Heterogeneous Resource Allocation Research Articles

A Truthful Randomized Mechanism for Heterogeneous Resource Allocation With Multi-Minded in Mobile Edge Computing

A Truthful Randomized Mechanism for Heterogeneous Resource Allocation With Multi-Minded in Mobile Edge Computing

Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning

Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks’ different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.

Energy-Efficient 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU) Using a Hybrid OGASCDASA-SAGMDEBROA Scheduling Algorithm

In fifth-generation (5G) networks, the deployment of heterogeneous networks (HetNets) with macrocells layered over tiny cells is considered to be a practical solution to handle the growing demand for mobile traffic. The deployment of a significant count of small cell base stations (BSs) results in a notable rise in energy consumption. In this manuscript, Energy-Efficient (EE) 5G Heterogeneous Cloud Radio Access Networks (RRH to BBU), using Hybrid online green algorithm-based sleep scheduling and cost-efficient deadline-aware Scheduling Algorithm (OGASCDASA), are proposed. Here, the energy efficiency optimization issue is in the downlink of two-tier Heterogeneous Cloud Radio Access Networks (H-CRAN) by lowering micro and pico cells. Hybrid OGASCDASA is proposed for reducing remote radio side energy use when maintaining coverage and Quality of service (QoS) of H-CRAN. At the cloud side Baseband Units (BBU), hybrid Simulated Annealing with the Gaussian Mutation and Distortion Equalization algorithm with Battle Royale optimization is proposed to reduce the energy consumption of BBU by decreasing the count of BBU servers. The proposed EE-HCRAN-Hybrid OGASCDASA-SAGMDEBROA method attains 20.48%, 27.34%, and 32.24% higher throughput and 28.30%, 17.30%, and 32.94% lower delay compared to the existing models, such as heterogeneous computational resource allocation for NOMA (HCRA-NOMA-GMECS), energy-efficient hierarchical resource sharing in uplink-downlink decoupled NOMA heterogeneous networks (EE-HRA-NOMA-HetNet), energy-aware hierarchical resource management with backhaul traffic optimization in heterogeneous cellular networks (EA-HRM-BTO-HCN), respectively.

CPU–GPU Heterogeneous Computation Offloading and Resource Allocation Scheme for Industrial Internet of Things

CPU–GPU Heterogeneous Computation Offloading and Resource Allocation Scheme for Industrial Internet of Things

Optimal routing and heterogeneous resource allocation for computing-aware networks

Computing-aware networking (CAN) is introduced to unite the computing resources distributed in different platforms. This paper proposes a joint routing and resource allocation model to minimize the total operational cost in CAN. We formulate the problem as an integer linear programming problem. We introduce a polynomial-time algorithm for larger-size problems. The numerical results reveal that the introduced algorithm reduces the computation time 9.18 times, with increasing the objective no more than 4% compared to the optimal solution; the proposed model can reduce 70% of the total cost compared to a baseline adopting a two-stage strategy in our examined cases.

Cost-based resource allocation method for efficient allocation of resources in a heterogeneous cloud environment

Cloud computing is appealing due to features like adaptability, portability, utility service and on-demand service. Cloud resource providers are a source of computing, and each provider delivers different types of resources. In an active cloud environment, timely resource allocation is more important. In order to increase the effectiveness and user-friendliness of resource allocation in the heterogeneous cloud, the paper suggests an efficient cost-based resource allocation (ECRA) method and framework. In the heterogeneous cloud, there is no centralized resource allocation manager (CRAM) to get all requested resources from a single counter. The proposed methodology for allocating resources divides them according to their cost. The paper’s framework for allocating resources consists of various parts. The Unified Heterogeneous Resource Allocation Manager (UHRAM) part of the framework collects and manages resources from several cloud resource providers. The resource identifier is one of the components in the framework, which is coupled to UHRAM to determine the cost of the resources. The low-cost resources are scheduled and to be in a ready state for allocation. The proposed ECRA is simulated and compared based on parameters like total computation time, response time and resource allocation percentage with existing resource allocation methods. The results prove that the proposed ECRA is efficient in allocating the resources in minimum response time and it allocates maximum resources for lower cost.

