Fair Resource Allocation Research Articles

An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm

Cloud computing provides services and resources in the Internet, and many applications are self-service-supported, on-demand resource allocation-adapted. These dynamic networks allocated necessary resource to the users’ need and they require proper resource allocation scheme. Since various resources are consumed by users if resource allocation is not proper, this leads the system to load imbalance nature. Using Internet-connected devices for storage and computation not only communicates the cloud resources but also connects the devices to network through various protocols. These changes make the network into a complex, dense, heterogeneous system. In this paper, a green computing fair resource allocation through deep reinforcement learning model is proposed to provide efficient resource allocation scheme to the users in the network. Conventional Q-learning model fails in dimensionality problem when the state space increases exponentially. The proposed model is combined with fair resource allocation with deep reinforcement learning to provide better allocation schemes compared to the conventional model.

Fairness criteria for allocating scarce resources

We develop an optimization model to provide a fair allocation of multiple resources to multiple users. All resources might not be suitable to all users. We develop a notion of fairness, and then provide a general class of functions achieving it. Next, we develop more restricted notions of fairness—special cases of which exist in literature. Finally, we distinguish between scarce and abundant resources, and show that if a resource is abundant, all users seeking it achieve the maximum possible coverage.

Competitive equilibrium for almost all incomes: existence and fairness

Competitive equilibrium (CE) is a fundamental concept in market economics. Its efficiency and fairness properties make it particularly appealing as a rule for fair allocation of resources among agents with possibly different entitlements. However, when the resources are indivisible, a CE might not exist even when there is one resource and two agents with equal incomes. Recently, Babaioff and Nisan and Talgam-Cohen (2017) have suggested to consider the entire space of possible incomes, and check whether there exists a competitive equilibrium for almost all income-vectors --- all income-space except a subset of measure zero. They proved various existence and non-existence results, but left open the cases of four goods and three or four agents with monotonically-increasing preferences. This paper proves non-existence in both these cases, thus completing the characterization of CE existence for almost all incomes in the domain of monotonically increasing preferences. Additionally, the paper provides a complete characterization of CE existence in the domain of monotonically decreasing preferences, corresponding to allocation of chores. On the positive side, the paper proves that CE exists for almost all incomes when there are four goods and three agents with additive preferences. The proof uses a new tool for describing a CE, as a subgame-perfect equilibrium of a specific sequential game. The same tool also enables substantially simpler proofs to the cases already proved by Babaioff et al. Additionally, this paper proves several strong fairness properties that are satisfied by any CE allocation, illustrating its usefulness for fair allocation among agents with different entitlements.

Fair and efficient resource allocation in IEEE 802.11ah WLAN with heterogeneous data rates

For dense wireless LANs (WLANs), IEEE 802.11ah has specified restricted access window (RAW) as the channel access scheme. Here the competing stations (STAs) are divided into groups and STAs belonging to each group attempt to access the channel during their designated RAW slot by invoking the distributed coordination function (DCF) protocol. This paper describes an analytical model to evaluate the throughput performance of IEEE 802.11ah WLAN, when STAs use distinct data rates and the RAW mechanism is implemented for medium sharing. When STAs are grouped randomly (i.e., without considering their data rates), a group will contain STAs operating at distinct data rates. Since all these STAs simultaneously contend for channel access based on DCF protocol, the throughput of high data rate STAs are down-equalized to that of lower data rate STAs; thus the aggregate network throughput is degraded significantly. To resolve the resulting performance anomaly problem, we consider data rate based grouping where STAs operating at the same data rate are grouped together. We describe an algorithm for implementing data rate based grouping at the access point (AP). Further, we describe an analytical procedure to find the network throughput under data rate based grouping. Through numerical and experimental investigations, we establish that data rate based grouping can significantly improve the aggregate network throughput performance, as compared to the conventional random grouping strategy. Further, we use Jain’s fairness index (JFI) to establish that data rate based grouping can also provide fair resource allocation among the STAs operating at distinct data rates, by ensuring that all the competing STAs in the network achieve throughput proportional to their data rates.

