Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing

Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate users’ preferences as well as service providers’ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the users’ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operators’ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the users’ and operators’ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from users’ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operators’ utility, as total utility reward obtained increases towards a defined ‘goal state’

A Reinforcement Learning Based Model for Adaptive ServiceQuality Management in E-Commerce Websites

Providing high-quality service to all users is adifficult and inefficient strategy for e-commerce providers,especially when Web servers experience overload condi-tions that cause increased response time and requestrejections, leading to user frustration and reduced revenue.In an e-commerce system, customer Web sessions havediffering values for service providers. These tend to: givepreference to customer Web sessions that are likely tobring more profit by providing better service quality. Thispaper proposes a reinforcement-learning based adaptivee-commerce system model that adapts the service qualitylevel for different Web sessions within the customer’snavigation in order to maximize total profit. The e-com-merce system is considered as an electronic supply chainwhich includes a network of basic e- providers used tosupply e-commerce services for end customers. The learneragent noted as e-commerce supply chain manager(ECSCM) agent allocates a service quality level to thecustomer’s request based on his/her navigation pattern inthe e-commerce Website and selects an optimized combi-nation of service providers to respond to the customer’srequest. To evaluate the proposed model, a multi agentframework composed of three agent types, the ECSCMagent, customer agent (buyer/browser) and service provideragent, is employed. Experimental results show that theproposed model improves total profits through costreduction and revenue enhancement simultaneously andencourages customers to purchase from the Websitethrough service quality adaptation

Multiobjective auction-based switching-off scheme in heterogeneous networks: to bid or not to bid?

©2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The emerging data traffic demand has caused a massive deployment of network infrastructure, including Base Stations (BSs) and Small Cells (SCs), leading to increased energy consumption and expenditures. However, the network underutilization during low traffic periods enables the Mobile Network Operators (MNOs) to save energy by having their traffic served by third party SCs, thus being able to switch off their BSs. In this paper, we propose a novel market approach to foster the opportunistic utilization of the unexploited SCs capacity, where the MNOs, instead of requesting the maximum capacity to meet their highest traffic expectations, offer a set of bids requesting different resources from the third party SCs at lower costs. Motivated by the conflicting financial interests of the MNOs and the third party, the restricted capacity of the SCs that is not adequate to carry the whole traffic in multi-operator scenarios, and the necessity for energy efficient solutions, we introduce a combinatorial auction framework, which includes i) a bidding strategy, ii) a resource allocation scheme, and iii) a pricing rule. We propose a multiobjective framework as an energy and cost efficient solution for the resource allocation problem, and we provide extensive analytical and experimental results to estimate the potential energy and cost savings that can be achieved. In addition, we investigate the conditions under which the MNOs and the third party companies should take part in the proposed auction.Peer ReviewedPostprint (author's final draft

From geographically dispersed data centers towards hierarchical edge computing

Internet scale data centers are generally dispersed in different geographical regions. While the main goal of deploying the geographically dispersed data centers is to provide redundancy, scalability and high availability, the geographic dispersity provides another opportunity for efficient employment of global resources, e.g., utilizing price-diversity in electricity markets or utilizing locational diversity in renewable power generation. In other words, an efficient approach for geographical load balancing (GLB) across geo-dispersed data centers not only can maximize the utilization of green energy but also can minimize the cost of electricity. However, due to the different costs and disparate environmental impacts of the renewable energy and brown energy, such a GLB approach should tap on the merits of the separation of green energy utilization maximization and brown energy cost minimization problems. To this end, the notion of green workload and green service rate, versus brown workload and brown service rate, respectively, to facilitate the separation of green energy utilization maximization and brown energy cost minimization problems is proposed. In particular, a new optimization framework to maximize the profit of running geographically dispersed data centers based on the accuracy of the G/D/1 queueing model, and taking into consideration of multiple classes of service with individual service level agreement deadline for each type of service is developed. A new information flow graph based model for geo-dispersed data centers is also developed, and based on the developed model, the achievable tradeoff between total and brown power consumption is characterized. Recently, the paradigm of edge computing has been introduced to push the computing resources away from the data centers to the edge of the network, thereby reducing the communication bandwidth requirement between the sources of data and the data centers. However, it is still desirable to investigate how and where at the edge of the network the computation resources should be provisioned. To this end, a hierarchical Mobile Edge Computing (MEC) architecture in accordance with the principles of LTE Advanced backhaul network is proposed and an auction-based profit maximization approach which effectively facilitates the resource allocation to the subscribers of the MEC network is designed. A hierarchical capacity provisioning framework for MEC that optimally budgets computing capacities at different hierarchical edge computing levels is also designed. The proposed scheme can efficiently handle the peak loads at the access point locations while coping with the resource poverty at the edge. Moreover, the code partitioning problem is extended to a scheduling problem over time and the hierarchical mobile edge network, and accordingly, a new technique that leads to the optimal code partitioning in a reasonable time even for large-sized call trees is proposed. Finally, a novel NOMA augmented edge computing model that captures the gains of uplink NOMA in MEC users\u27 energy consumption is proposed

