Dynamic Resource Allocation Mechanism Research Articles

An ML‐based task clustering and placement using hybrid Jaya‐gray wolf optimization in fog‐cloud ecosystem

SummaryThe rapid expansion of IoT systems has caused network congestion and delays in task placement and resource provisioning as usually the tasks are executed at a far location in the cloud. Fog computing reduces the computing burden of cloud data centers as well as the communication burden of the internet as fog resources are placed near the data generation points. Within Fog computing, an important challenge is the optimal task placement which is an NP‐class problem. This work applies machine learning for task clustering and addresses the task placement problem in a fog computing environment using a hybrid of two recent metaheuristics; Jaya and gray wolf optimization (GWO). The hybrid method considers optimizing the total number of active fog nodes, load balancing in fog nodes, and average response time of the tasks. The performance of the proposed method is evaluated on a real‐time LCG dataset and is compared with reinforcement learning fog scheduling (RLFS), genetic algorithm (GA), dynamic resource allocation mechanism (DRAM), load balancing and scheduling algorithm (LBSSA), and particle swarm optimization with simulated annealing (PSO‐SA) algorithms. The results demonstrate the superiority of the suggested method over the baseline techniques in terms of average improvement of 51.04% in load balance variance, 30.25% in average response time, 24.16% in execution time, and 47.10% in the number of devices used.

Dynamic Allocation Mechanism of Cloud Computing Resources Driven by Neural Network

With the popularization of cloud computing technology, the dynamic allocation mechanism of cloud computing resources has become an important research field to improve resource utilization and meet the needs of diversified workloads. The purpose of this study is to explore the dynamic allocation mechanism of cloud computing resources driven by neural network and introduce the powerful ability of deep learning into cloud computing environment. We put forward a comprehensive framework, which combines data collection, analysis, decision-making and implementation to realize intelligent resource allocation. These data will be used to train BP neural network (BPNN). In order to predict the bidding price, a BPNN is designed, which usually includes input layer, hidden layer and output layer. The number of nodes in the input layer is equal to the dimension of the input feature, and the number of nodes in the output layer is 1, which indicates the prediction of the bidding price. Through experiments and simulations, we verify the effectiveness of the dynamic resource allocation mechanism driven by neural network. The results show that this mechanism can better adapt to the changing workload requirements, improve resource utilization and reduce resource waste. In addition, it provides better performance and user experience, thus enhancing the competitiveness of cloud computing systems.

A Distributed Underwater Multi-Target Tracking Algorithm Based on Two-Layer Particle Filter

Underwater multi-target tracking is one of the key technologies for military missions, including patrol and combat in the crucial area. Since the underwater environment is complex and targets’ trajectories may intersect when they are in a dense area, it is challenging to guarantee the precision of observed information. In order to provide high-precision underwater localization and tracking services over an underwater monitoring network, a dynamic network resource allocation mechanism and an underwater multi-target tracking algorithm based on a two-layer particle filter with distributed probability fusion (TLPF-DPF) are proposed. The position estimation model based on geometric constraints and the dynamic allocation mechanism of network resources based on prior position estimation are designed. Using the improved filtering algorithm with known initial states, the reliable tracking of multiple targets with trajectory intersection in a small area under complex noises is achieved. In the non-Gaussian environment, the average positioning error of TLPF-DPF is less by nearly 30% than alternative algorithms. When switching from a Gaussian environment to a non-Gaussian environment, the performance degradation of TLPF-DPF is less than 12%, which exhibits stability compared with other algorithms when targets are close to each other with crossing trajectories.

