Reliable Resource Allocation Research Articles

Systematic review of IPSAS standards: The introduction of IPSAS in public establishments

The ultimate objective of this paper is to examine the impact of the adoption of IPSAS in Morocco on the production of accounting and financial information, in order to inform decision-making processes. IPSAS ushered in a global revolution in public accounting reforms, and Morocco is among the nations that have adopted these standards, starting their implementation several years ago. The integration of IPSAS mobilises a theoretical and conceptual framework encompassing the theories and New Public Management (NPM), as well as the theory of public value. The aim is to harmonise accounting practices with global standards, thereby enhancing the transparency, comparability and credibility of financial information. However, the implementation of IPSAS in Moroccan institutions raises a number of issues and challenges, particularly in terms of adapting systems, training staff and managing costs. To deepen our understanding of this topic, we conducted a bibliometric analysis using the VOSviewer tool. This analysis enabled us to identify influential authors, collaborative networks, and the main and emerging themes in IPSAS research. The results show that the implementation of IPSAS helps to ensure good quality and transparency in public sector financial reporting, providing better information for financial management, accountability and decision making. IPSAS, as international accounting standards specific to the public sector, aim to improve the quality of financial reporting by public entities, thereby enabling more reliable resource allocation decisions and contributing to improved financial transparency and accountability.

Radar: Reliable Resource Scheduling for Composable/Disaggregated Data Centers

Hardware disaggregation decouples resources (e.g., processors and memory) from monolithic servers, potentially improving service reliability. However, from another perspective, directly exposing resource modules to a shared network may adversely affect service reliability. In this paper, we study a reliable resource allocation problem in disaggregated DCs (DDCs), considering network impact and different disaggregation scales. We provide a mixed-integer linear programming formulation and a resource allocation framework named Radar for this problem. Numerical results demonstrate that the benefits of hardware disaggregation may be adversely affected by an imperfect network. It also shows that both the hardware backup and a proposed migration-based restoration can be applied to overcome this potential adverse effect.

A hybrid grey wolf optimizer using opposition-based learning, sine cosine algorithm and reinforcement learning for reliable scheduling and resource allocation

As the number of space debris in geosynchronous Earth orbits continues to grow, the threat posed by space debris to satellites surveillance is increasing, and the available orbital resources are also decreasing. Thus, reasonably scheduling and allocating the resources for space object tracking has become vital. This paper establishes an optimization model for the resource allocation and scheduling problem for space debris tracking. A fusion algorithm that combines the grey wolf optimizer, opposition-based learning, sine cosine search strategy, and reinforcement learning was proposed and used to solve the problem. Six groups of realistic data were selected based on the relevant background information of space debris tracking to test the validity and effectiveness of the proposed algorithm. The performance of the state-of-the-art optimization algorithms was compared with that of the proposed algorithms. The result of the experiment indicates that the proposed algorithm effectively solves the resource allocation and scheduling problem for space debris tracking.

Reliable Resource Allocation and Management for IoT Transportation Using Fog Computing

Resource allocation in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.

On Attacking Future 5G Networks with Adversarial Examples: Survey

The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to dynamically create and deploy multiple services which function under various requirements in different vertical sectors while operating on top of the same physical infrastructure. The recent progress in artificial intelligence and machine learning is theorized to be a potential answer to the arising resource allocation challenges. It is therefore expected that future generation mobile networks will heavily depend on its artificial intelligence components which may result in those components becoming a high-value attack target. In particular, a smart adversary may exploit vulnerabilities of the state-of-the-art machine learning models deployed in a 5G system to initiate an attack. This study focuses on the analysis of adversarial example generation attacks against machine learning based frameworks that may be present in the next generation networks. First, various AI/ML algorithms and the data used for their training and evaluation in mobile networks is discussed. Next, multiple AI/ML applications found in recent scientific papers devoted to 5G are overviewed. After that, existing adversarial example generation based attack algorithms are reviewed and frameworks which employ these algorithms for fuzzing stat-of-art AI/ML models are summarised. Finally, adversarial example generation attacks against several of the AI/ML frameworks described are presented.