Generating demand functions for data plans from mobile network operators based on users’ profiles

The final publication is available at Springer via http://dx.doi.org/10.1007/s10922-018-9448-1The evaluation of pricing approaches for mobile data services proposed in the literature can rarely be done in practice. Evaluation by simulation is the most common practice. In these proposals demand and utility functions that describe the reaction of users to offered service prices, use traditional and arbitrary functions (linear, exponential, logit, etc.). In this paper, we present a new approach to construct a simulation model whose output can be used as an alternative method to create demand functions avoiding to use arbitrary and predefined demand functions. However, it is out of the scope of this paper to utilize them to propose pricing approaches, since the main objective of this article is to show the difference between the arbitrary demand functions used and our approach that come from users’ data. The starting point in this paper is to consider data offered from Eurostat, although other data sources could be used for the same purposes with the aim to offer more realistic values that could characterize more appropriately, what users are demanding. In this sense, some demographic and psychographic characteristics of the users are included and others such as the utilization of application usage profiles, as parameters that are included in the user`s profiles. These characteristics and usage profiles make up the user profile that will influence users’ behavior in the model. Using the same procedure, Mobile Network Operators could feed their customers’ data into the model and use it to validate their pricing approaches more accurately before their real implementation or simulate future or hypothetical scenarios. It also makes possible to segment users and make insights for decision-making. Results presented in this paper refer to a simple study case, since the purpose of the paper is to show how the proposal model works and to reveal its differences with arbitrary demand functions used. Of course, results depend on the set of parameters assigned to characterize each user’s profile.Peer ReviewedPostprint (published version

A source-destination based dynamic pricing scheme to optimize resource utilization in heterogeneous wireless networks

