Advance reservation games

Advance reservation (AR) services form a pillar of several branches of the economy, including transportation, lodging, dining, and, more recently, cloud computing. In this work, we use game theory to analyze a slotted AR system in which customers differ in their lead times. For each given time slot, the number of customers requesting service is a random variable following a general probability distribution. Based on statistical information, the customers decide whether or not to make an advance reservation of server resources in future slots for a fee. We prove that only two types of equilibria are possible: either none of the customers makes AR or only customers with lead time greater than some threshold make AR. Our analysis further shows that the fee that maximizes the provider’s profit may lead to other equilibria, one of which yields zero profit. In order to prevent ending up with no profit, the provider can elect to advertise a lower fee yielding a guaranteed but smaller profit. We refer to the ratio of the maximum possible profit to the maximum guaranteed profit as the price of conservatism. When the number of customers is a Poisson random variable, we prove that the price of conservatism is one in the single-server case, but can be arbitrarily high in a many-server system.CNS-1117160 - National Science Foundationhttp://people.bu.edu/staro/ACM_ToMPECS_AR.pdfAccepted manuscrip

Throughput Optimal On-Line Algorithms for Advanced Resource Reservation in Ultra High-Speed Networks

Advanced channel reservation is emerging as an important feature of ultra high-speed networks requiring the transfer of large files. Applications include scientific data transfers and database backup. In this paper, we present two new, on-line algorithms for advanced reservation, called BatchAll and BatchLim, that are guaranteed to achieve optimal throughput performance, based on multi-commodity flow arguments. Both algorithms are shown to have polynomial-time complexity and provable bounds on the maximum delay for 1+epsilon bandwidth augmented networks. The BatchLim algorithm returns the completion time of a connection immediately as a request is placed, but at the expense of a slightly looser competitive ratio than that of BatchAll. We also present a simple approach that limits the number of parallel paths used by the algorithms while provably bounding the maximum reduction factor in the transmission throughput. We show that, although the number of different paths can be exponentially large, the actual number of paths needed to approximate the flow is quite small and proportional to the number of edges in the network. Simulations for a number of topologies show that, in practice, 3 to 5 parallel paths are sufficient to achieve close to optimal performance. The performance of the competitive algorithms are also compared to a greedy benchmark, both through analysis and simulation.Comment: 9 pages, 8 figure

Priority-Based Synchronization of Distributed Data

We consider the general problem of synchronizing the data on two devices using a minimum amount of communication, a core infrastructural requirement for a large variety of distributed systems. Our approach considers the interactive synchronization of prioritized data, where, for example, certain information is more time-sensitive than other information. We propose and analyze a new scheme for efficient priority-based synchronization, which promises benefits over conventional synchronization

Brief announcement: passive and active attacks on audience response systems using software defined radios

Audience response systems, also known as clickers, are used at many academic institutions to offer active learning environments. Since these systems are used to administer graded assignments, and sometimes even exams, it is crucial to assess their security. Our work seeks to exploit and document potential vulnerabilities of clickers. For this purpose, we use software defined radios to perform jamming, sniffing and spoofing attacks on an audience response system in production, which provide different possible methods of cheating. The results of our study demonstrate that clickers are easily exploitable. We build a prototype and show that it is practically possible to covertly steal or forge answers of a peer or even an entire classroom, with high levels of confidence. Additionally, we find that the receivers software of the system lacks protection against unexpected answers, which allows our spoofer to submit any ASCII character and opens the receiver up to possible fuzzing attacks. As a result of this study, we discourage using clickers for high-stake assessments, unless they provide proper security protection..http://people.bu.edu/staro/SSS2017_Brief_v0.pdfhttp://people.bu.edu/staro/SSS2017_Brief_v0.pdfhttp://people.bu.edu/staro/SSS2017_Brief_v0.pdfAccepted manuscrip

Equilibrium and Learning in Queues with Advance Reservations

Consider a multi-class preemptive-resume $M/D/1$ queueing system that supports advance reservations (AR). In this system, strategic customers must decide whether to reserve a server in advance (thereby gaining higher priority) or avoid AR. Reserving a server in advance bears a cost. In this paper, we conduct a game-theoretic analysis of this system, characterizing the equilibrium strategies. Specifically, we show that the game has two types of equilibria. In one type, none of the customers makes reservation. In the other type, only customers that realize early enough that they will need service make reservations. We show that the types and number of equilibria depend on the parameters of the queue and on the reservation cost. Specifically, we prove that the equilibrium is unique if the server utilization is below 1/2. Otherwise, there may be multiple equilibria depending on the reservation cost. Next, we assume that the reservation cost is a fee set by the provider. In that case, we show that the revenue maximizing fee leads to a unique equilibrium if the utilization is below 2/3, but multiple equilibria if the utilization exceeds 2/3. Finally, we study a dynamic version of the game, where users learn and adapt their strategies based on observations of past actions or strategies of other users. Depending on the type of learning (i.e., action learning vs.\ strategy learning), we show that the game converges to an equilibrium in some cases, while it cycles in other cases

Characterizing Orphan Transactions in the Bitcoin Network

Orphan transactions are those whose parental income-sources are missing at the time that they are processed. These transactions are not propagated to other nodes until all of their missing parents are received, and they thus end up languishing in a local buffer until evicted or their parents are found. Although there has been little work in the literature on characterizing the nature and impact of such orphans, it is intuitive that they may affect throughput on the Bitcoin network. This work thus seeks to methodically research such effects through a measurement campaign of orphan transactions on live Bitcoin nodes. Our data show that, surprisingly, orphan transactions tend to have fewer parents on average than non-orphan transactions. Moreover, the salient features of their missing parents are a lower fee and larger size than their non-orphan counterparts, resulting in a lower transaction fee per byte. Finally, we note that the network overhead incurred by these orphan transactions can be significant, exceeding 17% when using the default orphan memory pool size (100 transactions). However, this overhead can be made negligible, without significant computational or memory demands, if the pool size is merely increased to 1000 transactions