios.

Routing is, arguably, the most fundamental task in computer networking, and the most extensively studied one. A key challenge for routing in real-world environments is the need to contend with uncertainty about future traffic demands. We present a new approach to routing under demand uncertainty: tackling this challenge as stochastic optimization, and employing deep learning to learn complex patterns in traffic demands. We show that our method provably converges to the global optimum in well-studied theoretical models of multicommodity flow. We exemplify the practical usefulness of our approach by zooming in on the real-world challenge of traffic engineering (TE) on wide-area networks (WANs). Our extensive empirical evaluation on real-world traffic and network topologies establishes that our approach's TE quality almost matches that of an (infeasible) omniscient oracle, outperforming previously proposed approaches, and also substantially lowers runtimes., To appear at NSDI 2023

Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker. We model these problems as Exo-MDPs (Markov Decision Processes with Exogenous Inputs) and design a class of data-efficient algorithms for them termed Hindsight Learning (HL). Our HL algorithms achieve data efficiency by leveraging a key insight: having samples of the exogenous variables, past decisions can be revisited in hindsight to infer counterfactual consequences that can accelerate policy improvements. We compare HL against classic baselines in the multi-secretary and airline revenue management problems. We also scale our algorithms to a business-critical cloud resource management problem -- allocating Virtual Machines (VMs) to physical machines, and simulate their performance with real datasets from a large public cloud provider. We find that HL algorithms outperform domain-specific heuristics, as well as state-of-the-art reinforcement learning methods., Comment: 53 pages, 6 figures

The cloud computing industry has grown rapidly over the last decade, and with this growth there is a significant increase in demand for compute resources. Demand is manifested in the form of Virtual Machine (VM) requests, which need to be assigned to physical machines in a way that minimizes resource fragmentation and efficiently utilizes the available machines. This problem can be modeled as a dynamic version of the bin packing problem with the objective of minimizing the total usage time of the bins (physical machines). Earlier works on dynamic bin packing assumed that no knowledge is available to the scheduler and later works studied models in which lifetime/duration of each "item" (VM in our context) is available to the scheduler. This extra information was shown to improve exponentially the achievable competitive ratio. Motivated by advances in Machine Learning that provide good estimates of workload characteristics, this paper studies the effect of having extra information regarding future (total) demand. In the cloud context, since demand is an aggregate over many VM requests, it can be predicted with high accuracy (e.g., using historical data). We show that the competitive factor can be dramatically improved by using this additional information; in some cases, we achieve constant competitiveness, or even a competitive factor that approaches 1. Along the way, we design new offline algorithms with improved approximation ratios for the dynamic bin-packing problem., Comment: 30 pages

We study dynamic models of online-advertising auctions in the Internet: advertisers compete for space on a web page over multiple time periods, and the web page displays ads in differentiated slots based on their bids and other considerations. The complex interactions between the advertisers and the website (which owns the web page) is modeled as a dynamic game. Our goal is to derive ad-slot placement and pricing strategies which maximize the expected revenue of the website. We show that the problem can be transformed into a scheduling problem familiar to queueing theorists. When only one advertising slot is available on a webpage, we derive the optimal revenue-maximizing solution by making connections to the familiar cμ rule used in queueing theory. More generally, we show that a cμ-like rule can serve as a good suboptimal solution, while the optimal solution itself may be computed using dynamic programming techniques.

The cloud computing paradigm offers easily accessible computing resources of variable size and capabilities. We consider a cloud-computing facility that provides simultaneous service to a heterogeneous, time-varying population of users, each associated with a distinct job. Both the completion time, as well as the user's utility, may depend on the amount of computing resources applied to the job. In this paper, we focus on the objective of maximizing the long-term social surplus, which comprises of the aggregate utility of executed jobs minus load-dependent operating expenses. Our problem formulation relies on basic notions of welfare economics, augmented by relevant queueing aspects. We first analyze the centralized setting, where an omniscient controller may regulate admission and resource allocation to each arriving job based on its individual type. Under appropriate convexity assumptions on the operating costs and individual utilities, we establish existence and uniqueness of the social optimum. We proceed to show that the social optimum may be induced by a single per-unit price, which charges a fixed amount per unit time and resource from all users.

We consider scheduling over a wireless system, where the channel state information is not available a priori to the scheduler, but can be inferred from the past. Specifically, the wireless system is modeled as a network of parallel queues. We assume that the channel state of each queue evolves stochastically as an ON/OFF Markov chain. The scheduler, which is aware of the queue lengths but is oblivious of the channel states, has to choose one queue at a time for transmission. The scheduler has no information regarding the current channel states, but can estimate them by using the acknowledgment history. We first characterize the capacity region of the system using tools from Markov Decision Processes (MDP) theory. Specifically, we prove that the capacity region boundary is the uniform limit of a sequence of Linear Programming (LP) solutions. Next, we combine the LP solution with a queue length based scheduling mechanism that operates over long `frames,' to obtain a throughput optimal policy for the system. By incorporating results from MDP theory within the Lyapunov-stability framework, we show that our frame-based policy stabilizes the system for all arrival rates that lie in the interior of the capacity region., National Science Foundation (U.S.) (NSF grants CNS-0626781), National Science Foundation (U.S.) (NSF grant CNS-0915988), United States. Army Research Office (ARO Muri Grant W911NF-08-1-0238), Israel Science Foundation (contract 890015)

In this paper we introduce a novel flow representation for finite games in strategic form. This representation allows us to develop a canonical direct sum decomposition of an arbitrary game into three components, which we refer to as the potential, harmonic, and nonstrategic components. We analyze natural classes of games that are induced by this decomposition, and in particular, focus on games with no harmonic component and games with no potential component. We show that the first class corresponds to the well-known potential games. We refer to the second class of games as harmonic games, and demonstrate that this new class has interesting properties which contrast with properties of potential games. Exploiting the decomposition framework, we obtain explicit expressions for the projections of games onto the subspaces of potential and harmonic games. This enables an extension of the equilibrium properties of potential and harmonic games to “nearby” games., National Science Foundation (U.S.). (Grant number DMI- 0545910), National Science Foundation (U.S.). (Grant number ECCS-0621922), United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Grant number FA9550-06-1-0303), National Science Foundation (U.S.). (Grant number FRG 0757207), United States. Defense Advanced Research Projects Agency. Information Theory for Mobile Ad-Hoc Networks Program, Marie Curie International Fellowship. (7th European Community Framework Programme)

We study power control in a multi-cell CDMA wireless system whereby self-interested users share a common spectrum and interfere with each other. Our objective is to design a power control scheme that achieves a (near) optimal power allocation with respect to any predetermined network objective (such as the maximization of sum-rate, or some fairness criterion). To obtain this, we introduce the potential-game approach that relies on approximating the underlying noncooperative game with a "close" potential game, for which prices that induce an optimal power allocation can be derived. We use the proximity of the original game with the approximate game to establish through Lyapunov-based analysis that natural user-update schemes (applied to the original game) converge within a neighborhood of the desired operating point, thereby inducing near-optimal performance in a dynamical sense. Additionally, we demonstrate through simulations that the actual performance can in practice be very close to optimal, even when the approximation is inaccurate. As a concrete example, we focus on the sum-rate objective, and evaluate our approach both theoretically and empirically., National Science Foundation (U.S.) (DMI-05459100), National Science Foundation (U.S.) (DMI-0545910), United States. Defense Advanced Research Projects Agency (ITMANET program), 7th European Community Framework Programme (Marie Curie International Fellowship)