Robust Resource Allocation Research Articles

Robust power allocation for orthogonal frequency division multiplexing‐based overlay/underlay cognitive radio network under spectrum sensing errors and channel uncertainties

In this study, a robust power allocation scheme with orthogonal frequency division multiplexing-based cognitive radio network is proposed to maximise total data transmission rate subject to interference power constraint of primary user (PU) and transmit power budget constraint of secondary user where channel uncertainties and spectrum sensing errors are simultaneously considered. The authors first formulate the interference model by taking the imperfect spectrum sensing into account, then the channel state information errors are considered and assumed to be bounded with ellipsoidal and interval sets to establish robust resource allocation problem. On the basis of the worst-case approach and Lagrange dual decomposition method, the original optimisation problem is converted into a convex one and solved. Simulation results show the robustness of their proposed scheme and the trade-off performance with a sub-optimal data transmission rate, but better protection of PU.

Robust Layered Transmission in Secure MISO Multiuser Unicast Cognitive Radio Systems

This paper studies robust resource allocation algorithm design for a multiuser multiple-input single-output (MISO) cognitive radio (CR) downlink communication network. We focus on a secondary system which provides unicast secure wireless layered video information to multiple single-antenna secondary receivers. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total transmit power at the secondary transmitter. The proposed framework takes into account a quality of service (QoS) requirement regarding video communication secrecy in the secondary system, the imperfection of the channel state information (CSI) of potential eavesdroppers (primary receivers) at the secondary transmitter, and a limit for the maximum tolerable received interference power at the primary receivers. Thereby, the proposed problem formulation exploits the self-protecting architecture of layered transmission and artificial noise generation to ensure communication secrecy. Semidefinite programming (SDP) relaxation is employed to derive a resource allocation algorithm which finds the global optimal solution to the formulated problem. Simulation results demonstrate significant transmit power savings and robustness against CSI imperfection for the proposed resource allocation algorithm for layered transmission compared to baseline schemes with traditional single-layer transmission.

A robust reliable energy-aware urgent computing resource allocation for flash-flood ensemble forecasting on HPC infrastructures for decision support

Forecasting severe hydro-meteorological events under time constraints requires reliable and robust models, which facilitate energy-aware allocations on high performance computing infrastructures. We present an innovative approach to quantify the performance of six heuristics in selecting optimal allocations to distributed HPC resources for an ensemble of meteorological forecasts for a flash-flood producing storm in Genoa (Liguria, Italy) in October 2014. The computing environments are expected to be dynamic and heterogeneous in nature with varying availability, performance and energy-to-solution. The results of the allocations are assessed and compared. The aim is to provide a robust, reliable and energy-aware resource allocation for ensembles of forecasts for time-critical decision support.

Multi-Objective Optimization for Robust Power Efficient and Secure Full-Duplex Wireless Communication Systems

In this paper, we investigate the power efficient resource allocation algorithm design for secure multiuser wireless communication systems employing a full-duplex (FD) base station (BS) for serving multiple half-duplex (HD) downlink (DL) and uplink (UL) users simultaneously. We propose a multi-objective optimization framework to study two conflicting yet desirable design objectives, i.e., total DL transmit power minimization and total UL transmit power minimization. To this end, the weighed Tchebycheff method is adopted to formulate the resource allocation algorithm design as a multi-objective optimization problem (MOOP). The considered MOOP takes into account the quality-of-service (QoS) requirements of all legitimate users for guaranteeing secure DL and UL transmission in the presence of potential eavesdroppers. Thereby, secure UL transmission is enabled by the FD BS and would not be possible with an HD BS. The imperfectness of the channel state information of the eavesdropping channels and the inter-user interference channels is incorporated for robust resource allocation algorithm design. Although the considered MOOP is non-convex, we solve it optimally by semidefinite programming (SDP) relaxation. Simulation results not only unveil the trade-off between the total DL transmit power and the total UL transmit power, but also confirm the robustness of the proposed algorithm against potential eavesdroppers.

Stochastic-based robust dynamic resource allocation for independent tasks in a heterogeneous computing system

Heterogeneous parallel and distributed computing systems frequently must operate in environments where there is uncertainty in system parameters. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. In such an environment, the execution time of any given task may fluctuate substantially due to factors such as the content of data to be processed. Determining a resource allocation that is robust against this uncertainty is an important area of research. In this study, we define a stochastic robustness measure to facilitate resource allocation decisions in a dynamic environment where tasks are subject to individual hard deadlines and each task requires some input data to start execution. In this environment, the tasks that cannot meet their deadlines are dropped (i.e., discarded). We define methods to determine the stochastic completion times of tasks in the presence of the task dropping. The stochastic task completion time is used in the definition of the stochastic robustness measure. Based on this stochastic robustness measure, we design novel resource allocation techniques that work in immediate and batch modes, with the goal of maximizing the number of tasks that meet their individual deadlines. We compare the performance of our technique against several well-known approaches taken from the literature and adapted to our environment. Simulation results of this study demonstrate the suitability of our new technique in a dynamic heterogeneous computing system.