RF Fingerprint-Identification-Based Reliable Resource Allocation in an Internet of Battle Things

With the advances of Internet of Things (IoT) technology, the Internet of Battlefield Things (IoBT) has revolutionized the military battlefield. Unlike civilian IoT applications, a few adversarial attackers may sneak into an IoBT and destroy the network infrastructure by generating cyberattacks, e.g., Distributed Denial-of-Service (DDoS) attacks, making the Quality of Service (QoS) of network dramatically decrease. To improve the performance of a network, we propose two schemes of enhancing network QoS: one is user access control with the aid of RF fingerprinting because of small-scaled available signal samples, while the other is the optimization of network performance. Specifically, we address the optimization of network utility with the methods of power allocation and channel allocation. Mathematically, we propose a mixed-integer nonlinear (MIN) problem, which optimizes the allocation of wireless resources. Also, the original optimization problem is decomposed into a power allocation subproblem and a channel allocation subproblem, and both these subproblems are nondeterministic polynomial (NP) hard problems. We propose a few approximate algorithms, which can iteratively converge to the optimum of the original problem in polynomial time. The simulation results show that our proposed algorithms can improve network utility and various network QoS than the other algorithms in an IoBT environment.

Reliable resource allocation with RF fingerprinting authentication in secure IoT networks

Reliable resource allocation with RF fingerprinting authentication in secure IoT networks

Reliability and robust resource allocation for Cache-enabled HetNets: QoS-aware mobile edge computing

Mobile edge computing is a key feature of next-generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Due to the high frame loss rate during multi-hop transmissions in wireless edge computing networks, the problem of reliability and system safety will be of great importance. Data caching is a necessity to provide reliable edge computing and, at the same time a major consumer of energy. So, we propose a reliable mobile edge computing system using active caching and robust resource allocation approaches. This paper presents an energy-efficient stochastic network calculus framework to control edge-computing data flows for active data-caching systems. In accordance with the entrance processes of different QoS-class data streams, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a reliable user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to improving system safety. In this energy-cooperative approach, the power can be shared among the cells using a grid network. The simulation results show that the proposed approach can effectively increase the network reliability and power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot user distribution models. The numerical results also exhibit considerable power saving for different traffic models in addition to increasing the total network throughput by up to 25%.

Distance Aware VM Allocation Process to Minimize Energy Consumption in Cloud Computing

Background: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. Aim and Objective: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. Methods: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Results and Conclusion: Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

Fuzzy Matching Learning for Dynamic Resource Allocation in Cellular V2X Network

Benefiting from the global deployment and fast commercialization of cellular systems, cellular-enabled vehicle-to-everything (V2X) opens up a significant prospect in the foreseeable future, for supporting proliferating vehicular services. Reliable and efficient resource allocation is a prerequisite for enjoying excellent quality of vehicular communication experiences, which yet still remains a serious challenge due to the inherent vehicle mobility and network heterogeneity. In this work, taking the rapidly varying channel state information (CSI) into consideration, we investigate the joint time-frequency allocation problem for the heterogeneous cellular V2X network, where resource sharing based on hierarchical reuse among different V2X communication types are granted. To address the high dynamics of V2X environment, a mapped fuzzy space is introduced, in which the uncertain CSI is interpreted as fuzzy numbers. This could effectively avoid the deterioration of such dynamic environment to the stability and convergence of optimization mechanisms. On this basis, we construct a two-side many-to-many fuzzy matching game (MM-FMG) to formulate the optimization problem with the dynamic and uncertain information, and further propose a vehicle-resource dynamic matching algorithm to solve this MM-FMG problem. Our developed scheme can achieve both reliable and efficient resource assignment by excluding the precise CSI, which is hence more suitable to the concerned V2X scenarios. Finally, the superior performances of our designed algorithm are demonstrated via numerical simulations.