Mobile wireless resources demand is rapidly growing due to the proliferation of bandwidth-hungry mobile devices and applications. This has resulted in congestion in mobile wireless networks (MWN) especially during the peak hours when user traffic can be as high as tenfold the average traffic. Mobile network operators (MNOs) have been trying to solve this problem in various ways. First, MNOs have tried to expand the network capacity but have still been unable to meet the peak hour demand. Focus has then shifted to economic and behavioral mechanisms. The widely used of these economic mechanisms is dynamic pricing which varies the MWN resources' price according to the congestion level in the MWN. This encourages users to shift their non-critical traffic from the busy hour, when the MWN is congested, to off-peak hours when the network is under-utilized. As a result, congestion of the MWN during the peak hours is reduced. At the same time, the MWN utilization during the off-peak hours is also increased. The current dynamic pricing schemes, however, only consider the congestion level in the call-originating cell and neglect the call-destination cell when computing the dynamic price. Due to this feature, we refer the current dynamic pricing schemes as source–based dynamic pricing (SDP) schemes in this work. The main problem with these schemes is that, when the majority of the users in a congested cell are callees, dynamic pricing is ineffective because callers and not callees pay for network services, and resources used by callers and callees are the same for symmetric services. For example, application of dynamic pricing does not deter a callee located in a congested cell from receiving a call, which originates from a caller located in an uncongested cell. Also, when the distribution of prospective callees is higher than that of callers in an underutilized cell, SDP schemes are ineffective as callees do not pay for a call and therefore low discounts do not entice them to increase utilization. In this distribution, dynamic pricing entices prospective callers to make calls but since their distribution is low, the MWN resource utilization does not increase by any significant margin. To address these problems, we have developed a source-destination based dynamic pricing (SDBDP) scheme, which considers congestion levels in both the call-originating and calldestination cells to compute the dynamic price to be paid by a caller. This SDBDP scheme is integrated with a load-based joint call admission control (JCAC) algorithm for admitting incoming service requests in to the least utilized radio access technology (RAT). The load-based JCAC algorithm achieves uniform traffic distribution in the heterogeneous wireless network (HWN). To test the SDBDP scheme, we have developed an analytical model based on M/M/m/m queuing model. New or handoff service requests, arriving when all the RATs in the HWN are fully utilized, lead to call blocking for new calls and call dropping for handoff calls. The call blocking probability, call dropping probability and percentage MWN utilization are used as the performance metrics in evaluating the SDBDP scheme. An exponential demand model is used to approximate the users' response to the presented dynamic price. The exponential demand model captures both the price elasticity of demand and the demand shift constant for different users. The matrix laboratory (MATLAB) tool has been used to carry out the numerical simulations. An evaluation scenario consisting of four groups of co-located cells each with three RATs is used. Both SDP and the developed SDBDP schemes have been subjected under the evaluation scenario. Simulation results show that the developed SDBDP scheme reduces both the new call blocking and handoff call dropping probabilities during the peak hours, for all callercallee distributions. On the other hand, the current SDP scheme only reduces new call blocking and handoff call dropping probabilities only under some caller –callee distributions (When the callers were the majority in the HWN). Also, the SDBDP scheme increases the percentage MWN utilization during the off-peak for all the caller-callee distributions in the HWN. On the other hand, the SDP scheme is found to increase the percentage MWN utilization only when the distribution of callers is higher than that of callees in the HWN. From analyzing the simulations results, we conclude that the SDBDP scheme achieves better congestion control and MWN resource utilization than the existing SDP schemes, under arbitrary caller-callee distribution

Multi-attribute demand characterization and layered service pricing

As cloud computing gains popularity, understanding the pattern and structure of its workload is increasingly important in order to drive effective resource allocation and pricing decisions. In the cloud model, virtual machines (VMs), each consisting of a bundle of computing resources, are presented to users for purchase. Thus, the cloud context requires multi-attribute models of demand. While most of the available studies have focused on one specific attribute of a virtual request such as CPU or memory, to the best of our knowledge there is no work on the joint distribution of resource usage. In the first part of this dissertation, we develop a joint distribution model that captures the relationship among multiple resources by fitting the marginal distribution of each resource type as well as the non-linear structure of their correlation via a copula distribution. We validate our models using a public data set of Google data center usage. Constructing the demand model is essential for provisioning revenue-optimal configuration for VMs or quality of service (QoS) offered by a provider. In the second part of the dissertation, we turn to the service pricing problem in a multi-provider setting: given service configurations (qualities) offered by different providers, choose a proper price for each offered service to undercut competitors and attract customers. With the rise of layered service-oriented architectures there is a need for more advanced solutions that manage the interactions among service providers at multiple levels. Brokers, as the intermediaries between customers and lower-level providers, play a key role in improving the efficiency of service-oriented structures by matching the demands of customers to the services of providers. We analyze a layered market in which service brokers and service providers compete in a Bertrand game at different levels in an oligopoly market while they offer different QoS. We examine the interaction among players and the effect of price competition on their market shares. We also study the market with partial cooperation, where a subset of players optimizes their total revenue instead of maximizing their own profit independently. We analyze the impact of this cooperation on the market and customers' social welfare