Truthful Online Scheduling with Commitments

We study online mechanisms for preemptive scheduling with deadlines, with the goal of maximizing the total value of completed jobs. This problem is fundamental to deadline-aware cloud scheduling, but there are strong lower bounds even for the algorithmic problem without incentive constraints. However, these lower bounds can be circumvented under the natural assumption of deadline slackness, i.e., that there is a guaranteed lower bound $s > 1$ on the ratio between a job's size and the time window in which it can be executed. In this paper, we construct a truthful scheduling mechanism with a constant competitive ratio, given slackness $s > 1$. Furthermore, we show that if $s$ is large enough then we can construct a mechanism that also satisfies a commitment property: it can be determined whether or not a job will finish, and the requisite payment if so, well in advance of each job's deadline. This is notable because, in practice, users with strict deadlines may find it unacceptable to discover only very close to their deadline that their job has been rejected

Dynamic Online-Advertising Auctions as Stochastic Scheduling

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

Non-Cooperative Spectrum Access -- The Dedicated vs. Free Spectrum Choice

We consider a dynamic spectrum access system in which Secondary Users (SUs) choose to either acquire dedicated spectrum or to use spectrum-holes (white spaces) which belong to Primary Users (PUs). The trade-off incorporated in this decision is between immediate yet costly transmission and free but delayed transmission (a consequence of both the possible appearance of PUs and sharing the spectrum holes with multiple SUs). We first consider a system with a single PU band, in which the SU decisions are fixed. Employing queueing-theoretic methods, we obtain explicit expressions for the expected delays associated with using the PU band. Based on that, we then consider self-interested SUs and study the interaction between them as a non-cooperative game. We prove the existence and uniqueness of a symmetric Nash equilibrium, and characterize the equilibrium behavior explicitly. Using our equilibrium results, we show how to maximize revenue from renting dedicated bands to SUs and briefly discuss the extension of our model to multiple PUs. Finally, since spectrum sensing can be resource-consuming, we characterize the gains provided by this capability.National Science Foundation (U.S.) (Grant CNS-0915988)National Science Foundation (U.S.) (Grant CNS-0916263)National Science Foundation (U.S.) (Grant CNS-1054856)National Science Foundation (U.S.). Engineering Research Centers Program (Center for Integrated Access Networks Grant EEC-0812072)United States. Office of Naval Research (Grant N00014-12-1-0064)United States. Army Research Office. Multidisciplinary University Research Initiative (Grant W911NF-08-1-0238

A state action frequency approach to throughput maximization over uncertain wireless channels

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

Large Language Models for Supply Chain Optimization

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 \emph{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 \name{} -- 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

Near-Optimal Power Control in Wireless Networks: A Potential Game Approach

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

ERA: A Framework for Economic Resource Allocation for the Cloud

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations

Brief Announcement: DeadlineAware Scheduling

ABSTRACT This paper presents a novel algorithm for scheduling big data jobs on large compute clusters. In our model, each job is represented by a DAG consisting of several stages linked by precedence constraints. The resource allocation per stage is malleable, in the sense that the processing time of a stage depends on the resources allocated to it (the dependency can be arbitrary in general). The goal of the scheduler is to maximize the total value of completed jobs, where the value for each job depends on its completion time. We design an algorithm for the problem which guarantees an expected constant approximation factor when the cluster capacity is sufficiently high. To the best of our knowledge, this is the first constant-factor approximation algorithm for the problem. The algorithm is based on formulating the problem as a linear program and then rounding an optimal (fractional) solution into a feasible (integral) schedule using randomized rounding

Sharing Buffer Pool Memory in Multi-Tenant Relational Database-as-a-Service

ABSTRACT Relational database-as-a-service (DaaS) providers need to rely on multi-tenancy and resource sharing among tenants, since statically reserving resources for a tenant is not cost effective. A major consequence of resource sharing is that the performance of one tenant can be adversely affected by resource demands of other colocated tenants. One such resource that is essential for good performance of a tenant's workload is buffer pool memory. In this paper, we study the problem of how to effectively share buffer pool memory in multi-tenant relational DaaS. We first develop an SLA framework that defines and enforces accountability of the service provider to the tenant even when buffer pool memory is not statically reserved on behalf of the tenant. Next, we present a novel buffer pool page replacement algorithm (MT-LRU) that builds upon theoretical concepts from weighted online caching, and is designed for multi-tenant scenarios involving SLAs and overbooking. MT-LRU generalizes the LRU-K algorithm which is commonly used in relational database systems. We have prototyped our techniques inside a commercial DaaS engine and extensive experiments demonstrate the effectiveness of our solution