Resource Allocation Scheme for Community-Based Fog Computing Based on Reputation Mechanism

Fog computing needs to seamlessly integrate heterogeneous computing resources widely distributed in edge networks, and then, it can provide unified resources and services for users. However, due to a large number of fog nodes, there are still many security problems in the fog computing process. For example, when fog servers make resource allocation between users' tasks and fog nodes, some fog nodes can falsely claim that they have more resources than their actual ability, so as to get more task processing qualifications. Thus, it will greatly damage the interests of users and affect the quality of service. In this article, we propose a resource allocation scheme for community-based fog computing based on a reputation mechanism. When fog network provides computing services for users, we use a reputation mechanism to enable users to obtain reliable resources in fog computing. In our proposed scheme, a user first submits his/her task request to the community-based fog network, and then, the fog server makes a reliable resource allocation process based on multiple-layer communities and reputation calculation. Based on our experiments, our scheme enables users to obtain reliable resources and improves the service quality of computing resources in fog computing.

RETRACTED ARTICLE: Efficient optimal resource allocation for profit maximization in software defined network approach to improve quality of service in cloud environments

Software-defined networking (SDR) technology is an approach to network management that enables dynamic, programmatically efficient network configuration in order to improve network performance and monitoring making it more like cloud computing than traditional network management. Cloud Resource scheduling is used to schedule the workload-based customer request. Here, cost effective resources allocation is introduced based on arriving request and cluster allocation. The profit maximizing scheme aims is to provide probabilistic guarantee against the resource overloading and migration. In this work, proposed software defined approach namely Modified Heuristic Search (MHS) Algorithm is proposed to achieve the cost-effective resources allocation in distributing computing environment to improve the Quality of Service in Cloud environment and its applications. To achieve the profit maximization, Cost Effective Reliable Resource allocation (CERRA) algorithm is utilized to measure the effective cluster selection in MHSA which includes a fitness function for selecting the arriving cloud requests to earn profit. Speed, transfer rate and energy are measured and compared with the existing method to analysis the resource allocation system.

Deep Reinforcement Learning Based Green Resource Allocation Mechanism in Mobile Edge Network for Ubiquitous Power IoT

Ubiquitous Power IoT is an important part of smart grid or energy internet. Edge network is a key component of smart grid and future energy Internet. IoT contains various terminals, diverse access networks and equipment. The allocation of energy-efficient resources under QoS guarantee is an effective means to ensure the reliable operation of the network. Due to the heterogeneity of the services carried and in order to reduce business operation risks and impact on the power grid, it is necessary to provide highly reliable resource allocation method to the ubiquitous power IoT business for the energy Internet. Edge network can reduce end-to-end latency and reduce backhaul link traffic. However, mobile edge networks moving the function of storage and computing down to the edge nodes of network, which makes resource management more complex. Moreover, the computing resources in the edge network is limited and the effect to the ecological environment and economic cost should also be considered. So how to allocate resources such as bandwidth and power more effectively while meeting the needs of users becomes an important issue. Even if the Deep Reinforcement Learning (DRL) algorithm has been used to a lot of the work which are correlated to edge networks, there lacks the applications for green resource allocation. In this paper, a mechanism based on Deep Reinforcement Learning (DRL) for the resource allocation problem is proposed oriented to edge networks. The mechanism aims at how to allocate resources more efficiently and energy-saving while satisfying the requirements of each user. The simulation results show that the energy efficiency value could be obtained when the algorithm converges and stabilizes. The Energy Efficiency (EE) of the proposed mechanism and the productiveness in satisfying user requirements and implementing green resource allocation are validated.