A bi-objective robust model for emergency resource allocation under uncertainty

Emergency resource allocation constitutes one of the most critical elements of response operations in the field of emergency management. This paper addresses an emergency resource allocation problem which involves multiple competing affected areas and one relief resource centre under supply shortage and uncertainty in the post-disaster phase. In humanitarian situations, both the efficiency and fairness of an allocation policy have a considerable influence on the effectiveness of emergency response operations. Thus, we formulate a bi-objective robust emergency resource allocation (BRERA) model which tries to maximise efficiency as well as fairness under different sources of uncertainties. To obtain decision-makers’ most preferred allocation policy, we propose a novel emergency resource allocation decision method which consists of three steps: (1) develop a bi-objective heuristic particle swarm optimisation algorithm to search the Pareto frontier of the BRERA model; (2) select a coefficient to measure fairness; and (3) establish a decision method based on decision-makers’ preference restricted by the fairness coefficient. Finally, a real case study taken from the 5 December 2008 Wenchuan Earthquake demonstrates the effectiveness of the proposed method through numerical results. The solution and model robustness are also analysed.

Robust Joint Resource Allocation for OFDMA-CDMA Spectrum Refarming System

In this paper, we investigate a spectrum refarming system where an OFDMA system shares the licensed band of a CDMA system. Both systems share the same cell site, but each has different base station (BS) antennas. Joint resource allocation problem is formulated to optimize the CDMA receive power, OFDMA transmit power, and subcarrier assignment. Conventional interference control to protect the primary CDMA system relies on the availability of cross-channel gains (CCGs) from each secondary OFDMA transmitter to the CDMA BS receiver. However, the CCGs are difficult to be obtained due to lack of intersystem cooperation. To address this problem, we first decouple the original problem into higher and lower-level problems via primal decomposition. Then, a robust lower-level resource allocation (R-LRA) scheme, which controls interference without using CCGs is proposed, with which CDMA users can be sufficiently protected. Thereafter, an enhanced R-LRA (ER-LRA) scheme is proposed to decrease the conservation of R-LRA scheme. Assisted by the CDMA inner power control and based on the ER-LRA, efficient algorithm is designed to solve the higher-level problem. Extensions of the ER-LRA for other scenarios are also studied. Simulation results are provided to validate the proposed schemes in facilitating and improving the spectrum sharing performance.

Robust Scheduling and Resource Allocation in the Downlink of Spatially Correlated MIMO-OFDMA Wireless Systems With Imperfect CSIT

Multiple-input-multiple-output (MIMO) orthogonal frequency-division multiple access (OFDMA) has been selected as the core physical-layer access scheme for the downlink of state-of-the-art and next-generation wireless communications standards. In these systems, scheduling and resource allocation (SRA) algorithms, jointly assigning transmission data rates, bandwidth, and power, become crucial in optimizing resource utilization while providing support to multimedia applications with heterogeneous quality-of-service (QoS) requirements. To this end, the transmitter is assumed to have channel state information at the transmitter (CSIT) that will typically be imperfect. This paper introduces a unified analytical framework for robust channel- and queue-aware QoS-guaranteed cross-layer SRA algorithms for the downlink of MIMO-OFDMA networks with imperfect CSIT. The framework is based on the statistical characterization of the signal-to-noise ratio (SNR) under imperfect CSIT and is general enough to encompass spatial correlation effects in the Tx and Rx antenna arrays, different types of traffic, uniform and continuous power allocation, discrete and continuous rate allocation, and protocols with different amounts of channel and queue awareness. Simulation results using parameters drawn from the Third-Generation Partnership Project Long-Term Evolution (3GPP-LTE) standard demonstrate the validity and advantages of the proposed robust cross-layer unified approach.

Robust adaptive power control for cognitive radio networks

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal-to-interference-and-noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst-case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.

Makespan and Energy Robust Stochastic Static Resource Allocation of a Bag-of-Tasks to a Heterogeneous Computing System

Today’s data centers face the issue of balancing electricity use and completion times of their workloads. Rising electricity costs are forcing data center operators to either operate within an electricity budget or to reduce electricity use as much as possible while still maintaining service agreements. Energy-aware resource allocation is one technique a system administrator can employ to address both problems: optimizing the workload completion time (makespan) when given an energy budget, or to minimize energy consumption subject to service guarantees (such as adhering to deadlines). In this paper, we study the problem of energy-aware static resource allocation in an environment where a collection of independent (non-communicating) tasks (“bag-of-tasks”) is assigned to a heterogeneous computing system. Computing systems often operate in environments where task execution times vary (e.g., due to cache misses or data dependent execution times). We model these execution times stochastically, using probability density functions. We want our resource allocations to be robust against these variations, where we define energy-robustness as the probability that the energy budget is not violated, and makespan-robustness as the probability a makespan deadline is not violated. We develop and analyze several heuristics for energy-aware resource allocation for both energy-constrained and deadline-constrained problems.