Supporting dynamic allocation of heterogeneous storage resources on HPC systems

SummaryScaling up large‐scale scientific applications on supercomputing facilities is largely dependent on the ability to scale up efficiently data storage and retrieval. However, there is an ever‐widening gap between I/O and computing performance. To address this gap, an increasingly popular approach consists in introducing new intermediate storage tiers (node‐local storage, burst‐buffers,) between the compute nodes and the traditional global shared parallel file‐system. Unfortunately, without advanced techniques to allocate and size these resources, they remain underutilized. In this article, we investigate how heterogeneous storage resources can be allocated on an high‐performance computing platform, just like compute resources. To this purpose, we introduce StorAlloc, a simulator used as a testbed for assessing storage‐aware job scheduling algorithms and evaluating various storage infrastructures. We illustrate its usefulness by showing through a large series of experiments how this tool can be used to size a burst‐buffer partition on a top‐tier supercomputer by using the job history of a production year.

Equilibrium characterizations of multi-resource Lotto games

The allocation of heterogeneous resources plays an increasingly important role in the completion of system objectives and tasks. In this paper, we focus on deriving optimal strategies for the allocation of heterogeneous resources in a well-known adversarial model known as the General Lotto game. In standard formulations, outcomes are determined solely by the players’ allocation strategies of a single type of resource across multiple contests. In particular, a player wins a contest if it sends more resources than the opponent. Here, we propose a multi-resource extension where the winner of a contest is now determined not only by the amount of resources allocated, but also by the composition of resource types that are allocated. We completely characterize the equilibrium payoffs and strategies for two distinct formulations. The first consists of a weakest-link/best-shot winning rule, and the second considers a winning rule based on a linear combination of the allocated resources. We then provide equilibrium investment strategies in scenarios where the resource types are costly to purchase, and players each have a limited monetary budget.

A Data-driven method for adaptive resource requirement allocation via probabilistic solar load and market forecasting utilizing digital twin

Fog computing is used to handle applications that require a lot of calculations at the network's edges for wireless Internet of Things (IoT) networks that have source-limited equipment. In addition to reducing computational time and fronthaul traffic information, fog computing still poses challenges in allocating the existing computing and communication sources, while maintaining the strict quality of service (QoS) standards. The current study examines the issue regarding task management and heterogeneous resource allocation among equipment for wireless IoT networks. As a means of transferring the information to the fog computing nodes (FNs), IoT devices collecting large amounts of information require appropriate offloading decisions. This paper investigates how non-orthogonal multiple access (NOMA) can be deployed for IoT networks in order to enable many devices for transmitting information of the load and the market to a similar FN simultaneously, in a similar frequency range, and in the same code domain. Several IoT devices are optimized together based on their resource allocation and transmission power in this paper while meeting the QoS standards. Additionally, the optimization problem can be expressed in the form of a mixed-integer nonlinear programming problem with the goal of minimizing the system's power usage. The problem would be NP-hard so a whale optimization algorithm (WOA) has been proposed for solving. According to simulation outcomes, the suggested architecture performs well in terms of throughput, delays, blackout likelihood, and power usage.

Differentially Private Combinatorial Cloud Auction

Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time.

Blockchain-Based WDP Solution for Real-Time Heterogeneous Computing Resource Allocation

The utilization of cloud and edge computing has become one of the most prevailing resource supply mechanisms. Thousands of enterprise users and Internet of Things (IoT) devices have performed operations through services sold by computing resource providers. So far, the auction match is the main strategy for allocating resources, in which there exists a trusted third party playing a role as a broker to deal with resource allocation requests for both providers and consumers. The main concerns in resource allocation architecture are how and how long to solve the Winner Determination Problem (WDP), which is used to lay out the match outcome. It is not easy for a single broker with limited computing power to generate an optimal solution in a short period since its corresponding time complexity is regarded as the NP-hard problem. Meanwhile, it is hard for people to trust the third party thoroughly. That is, there are three potential issues, including centralization, data security, and untrustworthiness in traditional matching architecture. In particular, the real-time matching cannot be achieved to fulfill users who have urgent needs of computing resources. To solve above issues, we have designed a trustworthy and real-time decentralized computing resource allocation platform based on blockchain and smart contract. In order to optimize the allocation results, we improve the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for miners to reach the consensus mechanism. Experimental results and comparison analysis have demonstrated that potential defects could be addressed in the new method, and the real-time resource allocation can be preserved firmly despite the balance vibration of market supply and demand.