Gynecologic oncology at the time of COVID-19 outbreak

The World Health Organization (WHO) classified the novel coronavirus (i.e., coronavirus disease 2019 [COVID-19]) as a global public health emergency. COVID-19 threatens to curtail patient access to evidence-based treatment. Medicine is changing, basically due to the limited available resources. In the field of gynecologic oncology, we have to re-design our treatments' paradigm. During COVID-19 pandemic outbreak, the highest priority is to achieve the maximum benefit from less demanding procedures. Extensive procedures should be avoided, in order to reduce hospitalization and postoperative events that might increase the in-hospital spread of the virus. There are ongoing concerns on the use of laparoscopic procedures, related to the possible contamination of the staff working in the operation room. Other minimally invasive techniques, including, vaginal surgery as well as robotic-assisted and isobaric procedures would be preferred over laparoscopy. A fair allocation of resources is paramount adequate treatments.

Malignant melanoma treatment and follow-up management during COVID-19 pandemic

Melanoma is a public health issue due to its increasing in­ci­dence and young age of diagnosis. There are three types of malignant melanoma: cutaneous, uveal and mucosal, all with different incidence, prognosis and management. Some malignant melanoma treatments – including che­mo­the­ra­py, targeted therapies, immunotherapy and radiation – can change the immune response. People with weakened im­mune systems or with lung problems due to radiotherapy pneu­mo­ni­tis or immune-mediated pneumonitis or with lung metastases have a much higher risk of complications if they are infected with SARS-CoV-2. On the 20th of April 2020, there were reported 2,444,209 coronavirus cases and 167,986 deaths. There are various recommendations amidst COVID-19 pandemic on diagnosis and treatment of patients with different kinds of solid tumors. The ethical prin­ci­ple that should guide our medical decisions should be primum non nocere, respecting patient wish and try­­ing to do what is best for him/her, with fair allocation of resources. In this review, we wanted to evaluate and rein­force the recommendations on melanoma patients’ ma­nage­ment by searching for different medical societies’ re­com­men­dation. We found guidelines on how to manage me­­la­­no­­ma patients in different stages of the disease and how to treat best patients, minimizing exposure to infection.

Fair Energy-Efficient Resource Allocation for Downlink NOMA Heterogeneous Networks

The increasing in energy consumptions of the current wireless networks, leads towards designing energy-efficient 5G networks. The application of non-orthogonal multiple access (NOMA) in the heterogeneous networks (HetNets) improves the spectrum utilization with the cost of efficient resource allocation. Hence, this article proposes optimal user-pairing and power allocation solutions towards achieving fair energy-efficient resource allocation in downlink femtocell NOMA-HetNets. In the proposed optimization process, the considered constraints are the user's transmission rate, transmit power budget at the base station (BS), and the interference. The energy consumption of both the transmitter and the receiver are considered to simulate the real system design. The Greedy Algorithm (GA) is used to achieve a low-complex optimal solution during the user-pairing process. Simultaneously, the max-min energy efficiency optimization approach is employed to maximize the minimum energy efficiency of the femtocell users to achieve the optimal power allocation solution. The mathematical formulation of the max-min energy efficiency is a non-convex fractional programming problem and is intractable. Thus, the fractional programming theory is adopted to transform the problem into a sequence of subtractive form, followed by the Sequential Convex Programming (SCP) approach to determine the optimal solution. Simulation results show that the proposed NOMA with optimal power allocation method using SCP and GA (NOMA-SCP-GA) achieves fair energy efficiency performance with lower complexity compared to the benchmark methods. Moreover, the minimum energy efficiency of the femtocell user is 38.22% higher than NOMA with Difference of Convex programming (NOMA-DC). The NOMA-SCP-GA method can assure 5G capability demands.

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.

Fair Packing and Covering on a Relative Scale

Fair resource allocation is a fundamental optimization problem with applications in operations research, networking, and economic and game theory. Research in these areas has led to the general acceptance of a class of $\alpha$-fair utility functions parameterized by $\alpha \in [0, \infty]$. We consider $\alpha$-fair packing---the problem of maximizing $\alpha$-fair utilities under positive linear constraints---and provide a simple first-order method for solving it with relative-error guarantees. The method has a significantly lower convergence time than the state of the art, and to analyze it, we leverage the approximate duality gap technique, which provides an intuitive interpretation of the convergence argument. Finally, we introduce a natural counterpart of $\alpha$-fairness for minimization problems and motivate its usage in the context of fair task allocation. This generalization yields $\alpha$-fair covering problems, for which we provide the first near-linear-time solvers with relative-error guarantees.