On Fault-Tolerant Bin Packing for Online Resource Allocation

We study an online fault-tolerant bin packing problem that models reliable resource allocation. In this problem, each item is replicated and has f + 1 replicas including one primary and f standbys. The packing of items is required to tolerate up to f faulty bins, i.e., to guarantee that at least one correct replica of each item is available regardless of which f bins turn to be faulty. Any feasible packing algorithm must satisfy an exclusion constraint and a space constraint. The exclusion constraint is generalized from the fault tolerance requirement and the space constraint comes from the capacity planning. The target of bin packing is to minimize the number of bins used. We first derive a lower bound on the number of bins needed by any feasible packing algorithm. We then study three heuristic algorithms named mirroring, shifting and mixing under a particular setting where all items have the same size. The mirroring algorithm has a low utilization of the bin capacity. Compared with the mirroring algorithm, the shifting algorithm requires fewer bins. However, in online packing, the process of opening bins by the shifting algorithm is not smooth. It turns out that even for packing a few items, the shifting algorithm needs to quickly open a large number of bins. The mixing algorithm adopts a dual average strategy to gradually open new bins for incoming items. We prove that the mixing algorithm is feasible and show that it balances the number of bins used and the process of opening bins. Finally, to pack items with different sizes, we extend the mirroring algorithm by adopting the First-Fit strategy and extend both the shifting and mixing algorithms by involving the harmonic strategy. The asymptotic competitive ratios of the three extended algorithms are analyzed respectively.

Twin-Timescale Radio Resource Management for Ultra-Reliable and Low-Latency Vehicular Networks

To efficiently support safety-related vehicular applications, the ultra-reliable and low-latency communication (URLLC) concept has become an indispensable component of vehicular networks (VNETs). Due to the high mobility of VNETs, exchanging near-instantaneous channel state information (CSI) and making reliable resource allocation decisions based on such short-term CSI evaluations are not practical. In this paper, we consider the downlink of a vehicle-to-infrastructure (V2I) system conceived for URLLC based on idealized perfect and realistic imperfect CSI. By exploiting the benefits of the massive MIMO concept, a two-stage radio resource allocation problem is formulated based on a novel twin-timescale perspective for avoiding the frequent exchange of near-instantaneous CSI. Specifically, based on the prevalent road-traffic density, Stage 1 is constructed for minimizing the worst-case transmission latency on a long-term timescale. In Stage 2, the base station allocates the total power at a short-term timescale according to the large-scale fading CSI encountered for minimizing the maximum transmission latency across all vehicular users. Then, a primary algorithm and a secondary algorithm are conceived for our V2I URLLC system to find the optimal solution of the twin-timescale resource allocation problem, with special emphasis on the complexity imposed. Finally, our simulation results show that the proposed resource allocation scheme significantly reduces the maximum transmission latency, and it is not sensitive to the fluctuation of road-traffic density.

A Novel Resource Allocation and Spectrum Defragmentation Mechanism for IoT-Based Big Data in Smart Cities.

People’s demand for high-traffic applications and the need for Internet of Things (IoT) are enormous in smart cities. The amount of data generated by virtual reality, high-definition video, and other IoT applications is growing at an exponential rate that far exceeds our expectations, and the types of data are becoming more diverse. It has become critical to reliably accommodate IoT-based big data with reasonable resource allocation in optical backbone networks for smart cities. For the problem of reliable transmission and efficient resource allocation in optical backbone networks, a novel resource allocation and spectrum defragmentation mechanism for massive IoT traffic is presented in this paper. Firstly, a routing and spectrum allocation algorithm based on the distance-adaptive sharing protection mechanism (DASP) is proposed, to obtain sufficient protection and reduce the spectrum consumption. The DASP algorithm advocates applying different strategies to the resource allocation of the working and protection paths. Then, the protection path spectrum defragmentation algorithm based on OpenFlow is designed to improve spectrum utilization while providing shared protection for traffic demands. The lowest starting slot-index first (LSSF) algorithm is employed to remove and reconstruct the optical paths. Numerical results indicate that the proposal can effectively alleviate spectrum fragmentation and reduce the bandwidth-blocking probability by 44.68% compared with the traditional scheme.