Dynamic Virtual Resource Allocation Mechanism for Survivable Services in Emerging NFV-Enabled Vehicular Networks

Vehicular ad-hoc network (VANET) is an emerging aspect of the 5G vertical application. Network function virtualization (NFV) is the key enabling technology of 5G and beyond 5G (B5G) networks. In NFV-enabled vehicular and 5G networks, all underlying nodes (e.g. vehicular, edge, core) can be completely virtualized and easy to be managed and allocated. Network service providers can implement each dynamically requested virtual network service (VNS), having arbitrary topology and customized resource demands, on top of the NFV-enabled networks. However, network elements (e.g. nodes and links) may come into failures accidentally. Consequently, it will lead to the performance degradation of implemented VNSs that run on top of the failed network elements. It is vital to guarantee the survivable services even though the network elements fail accidentally. Therefore, we propose the dynamic virtual resource allocation mechanism in this paper. Firstly, we introduce the business model and formulate the dynamic virtual resource allocation in NFV-enabled networks. Secondly, we detail all modules of our proposed mechanism. Especially, the initial resource allocation and re-allocation modules of achieving the survivable network services are detailed. Finally, we execute the comprehensive simulations by comparing with the typical virtual resource allocation mechanisms. The simulation results are discussed so as to highlight the merits of the proposed mechanism.

Robust Dynamic CPU Resource Provisioning in Virtualized Servers

We present robust dynamic resource allocation mechanisms to allocate application resources meeting Service Level Objectives (SLOs) agreed between cloud providers and customers. In fact, two filter-based robust controllers, i.e., <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> filter and Maximum Correntropy Criterion Kalman filter (MCC-KF), are proposed. The controllers are self-adaptive, with process noise variances and covariances calculated using previous measurements within a time window. In the allocation process, a bounded client mean response time ( <inline-formula><tex-math notation="LaTeX">$\mathop {\mathrm{mRT}}$</tex-math></inline-formula> ) is maintained. Both controllers are deployed and evaluated on an experimental testbed hosting the RUBiS (Rice University Bidding System) auction benchmark web site. The proposed controllers offer improved performance under abrupt workload changes, shown via rigorous comparison with current state-of-the-art. On our experimental setup, the Single-Input-Single-Output (SISO) controllers can operate on the same server where the resource allocation is performed; while Multi-Input-Multi-Output (MIMO) controllers are on a separate server where all the data are collected for decision making. SISO controllers take decisions not dependent to other system states (servers), albeit MIMO controllers are characterized by increased communication overhead and potential delays. While SISO controllers offer improved performance over MIMO ones, the latter enable a more informed decision making framework for resource allocation problem of multi-tier applications.

Disrupted population coding in the prefrontal cortex underlies pain aversion.

SUMMARYThe prefrontal cortex (PFC) regulates a wide range of sensory experiences. Chronic pain is known to impair normal neural response, leading to enhanced aversion. However, it remains unknown how nociceptive responses in the cortex are processed at the population level and whether such processes are disrupted by chronic pain. Using in vivo endoscopic calcium imaging, we identify increased population activity in response to noxious stimuli and stable patterns of functional connectivity among neurons in the prelimbic (PL) PFC from freely behaving rats. Inflammatory pain disrupts functional connectivity of PFC neurons and reduces the overall nociceptive response. Interestingly, ketamine, a well-known neuromodulator, restores the functional connectivity among PL-PFC neurons in the inflammatory pain model to produce anti-aversive effects. These results suggest a dynamic resource allocation mechanism in the prefrontal representations of pain and indicate that population activity in the PFC critically regulates pain and serves as an important therapeutic target.

Dynamic Resource Allocation with Cost Externality

The inter-temporal resource allocation efficiency of a property rights-based common-pool resource system is challenged by a cost externality when one user’s extraction raises the extraction cost for others. This paper builds a dynamic resource allocation model to illustrate the efficiency loss from a standard property rights market. We then create a novel inter-temporal allocation mechanism that preserves dynamic efficiency. Our dynamic resource allocation mechanism includes an optimal planning stage where the agents collectively determine a binding extraction target for each period and a market stage where agents can exchange their extraction rights assigned within each period. The theoretical model demonstrates that our mechanism can achieve the socially optimal allocation in two specific environments. A numerical simulation of our mechanism for a general environment consistently tracks the social optimum and significantly outperforms the traditional property rights market.

