Fair Resource Allocation Strategies Research Articles

Optimal QoE-Fair Resource Allocation in Multipath Video Delivery Network

A steadily increasing number of users consume videos over the Internet. In current video platforms, players run a control algorithm that dynamically chooses the video bitrate to match the time-varying network bandwidth. Such an algorithm strives to improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users rather than to a quality-fair distribution of resources, possibly leading to user abandonment for those users receiving a lower quality. Therefore, we argue that well-designed video delivery networks should gracefully degrade the perceived quality equally for all users when resources become scarce. In this paper, we propose the Multi-Commodity Flow Problem (MCFP) optimization framework to address the issue of designing a QoE-fair optimal allocation strategy. We show how to make the resulting problem tractable for video platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is tested through realistic simulations involving thousands of concurrent users on two real networks by varying both the total load on the network and the system parameters.

Heterogeneous RAN slicing resource allocation using mathematical program with equilibrium constraints

Network slicing is considered to be a key feature of the 5th generation mobile networks. It permits multiple tenants, i.e. mobile virtual network operators, to share virtual resources. However, each tenant only considers the individual slice utility, which leads to unfair resource allocation among tenants. To achieve the aim that the infrastructure provider can fairly allocate virtual resources to tenants, a two-layer resource allocation architecture in a heterogeneous radio access network (RAN) is proposed and it is formulated as a mathematical program with equilibrium constraints (MPEC). The existence of the solution in the lower layer is proved via the properties of the quasi-variational problem, indicating that the MPEC is solvable. Combining the two-layer architecture and successive convex approximation method, a fair algorithm is proposed, which provides fair resource allocation strategies for the infrastructure provider. Compared with the existing static slicing and social optimal methods, the analysis and simulation results confirm that the proposed algorithm weighs the utilities of the total network system and each tenant. In addition, regarding their utilities, the gap between the proposed method and the social optimal is within 5%, which outperforms static slicing.

A multi-stage stochastic programming approach to epidemic resource allocation with equity considerations.

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

TARTS: A Temperature-Aware Real-Time Deadline-Partitioned Fair Scheduler

Modern multicore systems, which perform complex functionalities on densely packed multi-million gate platforms at high frequencies, are often susceptible to inappropriate surges in core temperatures, if not effectively controlled. Increasing temperature above specified limits not only leads to high cost of cooling but also results in higher dissipation of leakage power together with a reduction in performance and lower system life expectancy. In this work, we propose a two-level low-overhead proportional fair resource allocation strategy called Temperature-Aware Real-Time Deadline-Partitioned Fair Scheduler (TARTS), for periodic real-time tasks to be executed on a thermally bounded multicore device. The proposed algorithm’s first level divides time into distinct slices based on deadlines of tasks, so that exact proportional fairness is maintained at all slice boundaries. The second level accomplishes intra-slice scheduling with the aim of maximising the use of resources while not breaching a specified thermal threshold. Experimental results show that our scheme can deliver a significantly higher resource usage efficiency compared to a baseline greedy approach, TA-MTS. For example, TARTS is able to achieve up to 42% higher task acceptance ratios compared to TA-MTS on a fully loaded quad-core system with 80 tasks and temperature threshold of 90 °C.

A Multi-Commodity Flow Problem for Fair Resource Allocation in Multi-Path Video Delivery Networks

Video streaming services employ the Internet to distribute content to an ever-increasing number of concurrent viewers. The delivery architecture employed by leading video platforms requires players to run a control algorithm dynamically choosing the video bitrate to match the time-varying network bandwidth and avoid playback interruptions due to buffer underruns. Such an algorithm is generally designed to (selfishly) improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users and not to a distribution of resources that is fair in terms of user perceived quality. We argue that video service providers should manage their delivery network to address fairness issues to gracefully degrade the perceived quality equally for all users when resources become scarce. Even though the general problem of providing a fair level of perceived quality does not scale with the cumbersome number of concurrent users, this paper shows that the Multi-Commodity Flow Problem (MCFP) optimization framework is a proper and efficient tool to address this open issue. First, we show how to cast the resource allocation problem to an MCFP and then we propose a strategy to make the resulting problem tractable for video distribution platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is assessed using realistic simulations involving thousands of concurrent video sessions on a real network topology by varying both the total load on the network and key system parameters.

An evolutionary-based fuzzy resource assignment strategy for elastic traffic

Many difficult engineering problems have found natural solutions which have been inspired from biological behaviors of the living kinds. Some important examples include neural networks, Genetic Algorithm GA, DNA computing, artificial immune systems etc. Fair resource allocation strategies which are developed by many researchers are based on solving a form of constrained optimization problem. However, they are not necessarily lead to high-speed and stable solutions. There are plenty of high-speed fair rate allocation methods in the literature, some of them are based on fuzzy controllers for improving the convergence speed and are not necessarily optimal. Hence, in the current research, the GA has been adopted for finding the optimum membership functions which must be used in the fuzzy controller. Stability analysis is presented to guarantee the convergence property of the algorithm. After simulating, the results show that using the hybrid fuzzy-genetic approach improves the conventional methods in convergence speed and results in fewer oscillations in allocated rates.

A Cross-Layer Approach to Fair Resource Allocation for Multimedia Service in WiMAX

In this paper, a cross-layer resource allocation mechanism is proposed for wireless multimedia service. In particular, a game theory based on quality of service (QoS) for multimedia users is introduced to deal with the fairness of network resource allocation in wireless networks. Moreover, the channel states of wireless users are additionally regarded under the cross-layer design in WiMAX environment. In details, the bargaining solution is adopted to discover the efficient and fair resource allocation strategy for multimedia service in considering QoS in the peak signal-to-noise ratio (PSNR) and the channel states in the carrier-to-interface ratio (CINR). The proposed mechanism is illustrated and evaluated by simulation results of transmitting video sequences in WiMAX environment.

Revisiting Proportional Fairness: Anonymity Among Users in Interference Coupled Wireless Systems

The paper revisits the problem of proportional fairness in interference coupled wireless systems. It models interference coupling in wireless systems based on an interference function framework through a set of axioms (introduced by Yates in 1995). It utilizes the collective choice function to represent resource allocation strategies and an axiomatic framework to emulate certain desirable properties of resource allocation strategies. We introduce the axiom of equal priority in the power domain (and in the interference domain) and motivate it as interference coordination fairness. We consider this as an anonymity among the users, from the perspective of a central controller, e.g. a base station or an operator. We show that the proportional fair resource allocation strategy is anonymous to the identity of the users at the signal processing layer. Such an anonymity is relevant to obtain interference coordination fairness in iterative resource allocation strategies frequently encountered in wireless systems.

Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks

Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed for orthogonal frequency-division multiplexing (OFDM) networks designed to maximise performance while limiting the received interference at each user. This received interference is in essence used as a fairness metric; moreover, by defining different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in various cognitive radio scenarios. As applied to the scheme, the authors investigate a scenario where two cellular OFDM-based networks operate as primary and secondary systems in the same band, and the secondary system benefits by accessing the unused resources of the primary system if additional capacity is required. The primary system benefits either by charging the secondary system for the use of its resources or by some form of reciprocal arrangement allowing it to use the secondary system's licenced bands in a similar manner, when needed. Numerical results show our interference-limited scheduling approach to achieve excellent levels of efficiency and fairness by allocating resources more intelligently than proportional fair scheduling. A further important contribution is the application of sequential quadratic programming to solve the non-convex optimisation problems which arise in such scenarios.