Energy-Aware Virtual Network Migration for Internet of Things Over Fiber Wireless Broadband Access Network

The virtualized fiber wireless broadband access networks (V-FiWi) paradigm, effectively embedding heterogeneous virtual networks (VNs) originated from the service provider (SP) into shared substrate network (SN) provided by infrastructure provider (InP), plays a tremendous role in meeting the differentiated requirements between wireless frontend subnetwork and fiber backhaul subnetwork to achieve the interoperability of heterogeneous resource allocation. However, most of the existing V-FiWi integration systems mainly focused on the virtual network request (VNR) acceptance ratio, InP revenue, and substrate resource utilization, and they ignored the crucial issue called higher energy consumption cost, which was resulted from the imbalanced consumption of the substrate resource between the arrival and departure of VNR. In this article, we devote to exploring the energy-aware virtual network migration (EA-VNM) problem over the FiWi access technology, aiming to reoptimize the energy consumption while maintaining the high InP revenue and the large substrate resource utilization. In response to this issue, we first represent the service-oriented V-FiWi broadband access network architecture from the perspective of computing, storage, and network resource constraints, in which a migration model consisting of migration node and migration time is explained in detail. Then, we propose an enhanced KM-based energy-aware node migration (EKM-ENM) algorithm to economize on more bandwidth resource. More specially, the EA-VNM technology consists of network topology attributes and global network resources-based node-ranking measurement (NRM) phase, maximum weight matching-based node migration phase, and energy-aware link migration phase via Dijkstra shortest path algorithm. Finally, a rather large number of simulations are analyzed and evaluated numerically. Simulation results suggest that the proposed EKM-ENM algorithm outperforms the traditional embedding algorithms in terms of saving energy cost, decreasing time complexity, and improving VNR acceptance ratio.

An Adaptive Memetic Algorithm for the Joint Allocation of Heterogeneous Stochastic Resources.

This article investigates the joint allocation problem of stochastic resources (JASRs), which is widely found in complex systems. A general mathematical model for joint allocation of multiple heterogeneous stochastic resources is built, capturing the interdependencies between resources, quantity constraints, capability constraints, and strategy constraints of resources. An adaptive memetic algorithm (MA) is proposed for JASR, and the multipermutation encoding method is developed to denote assignment schemes of different resources. Multiple permutation-based operators are employed in the mutation and local search process under the genetic evolution framework and learning framework, respectively. Besides, a hybrid initialization method and an adaptive replacement strategy are put forward. Moreover, a restart strategy is developed to rebuild the population to maintain the genetic diversity. Twenty-five random test instances are produced to validate the effectiveness of the proposed MA. The results of computational experiments and the Wilcoxon rank-sum test demonstrate that these JASR instances can be well handled by the proposed adaptive MA, and the proposed MA is able to provide remarkably better decision schemes for the majority of the test instances than the prevailing solution methods.

A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

Optimizing protection resource allocation for traffic-driven epidemic spreading.

Optimizing the allocation of protection resources to control the spreading process in networks is a central problem in public health and network security. In this paper, we propose a comprehensive adjustable resource allocation mechanism in which the over allocation of resources can be also numerically reflected and study the effects of this mechanism on traffic-driven epidemic spreading. We observe that an inappropriate resource allocation scheme can induce epidemic spreading, while an optimized heterogeneous resource allocation scheme can significantly suppress the outbreak of the epidemic. The phenomenon can be explained by the role of nodes induced by the heterogeneous network structure and traffic flow distribution. Theoretical analysis also gives an exact solution to the epidemic threshold and reveals the optimal allocation scheme. Compared to the uniform allocation scheme, the increase in traffic flow will aggravate the decline of the epidemic threshold for the heterogeneous resource allocation scheme. This indicates that the uneven resource allocation makes the network performance of suppressing epidemic degrade with the traffic load level. Finally, it is demonstrated that real-world network topology also confirms the results.

Heterogeneous Computation and Resource Allocation for Wireless Powered Federated Edge Learning Systems