VM Scaling and Load Balancing via Cost Optimal MDP Solution

Dynamic resource allocation mechanism is an essential building block in contemporary cloud computing environment, enabling the support of the large variability of incoming requests from an enormous number of applications utilizing such cloud infrastructure. In this article, we devise a dynamic resource allocation mechanism that optimizes the application’s profit under the set of costs and revenues while maintaining performance constraints. Specifically, we devise a decision-maker (DM) agent which formulates the joint admission control, scaling and load balancing problem as a stochastic process solvable by Markov decision process (MDP) which provides the optimal policy. Accordingly, at each time instance, the DM can determine based on the system’s current state, on the set of requirements and on the set of costs, whether to add or release a VM (scale-out or scale-in, respectively), whether to admit or reject an upcoming task and if admitting it, which VM to allocate it to. We explore the value function structure and provide insights with respect to the optimal policies produced from it. To address scalability issues of the detailed MDP solution we provide an alternative solution by abstract MDP which consolidates multiple system states into a single abstract state, hence can cope with much larger systems at the expense of slight performance degradation. To demonstrate the feasibility of the suggested scheme, we designed and implemented it, alongside with two traditional auto-scalers, on the Amazon Web Services (AWS) infrastructure. We ran numerous MATLAB simulations and AWS-based experiments which provided insights and demonstrated superiority against the traditional policies we compared with.

Data Driven Resource Allocation for NFV-Based Internet of Things

Network functions virtualization (NFV) architecture enables quick and cost-effective response of mobile network operators to various Internet-of-Things (IoT) applications, where the crux is to effectively and efficiently allocate resources to virtual network functions (VNFs). However, a systematical approach for resource allocation in virtualized mobile core network is still unavailable. In this paper, we propose a synthetic approach based on analysis of both network processing procedures and users’ behaviors. Inspired by the static user behavior model adopted by equipment manufacturers’ load test procedures, we construct a more practical user behavior model by analyzing over $20TB$ real data from an operator. With the model, we propose a matrix mapping-based dynamic resource allocation mechanism for the virtualized mobile core networks. To demonstrate the effectiveness of our approach, we conduct experiments using application and signaling records of millions of real users. Results show that the new approach significantly increases the resource utilization and system capacity of the mobile core networks.

DPRA: Dynamic Power-Saving Resource Allocation for Cloud Data Center Using Particle Swarm Optimization

Cloud computing provides the scalable computation capability based on a virtualization technique. The energy conservation for green computing is one of the vital issues while allocating resources. To improve energy efficiency, the dynamic power-saving resource allocation (DPRA) mechanism based on a particle swarm optimization algorithm is proposed. The DPRA mechanism not only considers the energy consumption of physical machine (PM) and virtual machine (VM) but also newly tackles the energy efficiency ratio of air conditioner. Moreover, the least squares regression method is utilized to forecast PM's resource utilization for allocating VM and eliminating VM migrations. In simulation, the proposed DPRA mechanism is compared with three familiar allocation schemes and one previous solution. Simulation results show that in the presence of VM number, DPRA outperforms traditional schemes and previous solution in terms of total energy consumption (includes PMs and air conditioners), total electric bill, VM migration, and the number of shutdown PMs, chosen as objective performance metrics.

A Collaborative DDoS Defence Framework Using Network Function Virtualization

High-profile and often destructive distributed denial of service (DDoS) attacks continue to be one of the top security concerns as the DDoS attacks volumes are increasing constantly. Among them, the SYN Flood attack is the most common type. Conventional DDoS defense solutions may not be preferable, since they demand highly capable hardware resources, which induce high cost and long deployment cycle. The emerging of network function virtualization (NFV) technology introduces new opportunities to decrease the amount of proprietary hardware that is needed to launch and operate network services. In this paper, we propose a DDoS defense mechanism named CoFence, which facilitates a “domain-helps-domain” collaboration network among NFV-based domain networks. CoFence allows domain networks to help each other in handling large volume of DDoS attacks through resource sharing. Specifically, we design a dynamic resource allocation mechanism for domains so that the resource allocation is fair, efficient, and incentive-compatible. The resource sharing mechanism is modeled as a multi-leader-follower Stackelberg game. In this game, all domains have a degree of control to maximize their own utility. The resource supplier domains determine the amount of resource to each requesting peer based on optimizing a reciprocal-based utility function. On the other hand, the resource requesting domains decide the level of demand to send to the resource supplier domains in order to reach sufficient support. Our simulation results demonstrate that the designed resource allocation game is effective, incentive compatible, fair, and reciprocal under its Nash equilibrium.