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

Due to the growing demand for spectral resources with the emergence of IoT networks and applications, cooperative cognitive radio IoT networks (CCR-IoTN) are expected to play an essential role in the world of smart technology. In this paradigm, the benefits of cooperative communication and cognitive radio networks (CRN) are merged to meet IoT networks' needs. In this work, we employ the hybrid overlay-underlay CRN to guarantee both the secondary user (SU) stability and provide acceptable total throughput. In our CCR-IoTN, SUs may act as a relay to help the primary transmission. If the primary user (PU) is active, the secondary non-relay nodes transmit concurrently with the PU in underlay mode. In return for their cooperation, SUs are allowed to access the spectrum to transmit their data in overlay mode. In this context, we propose several fair resource allocation frameworks and compare well-known problems such as spectrum efficiency, energy efficiency as an objective function for CCR-IoTN. This kind of problem is inherently nonconvex. To solve the problems, therefore, we reformulate them as a convex problem using quadratic transform. Finally, we use simulation to evaluate the performance of the proposed hybrid resource allocation method. The resource allocation framework based on proportional fairness policy is shown to have acceptable performance in terms of throughput, fairness, and energy efficiency.

Optimal Resource Allocation and Interference Management for Multi-User Uplink Light Communication Systems With Angular Diversity Technology

Light communication (LC) technology has been regarded as a promising candidate for future indoor wireless networks by providing safe, power-efficient, and high data rate communications needed for tomorrow's applications. Both visible light (VL) and infrared (IR) wavelengths can be utilized to design LC systems. It is often proposed that VL can be used to offload downlink traffic while near-IR is typically used in the uplink. In this paper, the uplink multi-user LC system is considered where the system performance is degraded by both inter-symbol interference (ISI) resulting from multipath reflections and inter-user interference (IUI) coming from neighboring users. To mitigate these limitations, an optimal fair resource allocation (OFRA) scheme is proposed which aims to improve the fairness among the users in terms of their received signal to interference plus noise ratios (SINRs) by implementing the angle diversity technology. Precisely, by assigning an ON/OFF state for each LED of the angle diversity transmitter (ADT), used by each user, the IUI can be significantly reduced. Also, the angle diversity receiver (ADR) is used to effectively mitigate the effects of ISI. The allocation matrix which achieves the highest fairness between different users is obtained for different scenarios of user distribution. Toward this, the exhaustive search (ES) method is used to obtain the optimal solution for the optimization problem under consideration. However, to reduce the time complexity of ES method, a quasi-optimal solution called sub-optimal fair resource allocation scheme SFRA is proposed. The sub-optimal solution is based on the genetic algorithm (GA) scheme. The simulation results reveal that both the OFRA and SFRA achieve almost the same performance. Moreover, the simulation results indicate the superior performance of the proposed OFRA scheme over the conventional single transmitter (ST) one.

Fair Resource Allocation Based on User Satisfaction in Multi-OLT Virtual Passive Optical Network

In this paper, a fair resource allocation mechanism based on user satisfaction in multiple optical line terminal (multi-OLT) virtual passive optical network (VPON) is proposed. By introducing user satisfaction functions, the satisfaction of users can be quantified. By presenting multi-OLT VPON in metro-access optical network (MAON), different priority services can be effectively handled separately. Based on user satisfaction, a fair resource allocation mechanism called double fair dynamic bandwidth allocation scheme (DFDBAS) is proposed. DFDBAS is a combination of two algorithms: wavelength fair allocation (WFA) algorithm and timeslot fair allocation (TFA) algorithm. In TFA algorithm, max-min fairness (MMF) algorithm is presented to allocate bandwidth for high priority service and Lagrange multiplier method is implemented to allocate bandwidth for low priority service. By implementing DFDBAS, not only user delay satisfaction can be improved, but also user bandwidth satisfaction can increase. By the simulation and analysis, the effectiveness of the proposed algorithms is demonstrated. Compared with modified limited service (LS) algorithm, DFDBAS makes both delay satisfaction and bandwidth satisfaction increase by at least 30% when the load is greater than 1.