Federated learning (FL) is a popular edge learning approach that utilizes local data and computing resources of network edge devices to train machine learning (ML) models while preserving users’ privacy. Nevertheless, performing efficient learning tasks on the devices and achieving longer battery life are primary challenges faced by federated learning. In this paper, we are the first to study the application of heterogeneous computing (HC) and wireless power transfer (WPT) to federated learning to address these challenges. Especially, we propose a heterogeneous computation and resource allocation framework based on a heterogeneous mobile architecture to achieve effective implementation of FL. To minimize the energy consumption of smart devices and maximize their harvesting energy simultaneously, we formulate an optimization problem featuring multidimensional control, which jointly considers time splitting for WPT, dataset size allocation, transmit power allocation and subcarrier assignment during communications, and processor frequency of processing units (central processing unit (CPU) and graphics processing unit (GPU)). However, the major obstacle is how to design a proper algorithm to solve this optimization problem efficiently. For this purpose, we decouple the optimization variables so as to achieve high efficiency in deriving its solution. Particularly, we first compute the optimal processor frequency and dataset size allocation via employing the Lagrangian dual method, followed by finding the closed-form solution to the optimal time splitting allocation, and finally attain the optimal subcarrier assignment as well as transmit power for transmissions through an iteration algorithm. To evaluate the performance of our proposed scheme, we set up four baseline schemes as comparison, and simulation results show that the proposed scheme converges quite fast and better enhance the energy efficiency of the wireless powered FL system compared with the baseline schemes.

An online fair resource allocation solution for fog computing

Fog computing is a complementary computing paradigm to the existing cloud computing. A fundamental problem of fog computing is how to allocate the computing resources of fog nodes when scheduling tasks that arrive in an online manner. Other than task completion speed metrics, fairness of resource allocation between competing users is also an important metric to consider. One such metric is Dominant Resource Fairness (DRF), a fairness scheme that guarantees four key qualities: incentivised sharing, strategy-proof, Pareto-efficiency, and envy free. This paper examines the multi-resource, multi-server, and heterogeneous task resource allocation problem from a DRF perspective. Four different types of tasks are considered: ordered/unordered and splittable/unsplittable. Three low complexity heuristics are proposed to maximise fairness between users. Results show that the proposed heuristics are at least comparable to three baseline scheduling algorithms in terms of task completion speed while achieving higher fairness between users.

Pyscreener: A Python Wrapper for Computational Docking Software

pyscreener is a Python library that seeks to alleviate the challenges of large-scale structure-based design using computational docking. It provides a simple and uniform interface that is agnostic to the backend docking engine with which to calculate the docking score of a given molecule in a specified active site. Additionally, pyscreener features first-class support for task distribution, allowing users to seamlessly scale their code from a local, multi-core setup to a large, heterogeneous resource allocation.

Joint Task Offloading, D2D Pairing, and Resource Allocation in Device-Enhanced MEC: A Potential Game Approach

The emergence of intelligent applications produces the demand for computing. How to reduce the computation pressure in mobile-edge computing (MEC) under massive computation demand is an urgent problem to solve. Specifically, the allocation of heterogeneous resources, including communication resources and computing resources, needs to be optimized simultaneously. From the perspective of joint optimization of channel allocation, device-to-device (D2D) pairing, and offloading mode, this article studies the multiuser computing task offloading problem in device-enhanced MEC. The objective is maximizing the aggregate offloading benefits, i.e., the tradeoff between delay and energy consumption, of all compute-intensive users in the network. By introducing game theory, the problem is modeled as a multiuser computation task offloading game, which is proved to be an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE) solution. In order to find a desirable solution, this article proposes a better reply-based distributed multiuser computation task offloading algorithm (BR-DMCTO). Simulation results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.

A Novel Mobility-Aware Offloading Management Scheme in Sustainable Multi-Access Edge Computing

The concept of Multi-access Edge Computing (MEC) extends the provisioning of computing and storage capabilities from remote Cloud Data Centers (DC) to the proximity of end users via heterogeneous networks. By augmenting User Equipment (UE) with external computing power under the local coverage, Cloudlet-based offloading performs as a critical enabler to boost application execution performance and to prolong battery lifespan in the mobile devices. However, the mobility of UEs introduces intra-Cloudlet intermittent connections and inter-Cloudlet unbalanced load distributions in the MEC environment, which consequently leads to offloading failures and service downgrading. In this paper, we propose a novel MEC-based mobility-aware offloading model to solve the intra-Cloudlet offloading scheduling issue and inter-Cloudlet load-aware heterogeneous resource allocation issue in terms of concerning the offloading execution efficiency, task processing time constraints, and energy efficiency. A priority-based queue model is designed to formulate the intra-Cloudlet mobility-aware offloading scheduling problem, resolved by the adoption of the Particle Swarm heuristic. The energy-aware inter-Cloudlet resource selection procedure is formalized in a mobility-aware multi-site resource allocation model, which is further solved by lightweight dynamic load balancing. The results of the experiment indicate that the proposed framework can effectively improve the overall offloading service provisioning quality in the intra-Cloudlet and inter-Cloudlet offloading scenarios, compared to the current works.