A Quantum Optimization Model for Dynamic Resource Allocation in Cloud Computing

Quantum Computing and Cloud Computing technologieshave potential capability to change the dynamic of futurecomputing. Similarly, both Complexities, time and Space are the basicconstraints which can determine the efficient cloud service performance.Quantum optimization for the cloud resources in dynamic environmentprovides a way to deal with the present classical cloud computationmodel’s challenges. By combining the fields of quantum computing andcloud computing, will result in evolutionary technology. Virtual resourceallocation is a major challenge facing cloud computing with dynamiccharacteristics, a single aspect for the evaluation of resource allocationstrategy cannot satisfy the real world demands in this case. QuantumOptimization resource allocation mechanism on the cloud computingenvironment based two-way factors, improving user satisfaction and bestuse of resource utilization of cloud computing systems.A dynamic resource allocation mechanism for cloud services, based onnegotiation by keeping the focus on preferences and pricing factor istherefore proposed.

Constrained multi-objective optimization using constrained non-dominated sorting combined with an improved hybrid multi-objective evolutionary algorithm

ABSTRACTConstrained multi-objective optimization problems (cMOPs) are complex because the optimizer should balance not only between exploration and exploitation, but also between feasibility and optimality. This article suggests a parameter-free constraint handling approach called constrained non-dominated sorting (CNS). In CNS, each solution in a population is assigned a constrained non-dominated rank based on its constraint violation degree and Pareto rank. An improved hybrid multi-objective optimization algorithm called cMOEA/H for solving cMOPs is proposed. Additionally, a dynamic resource allocation mechanism is adopted by cMOEA/H to spare more computational efforts for those relatively hard sub-problems. cMOEA/H is first compared with the baseline algorithm using an existing constraint handling mechanism, verifying the advantages of the proposed constraint handling mechanism. Then cMOEA/H is compared with some classic constrained multi-objective optimizers, experimental results indicating that cMOEA/H could be a competitive alternative for solving cMOPs. Finally, the characteristics of cMOEA/H are studied.

Context-Aware Dynamic Resource Allocation for Cellular M2M Communications

While the huge number of machine-to-machine (M2M) devices connects to the LTE system, the bursty random access attempts from them are potential to cause severe random access collisions and degrade the successful probability as well as delay of network connection establishment. To address this issue, current LTE spec has specified access class barring (ACB) and enhanced access barring (EAB), but both methods lack a contention detection method to decide when to activate it as well as a proper way to dynamically adjust the parameter. In this work, we first proposed a contention detection method that can work with current long-term evolution (LTE) standard and is easy to be implemented. To further improve the random access performance in dynamic world, we proposed a context-aware dynamic resource allocation (CADRA) mechanism, which is a two-phase method to resolve random access contention: Phase I for the estimation of random access attempts, and Phase II for resource allocation. By CADRA, high resource efficiency and low random access delay can be achieved without prior knowledge about random access arrival traffic, and thus this approach is competent in diverse applications and scenarios of Internet of Things (IoT). Simulation results show that the proposed contention detection method works great with ACB and EAB. Our proposed CADRA has good performance in resource efficiency and delay while satisfying success probability.

A Dynamic Pricing Reverse Auction-Based Resource Allocation Mechanism in Cloud Workflow Systems

Market-oriented reverse auction is an efficient and cost-effective method for resource allocation in cloud workflow systems since it can dynamically allocate resources depending on the supply-demand relationship of the cloud market. However, during the auction the price of cloud resource is usually fixed, and the current resource allocation mechanisms cannot adapt to the changeable market properly which results in the low efficiency of resource utilization. To address such a problem, a dynamic pricing reverse auction-based resource allocation mechanism is proposed. During the auction, resource providers can change prices according to the trading situation so that our novel mechanism can increase the chances of making a deal and improve efficiency of resource utilization. In addition, resource providers can improve their competitiveness in the market by lowering prices, and thus users can obtain cheaper resources in shorter time which would decrease monetary cost and completion time for workflow execution. Experiments with different situations and problem sizes are conducted for dynamic pricing-based allocation mechanism (DPAM) on resource utilization and the measurement of Time⁎Cost (TC). The results show that our DPAM can outperform its representative in resource utilization, monetary cost, and completion time and also obtain the optimal price reduction rates.