CDF-Based Multiuser Scheduling for Downlink Non-Orthogonal Multiple Access (NOMA)

In this paper, we propose cumulative distribution function (CDF)-based multiuser scheduling (CMS) for downlink non-orthogonal multiple access (NOMA) to accommodate a massive number of IoT devices over limited radio resources and statistically analyze the ergodic capacity of devices. With the proposed CMS algorithm, multiple devices having relatively better channel gain to exploit multiuser diversity are selected while meeting QoS requirements by controlling the amount of resources. We first provide the user selection probability in terms of the weight of each device, which determines the amount of time resources. Based on some selected results, it is shown that equal weights ensure fair resource allocation. Further, for fair resource allocation scenario, the distribution of signal-to-interference-plus-noise ratio (SINR) when a specific device is selected is derived. With this derived statistical result of SINR, we analyze the ergodic capacity of the specific device. Some selected results show that the proposed CMS algorithm allocates the time resource more fairly than proportional fair scheduling. Further, the average throughput and amount of resources can be controlled by adjusting the weights of each device, so different QoS requirements can be guaranteed.

(In)Efficiency of Employment Offices: A Study on Welfare Benefits Determination – Is There a Trade-Off between Time Saving Case Management and Quality of Decisions?

This study investigates the efficiency of the process of benefit determination for welfare recipients in Germany. A stochastic frontier analysis is used to compute (in)efficiency of Jobcenter (employment offices) in terms of average processing time used for determining benefit levels per case. Next, the quality of the process of welfare benefits determination is considered by analyzing the share of upheld opposition because of misapplication of the laws. No effect of the (in)efficiency term on quality is estimated such that the quality of decision is unrelated to the time input. Manning of the employment offices seems to be to a large extent determined by other factors than a fair allocation of scarce resources in relation to demand for them. However in the case of treatment of one particular group (newly registered unemployed) and one organizational measure (offices that arrange specific appointments) a trade-off is estimated. Moreover, better skilled employees need less time for servicing cases and produce less erroneous decision.

Joint Management of Wireless and Computing Resources for Computation Offloading in Mobile Edge Clouds

We consider the computation offloading problem in an edge computing system in which an operator jointly manages wireless and computing resources across devices that make their offloading decisions autonomously with the objective to minimize their own completion times. We develop a game theoretical model of the interaction between the devices and an operator that can implement one of two resource allocation policies, a cost minimizing or a time fair resource allocation policy. We express the optimal cost minimizing resource allocation policy in closed form and prove the existence of Stackelberg equilibria for both resource allocation policies. We propose two efficient decentralized algorithms that devices can use for computing equilibria of offloading decisions under the cost minimizing and the time fair resource allocation policies. We establish bounds on the price of anarchy of the games played by the devices and by doing so we show that the proposed algorithms have bounded approximation ratios. Our simulation results show that the cost minimizing resource allocation policy can achieve significantly lower completion times than the time fair allocation policy. At the same time, the convergence time of the proposed algorithms is approximately linear in the number of devices, and thus they could be effectively implemented for edge computing resource management.

Voltage/Var Control for Hybrid Distribution Networks Using Decomposition-Based Multiobjective Evolutionary Algorithm

Hybrid distribution networks manage not only the traditional voltage/var controllers such as on-load tap changers and capacitor banks but also the independent entities such as grid-tied microgrids. Voltage/Var control (VVC) is challenging in this evolved system due to the difficulty of motivating the participation of various entities with conflicting interests. This paper proposes a multiobjective voltage/var optimisation model seeking tradeoff solutions to drive all the entities to maximise their VVC contributions. In the multiobjective VVC model, the contributions of different entities are identified by a fair resource allocation based approach. Moreover, the operational model of grid-tied hybrid AC/DC microgrid is integrated into the VVC to extend the application scenario to the future hybrid distribution network. The proposed VVC fairly identifies the contributions and adequately captures the tradeoffs among various independent entities, so their participation in VVC can be encouraged. The proposed multiobjective voltage/var optimisation model is solved by a genetic based solver called multiobjective evolutionary algorithm based on decomposition. The proposed VVC is tested on a modified IEEE 33 nodes system to demonstrate its effectiveness and advantages.