Robust capacity maximization transceiver design for MIMO OFDM systems

In this paper, we investigated capacity maximization problem for Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing systems with imperfect channel state information (CSI). To the best of our knowledge, the considered problem is still an open problem. However, the transceiver designs for MIMO OFDM systems have been extensively studied. It seems nobody gives closed-form solutions for resource allocation for MIMO OFDM systems with statistical channel estimation errors up to date. In our work, based on practical channel estimation algorithm, the channel estimation errors are first derived and then the robust resource allocation problem has been formulated. The structure of the optimal robust precoder is first derived, based on which the optimization problem will be simplified significantly. Furthermore, based on the Lagrangian dual method, a robust power allocation algorithm is proposed. The proposed power allocation can be considered as a variant of water-filling solution named cluster water-filling solution. Finally, simulation results show that our proposed robust design outperforms the non-robust design in terms of channel capacity.

Quantized Ergodic Radio Resource Allocation in Cognitive Femto Networks with Controlled Collision and Power Outage Probabilities

A robust Ergodic Resource Allocation (ERA) scheme is proposed in this paper in the framework of an orthogonal frequency division multiple access (OFDMA) based underlay heterogeneous network in which the allocations are made so as to maximize the average network sum-rate while guaranteeing the macro network interference requirements with any desired high probability. In previously proposed ERA schemes, both in conventional and heterogeneous networks, the optimal solution is obtained assuming that average of constraints are satisfied. In a heterogeneous network, this is translated into the fact that instantaneous level of interference on macro users can not be guaranteed, i.e., there is an uncontrolled probability of collision which is not acceptable by macro network. In this paper, we reformulate the ERA problem by replacing the average based constraints, in our case, femto total power constraint and macro interference threshold constraint, with their probabilistic counterparts so that both constraints are satisfied instantaneously with any desired high probability. We consider both cases of continuous and quantized channel state information. The proposed problems are then solved based on three methods, namely, iterative, analytical, and hybrid approaches. The optimality of the proposed methods is also assessed and the iterative approach is shown to have a performance quite close to that of the optimal solution. Simulation results confirm the effectiveness of the proposed scheme to provide a robust instantaneous imposed interference on macro network and a robust femto total transmit power. We also investigate the convergence properties of the proposed iterative approach.

Maximizing stochastic robustness of static resource allocations in a periodic sensor driven cluster

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete a task may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty—in a way that can provide a probability that a given level of QoS is achieved—is an important area of research. We have designed novel techniques for maximizing the probability that a given level of QoS is achieved. These techniques feature a unique application of both path relinking and local search within a Genetic Algorithm. In addition, we define a new methodology for finding resource allocations that are guaranteed to have a non-zero probability of addressing the timing constraints of the system. We demonstrate the use of this methodology within two unique steady-state genetic algorithms designed to maximize the robustness of resource allocations. The performance results for our techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.

Robust static resource allocation of DAGs in a heterogeneous multicore system

In this study, we consider an environment composed of a heterogeneous cluster of multicore-based machines used to analyze satellite data. The workload involves large data sets and is subject to a deadline constraint. Multiple applications, each represented by a directed acyclic graph (DAG), are allocated to a dedicated heterogeneous distributed computing system. Each vertex in the DAG represents a task that needs to be executed and task execution times vary substantially across machines. The goal of this research is to assign the tasks in applications to a heterogeneous multicore-based parallel system in such a way that all applications complete before a common deadline, and their completion times are robust against uncertainties in execution times. We define a measure that quantifies robustness in this environment. We design, compare, and evaluate five static resource allocation heuristics that attempt to maximize robustness. We consider six different scenarios with different ratios of computation versus communication, and loose and tight deadlines.

A matrix-based Bayesian approach for manufacturing resource allocation planning in supply chain management

Nowadays, the supply chain of manufacturing resources is typically a large complex network, whose management requires network-based resource allocation planning. This paper presents a novel matrix-based Bayesian approach for recommending the optimal resource allocation plan that has the largest probability as the optimal selection within the context specified by the user. A proposed matrix-based representation of the resource allocation plan provides supply chain modelling with a good basis to understand problem complexity, support computer reasoning, facilitate resource re-allocation, and add quantitative information. The proposed Bayesian approach produces the optimal, robust manufacturing resource allocation plan by solving a multi-criteria decision-making problem that addresses not only the ontology-based static manufacturing resource capabilities, but also the statistical nature of the manufacturing supply chain, i.e. probabilities of resource execution and resource interaction execution. A genetic algorithm is employed to solve the multi-criteria decision-making problem efficiently. We use a case study from manufacturing domain to demonstrate the applicability of the proposed approach to optimal manufacturing resource allocation planning.