Holding maximum customers in cloud business environment by efficient load balancing methods based on MPSO-MC

As is well-known Cloud is an Environment for sharing resources based on Anything as a Service (XaaS) pattern that includes software, platform, infrastructure, storage, etc. on demand. For allocating resources and managing it efficiently, the load has to be balanced on the cloud paradigm. Moreover, the reliable resource allocation with load balancing has become the significant resource focus in the current scenario. In the heterogeneous cloud environment, dispersion and uncertainty of cloud resources faces issues on the process of allocation that are not effectively handled and accessed by the existing approaches. With that concern, for providing proficient resource scheduling with apposite load balancing, an efficient load-balancing model based on modified particle swarm optimization with membrane computing has been proposed. Based on that, suitable resources are allocated for different jobs in accordance with the factors like completion time, scalability, makespan, utilization of resources, reliability, availability, etc. Moreover, in this paper, effective resource scheduling has been achieved with the modified particle swarm optimization that combined with membrane computing local and glob optimization of inter-membranes for providing an optimal solution. Spatial segmentation has also been performed for enhancing the membrane-based optimization.

Dynamic load balancing with tokens

Efficiently exploiting the resources of data centers is a complex task that requires efficient and reliable load balancing and resource allocation algorithms. The former are in charge of assigning jobs to servers upon their arrival in the system, while the latter are responsible for sharing the server resources between their assigned jobs. These algorithms should adapt to various constraints, such as data locality, that restrict the feasible job assignments. In this paper, we propose a token-based algorithm that efficiently balances the load between the servers without requiring any knowledge on the job arrival rates and the server capacities. Assuming a balanced fair sharing of the server resources, we show that the resulting dynamic load balancing is insensitive to the job size distribution. Its performance is compared to that obtained under the best static load balancing and in an ideal system that would constantly optimize the resource utilization. We also make the connection with other token-based algorithms such as Join-Idle-Queue.

Distributed Dynamic Management System Based on Recurrent Neural Networks

Distributed network system is responsible for many dynamic information computing services, and all tasks are scheduled through resource managers. Large-scale distributed network systems need to integrate heterogeneous and dynamic characteristics into the content of resource allocation and dynamic management. However, the diversity of network equipment and internal communication brings great difficulties to resource allocation/dynamic management. The effectiveness of scheduling decisions issued by resource managers is also difficult to determine. The resource allocation and dynamic management scheme of distributed network system proposed in this paper adopts bi-directional recurrent neural network learning to realize reliable and efficient resource allocation and operation and maintenance management solution. The output of bi-directional cyclic neural network is dependent on the current input and memory to synthetically decide when to match a task with a resource and improve the utilization and performance. Experiments in real distributed network systems show that the technical indicators of this method, including efficiency and execution rate, will not be significantly reduced by the increase in the number of tasks. Therefore, this method can further enhance the reliability and scalability of the resource allocation and dynamic management of distributed network systems.

Secure and Reliable Resource Allocation and Caching in Aerial-Terrestrial Cloud Networks (ATCNs)

Aerial-terrestrial cloud networks (ATCNs), global integration of air and ground communication systems, pave a way for a large set of applications such as surveillance, on-demand transmissions, data-acquisition, and navigation. However, such networks suffer from crucial challenges of secure and reliable resource allocation and content-caching as the involved entities are highly dynamic and there is no fine-tuned strategy to accommodate their connectivity. To resolve this quandary, cog-chain, a novel paradigm for secure and reliable resource allocation and content-caching in ATCNs, is presented. Various requirements, key concepts, and issues with ATCNs are also presented along with basic concepts to establish a cog-chain in ATCNs. Feed and fetch modes are utilized depending on the involved entities and caching servers. In addition, a cog-chain communication protocol is presented which avails to evaluate the formation of a virtual cog-chain between the nodes and the content-caching servers. The efficacy of the proposed solution is demonstrated through consequential gains observed for signaling overheads, computational time, reliability, and resource allocation growth. The proposed approach operates with the signaling overheads ranging between 30.36 and 303.6 bytes*hops/sec and the formation time between 186 and 195 ms. Furthermore, the overall time consumption is 83.33% lower than the sequential-verification model and the resource allocation growth is 27.17% better than the sequential-verification model.