Application of Kuramoto Model to Transmission Power Control in Wireless Body Area Networks

Wireless body area networks (WBANs) exhibits immense potential for application in ubiquitous healthcare systems. However, in dense environments where multiple WBANs are deployed such as hospitals, the performance of WBANs is deteriorated by inter-network interference. In this study, we apply Kuramoto model to the transmission power control of users in WBAN environments with the purpose of addressing the issue of inter-network interference, and thereby improving the Quality of Service (QoS) fairness in WBAN environment. For this purpose, we define the utility function which expresses the QoS of a user and synchronize this utility function value of each user for fair resource allocation. The synchronization of the utility function is obtained by Kuramoto model and coupling coefficient defined in Kuramoto model is dynamically adjusted for faster convergence. The range of convergence of the Kuramoto model is derived analytically. From the simulation results, we can verify that the performance of the proposed algorithm is better than those of the existing algorithms with regard to QoS fairness, power, and data rate. In addition, we show that the convergence speed of the proposed algorithm with adaptive coupling strength parameter outperforms the Kuramoto model with fixed coupling strength parameter.

Ethical dilemmas in COVID-19 times: how to decide who lives and who dies?

The respiratory disease caused by the coronavirus SARS-CoV-2 (COVID-19) is a pandemic that produces a large number of simultaneous patients with severe symptoms and in need of special hospital care, overloading the infrastructure of health services. All of these demands generate the need to ration equipment and interventions. Faced with this imbalance, how, when, and who decides, there is the impact of the stressful systems of professionals who are at the front line of care and, in the background, issues inherent to human subjectivity. Along this path, the idea of using artificial intelligence algorithms to replace health professionals in the decision-making process also arises. In this context, there is the ethical question of how to manage the demands produced by the pandemic. The objective of this work is to reflect, from the point of view of medical ethics, on the basic principles of the choices made by the health teams, during the COVID-19 pandemic, whose resources are scarce and decisions cause anguish and restlessness. The ethical values for the rationing of health resources in an epidemic must converge to some proposals based on fundamental values such as maximizing the benefits produced by scarce resources, treating people equally, promoting and recommending instrumental values, giving priority to critical situations. Naturally, different judgments will occur in different circumstances, but transparency is essential to ensure public trust. In this way, it is possible to develop prioritization guidelines using well-defined values and ethical recommendations to achieve fair resource allocation.

Scheduling Fair Resource Allocation Policies for Cloud Computing through Flow Control

In this short paper, we discuss the problem of resource allocation for cloud computing. The cloud provides a variety of resources for users based on their requirements. Thus, one of the main issues in cloud computing is to design an efficient resource allocation scheme. Each job generated by a user in the cloud has some resource requirements. In this work, we propose a resource allocation method which aims at maximizing the resource utilization and distributing the system’s resources in a fast and fair way, by controlling the flow according to the resources available and by analyzing the dominant demands of each job. Moreover, by parallelizing the computations required, the runtime of the proposed strategy increases linearly as the number of jobs N increases. Here, we present some initial experimental results for small sets of users, that have shown that our strategy allocates the available resources among user jobs in a fair manner, while increasing the overall utilization of each resource.

Effect of reducing the price of anarchy on fairness in highway resource allocation for individual users

The price of anarchy (PoA) is the efficiency loss of a system resulting from individual route choice behavior. Network traffic management is often aimed at moving the system from the user equilibrium (UE) state to the system optimum (SO) state, or to reduce the PoA. This, however, may have a negative impact on individual users. This paper examines the effect of reducing the PoA on individual users or user groups. We quantify the gains and losses of users or user groups and the room for improvement resulting from a change from the UE state to the SO state with a generalized cost function comparable to the BPR function. Properties of changes in flows and costs for alternative routes and their relationships to one another are derived. The implication of traffic control aimed at reducing the price of anarchy and at the same time ensuring fairness in highway resource allocation is discussed.