FlexAuc: Serving Dynamic Demands in a Spectrum Trading Market with Flexible Auction

In secondary spectrum trading markets, auctions are widely used by spectrum holders (SHs) to redistribute their unused channels to secondary wireless service providers (WSPs). As sellers, the SHs design proper auction schemes to stimulate more participants and maximize the revenue from the auction. As buyers, the WSPs determine the bidding strategies in the auction to better serve their end users. In this paper, we consider a three-layered spectrum trading market consisting of the SH, the WSPs and the end users. We jointly study the strategies of the three parties. The SH determines the auction scheme and spectrum supplies to optimize its revenue. The WSPs have flexible bidding strategies in terms of both demands and valuations considering the strategies of the end users. We design FlexAuc, a novel auction mechanism for this market to enable dynamic supplies and demands in the auction. We prove theoretically that FlexAuc not only maximizes the social welfare but also preserves other nice properties such as truthfulness and computational tractability.Comment: 11 pages, 7 figures, Preliminary version accepted in INFOCOM 201

An Efficient Cloud Market Mechanism for Computing Jobs with Soft Deadlines

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POEM: Pricing Longer for Edge Computing in the Device Cloud

Multiple access mobile edge computing has been proposed as a promising technology to bring computation services close to end users, by making good use of edge cloud servers. In mobile device clouds (MDC), idle end devices may act as edge servers to offer computation services for busy end devices. Most existing auction based incentive mechanisms in MDC focus on only one round auction without considering the time correlation. Moreover, although existing single round auctions can also be used for multiple times, users should trade with higher bids to get more resources in the cascading rounds of auctions, then their budgets will run out too early to participate in the next auction, leading to auction failures and the whole benefit may suffer. In this paper, we formulate the computation offloading problem as a social welfare optimization problem with given budgets of mobile devices, and consider pricing longer of mobile devices. This problem is a multiple-choice multi-dimensional 0-1 knapsack problem, which is a NP-hard problem. We propose an auction framework named MAFL for long-term benefits that runs a single round resource auction in each round. Extensive simulation results show that the proposed auction mechanism outperforms the single round by about 55.6% on the revenue on average and MAFL outperforms existing double auction by about 68.6% in terms of the revenue.Comment: 8 pages, 1 figure, Accepted by the 18th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP

Online Auctions in IaaS Clouds: Welfare and Profit Maximization with Server Costs

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Core-Selecting Auctions for Dynamically Allocating Heterogeneous VMs in Cloud Computing

In a cloud market, the cloud provider provisions heterogeneous virtual machine (VM) instances from its resource pool, for allocation to cloud users. Auction-based allocations are efficient in assigning VMs to users who value them the most. Existing auction design often overlooks the heterogeneity of VMs, and does not consider dynamic, demand-driven VM provisioning. Moreover, the classic VCG auction leads to unsatisfactory seller revenues and vulnerability to a strategic bidding behavior known as shill bidding. This work presents a new type of core-selecting VM auctions, which are combinatorial auctions that always select bidder charges from the core of the price vector space, with guaranteed economic efficiency under truthful bidding. These auctions represent a comprehensive three-phase mechanism that instructs the cloud provider to judiciously assemble, allocate, and price VM bundles. They are proof against shills, can improve seller revenue over existing auction mechanisms, and can be tailored to maximize truthfulness.published_or_final_versio

RSMOA: a revenue and social welfare maximizing online auction for dynamic cloud resource provisioning

We study online cloud resource auctions where users can arrive anytime and bid for heterogeneous types of virtual machines (VMs) assembled and provisioned on the fly. The proposed auction mechanism RSMOA, to the authors’ knowledge, represents the first truthful online mechanism that timely responds to incoming users’ demands and makes dynamic resource provisioning and allocation decisions, while guaranteeing efficiency in both the provider’s revenue and system social welfare. RSMOA consists of two components: (1) an online mechanism that computes resource allocation and users’ payments based on a global, non-decreasing pricing curve, and guarantees truthfulness; (2) a judiciously designed pricing curve, which is derived from a threat-based strategy and guarantees a competitive ratio O(ln(p)) in both system social welfare and the provider’s revenue, as compared to the celebrated offline Vickrey-Clarke-Groves (VCG) auction. Here p is the ratio between the upper and lower bounds of users’ marginal valuation of a type of resource. The efficacy of RSMOA is validated through extensive theoretical analysis and trace-driven simulation studies.published_or_final_versio