Improve Constrained Recourse Fairness in Cloud Computing with Bottleneck-Aware Allocation

Fair resource is a key building block of any shared computing system. Recently fair division theory has emerged as a promising approach for the allocation of multiple computational resources among agents. Recent research has discussed efficiency and fairness requirements and identified a number if desirable scheduling objectives including so-called dominant resource fairness(DRF). DRF is not good between fair and resource utilization. A new allocation model Balancing Fairness and Efficiency with BottleneckAware Allocation(BAA) find good appropriate balance between fairness to the clients and maximizing system utilization. But BAA without taking into account the weight value and maximum number of tasks which are users need to run. We propose the IBAA fair allocation mechanism. IBAA has lots of good properties, it satisfies DSI, PE and EF. We construct IBAA mechanisms that provably satisfy properties, and analyze the performance. We believe that our work informs the design of superior multiusers system, and at the same time expands the scope of fair division theory by initiating the study of dynamic and fair resource allocation mechanisms.

Dynamic resource allocation based on service time prediction for device‐to‐device communication underlaying cellular networks

This work studies the dynamic resource allocation for device-to-device communication and proposes a service time prediction-based dynamic resource allocation mechanism for a cellular system. The proposed allocation mechanism introduces the concept of ‘utility’ as a metric to reflect the quality of experience of users with different service requirements, and takes into account service time prediction in spectral resource allocation. Specifically, it enables a base station to predict the service time of a new user and the remaining service time of existing users in the system, and takes into account the predicted service time and remaining service time in calculating the effective utility gain caused by the arrival of a new user. Based on the calculated effective utility gain, the base station will select the physical resource block (PRB) with the largest effective utility gain every time it allocates a PRB to the new user. Simulation results show that the proposed dynamic allocation mechanism can significantly improve the system performance in terms of the overall average utility and the number of users successfully admitted to the system.

An Auction Based Negotiation Protocol for Resource Allocation in Customized Housing Construction

Scheduling has long been a challenge to housing construction industry. It is critical to find a dynamic resource allocation mechanism that can fit into the intrinsically distributed subcontracting project and react to highly dynamic real-time event from construction site. An agent based scheduling mechanism is proposed at operation level to address the distributed decision making structure in housing construction context. An auction based negotiation protocol is presented to facilitate coordination amon g distributed scheduling agents. The proposed mechanism has been evaluated favorably in terms of load balancing and resource utilization under dynamically changing environment. Video abstract [Display omitted]

No Agent Left Behind: Dynamic Fair Division of Multiple Resources

Recently fair division theory has emerged as a promising approach for allocation of multiple computational resources among agents. While in reality agents are not all present in the system simultaneously, previous work has studied static settings where all relevant information is known upfront. Our goal is to better understand the dynamic setting. On the conceptual level, we develop a dynamic model of fair division, and propose desirable axiomatic properties for dynamic resource allocation mechanisms. On the technical level, we construct two novel mechanisms that provably satisfy some of these properties, and analyze their performance using real data. We believe that our work informs the design of superior multiagent systems, and at the same time expands the scope of fair division theory by initiating the study of dynamic and fair resource allocation mechanisms.

Dynamic resource allocation in fuzzy coalitions: a game theoretic model

We introduce an efficient and dynamic resource allocation mechanism within the framework of a cooperative game with fuzzy coalitions (cooperative fuzzy game). A fuzzy coalition in a resource allocation problem can be so defined that membership grades of the players in it are proportional to the fractions of their total resources. We call any distribution of the resources possessed by the players, among a prescribed number of coalitions, a fuzzy coalition structure and every membership grade (equivalently fraction of the total resources), a resource investment. It is shown that this resource investment is influenced by the satisfaction of the players in regard to better performance under a cooperative setup. Our model is based on the real life situations, where possibly one or more players compromise on their resource investments in order to help forming coalitions.