Statistically robust resource allocation for distributed multi-carrier cooperative networks

In this paper, we investigate joint subcarrier and power allocation scheme for distributed multicarrier cooperative networks with imperfect channel state information. Using practical channel estimation algorithms, the statistic model of the channel estimation error is first derived. Then based on the channel error model, the resource allocation scheme aims at maximizing the sum rate of the overall network. Specifically, both subcarrier allocation and power allocation are taken into account. In the subcarrier allocation, a realistic problem of the power leakage between neighboring subcarriers is also addressed. Furthermore, based on Lagrange dual-decomposition algorithm, a practical power allocation algorithm is proposed. Finally, the simulation results demonstrate the performance advantages of the proposed robust design.

Traffic-Adaptive and Link-Quality-Aware Communication in Wireless Sensor Networks

This paper is a summary of the main contributions of the PhD thesis published in [1]. The main research contributions of the thesis are driven by the research question how to design simple, yet efficient and robust run-time adaptive resource allocation schemes within the communication stack of Wireless Sensor Network (WSN) nodes. The thesis addresses several problem domains with contributions on different layers of the WSN communication stack.

Reformulation based MaxSAT robustness

The presence of uncertainty in the real world makes robustness a desirable property of solutions to constraint satisfaction problems (CSP). A solution is said to be robust if it can be easily repaired when unexpected events happen. This issue has already been addressed in the frameworks of Boolean satisfiability (SAT) and Constraint Programming (CP). Most existing works on robustness implement search algorithms to look for robust solutions instead of taking the declarative approach of reformulation, since reformulation tends to generate prohibitively large formulas, especially in the CP setting. In this paper we consider the unaddressed problem of robustness in weighted MaxSAT, by showing how robust solutions to weighted MaxSAT instances can be effectively obtained via reformulation into pseudo-Boolean formulae. Our encoding provides a reasonable balance between increase in size and performance, as shown by our experiments in the robust resource allocation framework. We also address the problem of flexible robustness, where some of the breakages may be left unrepaired if a totally robust solution does not exist. In a sense, since the use of SAT and MaxSAT encodings for solving CSP has been gaining wide acceptance in recent years, we provide an easy-to-implement new method for achieving robustness in combinatorial optimization problems.

An effective and robust two-phase resource allocation scheme for interdependent tasks in mobile ad hoc computational Grids

This paper addresses the problem of resource allocation to interdependent tasks in mobile ad hoc computational Grids. Dependencies between tasks imply that there can be heavy communication induced by data transfers between tasks executed on separate nodes. The communication in mobile ad hoc Grids is always expensive and unreliable, and therefore plays a critical role in application performance. There are several factors that contribute to communication cost. Unreliable and short-term connectivity can increase communication cost due to frequent failure and activation of links, and ineffective resource allocation can increase communication cost due to multi hop communication between dependent tasks. To reduce communication cost, an effective and robust resource allocation scheme is required. However, the design of such a scheme for mobile ad hoc computational Grids exhibits numerous difficulties due to the constrained communication environment, node mobility, and lack of pre-existing network infrastructure.In this paper, we propose a two-phase resource allocation scheme to reduce communication cost between dependent tasks. The scheme is divided into two phases. The first phase exploits the history of user mobility patterns to select nodes that provide long-term connectivity and the second phase takes into account the task and dependency types, and uses the distance information among the nodes selected in the first phase to reduce communication costs. The scheme is validated in a simulation environment using various workloads and parameters.

Heuristics for Robust Resource Allocation of Satellite Weather Data Processing on a Heterogeneous Parallel System

This work considers the satellite data processing portion of a space-based weather monitoring system. It uses a heterogeneous distributed processing platform. There is uncertainty in the arrival time of new data sets to be processed, and resource allocation must be robust with respect to this uncertainty. The tasks to be executed by the platform are classified into two broad categories: high priority (e.g., telemetry, tracking, and control), and revenue generating (e.g., data processing and data research). In this environment, the resource allocation of the high-priority tasks must be done before the resource allocation of the revenue generating tasks. A two-part allocation scheme is presented in this research. The goal of first part is to find a resource allocation that minimizes makespan of the high-priority tasks. The robustness for the first part of the mapping is defined as the difference between this time and the expected arrival of the next data set. For the second part, the robustness of the mapping is the difference between the expected arrival time and the time at which the revenue earned is equal to the operating cost. Thus, the heuristics for the second part find a mapping that minimizes the time for the revenue (gained by completing revenue generating tasks) to be equal to the cost. Different resource allocation heuristics are designed and evaluated using simulations, and their performance is compared to a mathematical bound.