9,856 research outputs found
Auction-based Bandwidth Allocation Mechanisms for Wireless Future Internet
An important aspect of the Future Internet is the efficient utilization of
(wireless) network resources. In order for the - demanding in terms of QoS -
Future Internet services to be provided, the current trend is evolving towards
an "integrated" wireless network access model that enables users to enjoy
mobility, seamless access and high quality of service in an all-IP network on
an "Anytime, Anywhere" basis. The term "integrated" is used to denote that the
Future Internet wireless "last mile" is expected to comprise multiple
heterogeneous geographically coexisting wireless networks, each having
different capacity and coverage radius. The efficient management of the
wireless access network resources is crucial due to their scarcity that renders
wireless access a potential bottleneck for the provision of high quality
services. In this paper we propose an auction mechanism for allocating the
bandwidth of such a network so that efficiency is attained, i.e. social welfare
is maximized. In particular, we propose an incentive-compatible, efficient
auction-based mechanism of low computational complexity. We define a repeated
game to address user utilities and incentives issues. Subsequently, we extend
this mechanism so that it can also accommodate multicast sessions. We also
analyze the computational complexity and message overhead of the proposed
mechanism. We then show how user bids can be replaced from weights generated by
the network and transform the auction to a cooperative mechanism capable of
prioritizing certain classes of services and emulating DiffServ and time-of-day
pricing schemes. The theoretical analysis is complemented by simulations that
assess the proposed mechanisms properties and performance. We finally provide
some concluding remarks and directions for future research
Quantized VCG Mechanisms for Polymatroid Environments
Many network resource allocation problems can be viewed as allocating a
divisible resource, where the allocations are constrained to lie in a
polymatroid. We consider market-based mechanisms for such problems. Though the
Vickrey-Clarke-Groves (VCG) mechanism can provide the efficient allocation with
strong incentive properties (namely dominant strategy incentive compatibility),
its well-known high communication requirements can prevent it from being used.
There have been a number of approaches for reducing the communication costs of
VCG by weakening its incentive properties. Here, instead we take a different
approach of reducing communication costs via quantization while maintaining
VCG's dominant strategy incentive properties. The cost for this approach is a
loss in efficiency which we characterize. We first consider quantizing the
resource allocations so that agents need only submit a finite number of bids
instead of full utility function. We subsequently consider quantizing the
agent's bids
Scheduling of data-intensive workloads in a brokered virtualized environment
Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments
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