Large-scale Resource Allocation Research Articles

Container Scaling Strategy Based on Reinforcement Learning

Elasticity capability is one of the most important capabilities of cloud computing, which combines large-scale resource allocation capability to quickly achieve minute-level resource demand provisioning to meet the elasticity requirements of different scale scenarios. The elasticity capability is mainly determined by the container start-up speed and container scaling strategy together, where the container scaling strategy contains both vertical container scaling strategy and horizontal container scaling strategy. In order to make the container scaling policy more effective and improve the application service quality and resource utilization, we briefly introduce Kubernetes’ horizontal pod autoscaling (HPA) strategy, analyze the existing problem of HPA, and develop a container scaling strategy based on reinforcement learning. First, we analyze the problems of Kubernetes’ existing HPA container autoscaling strategy in the scale-up and scale-down phases, respectively. Second, the Markov decision model is used to model the container scaling problem. Then, we propose a model-based reinforcement learning algorithm to solve the container scaling problem. Finally, we compare the experimental results of the HPA scaling strategy and the model-based reinforcement learning strategy with the results from the resource utilization of the application, the change of the number of pods, and the application response time; through the experimental analysis, we verify that the reinforcement learning-based container scaling strategy can guarantee the application service quality and improve the utilization of the application resources more effectively than the HPA strategy.

Two-Timescale Resource Management for Ultrareliable and Low-Latency Vehicular Communications

Ultra-reliable low-latency communication (URLLC) is essential for future vehicle-to-vehicle (V2V) networks to improve traffic safety and enhance driving experience. Due to the fast-varying channel caused by high mobility, guaranteeing latency and reliability performance of the V2V links is a tremendous challenge. In this paper, we propose a novel resource allocation framework to support ultra-reliable low-latency V2V communications. The proposed framework includes both large-scale and small-scale resource optimizations. The large-scale resource allocation is performed at the central base station based on large-scale channel information periodically collected from vehicles. On the other hand, the small-scale resource allocation is performed at the vehicles according to instantaneous channel and queuing information. We develop optimal solutions for both resource allocation problems. With the proposed optimal solutions, the latency performance at the occurrence of extreme events is enhanced by enabling spectrum sharing among the vehicles. Simulation results demonstrate that the proposed algorithm can effectively improve the URLLC performance compared against the benchmark algorithm.

An Exact Algorithm for Large-Scale Continuous Nonlinear Resource Allocation Problems with Minimax Regret Objectives

We study a large-scale resource allocation problem with a convex, separable, not necessarily differentiable objective function that includes uncertain parameters falling under an interval uncertainty set, considering a set of deterministic constraints. We devise an exact algorithm to solve the minimax regret formulation of this problem, which is NP-hard, and we show that the proposed Benders-type decomposition algorithm converges to an [Formula: see text]-optimal solution in finite time. We evaluate the performance of the proposed algorithm via an extensive computational study, and our results show that the proposed algorithm provides efficient solutions to large-scale problems, especially when the objective function is differentiable. Although the computation time takes longer for problems with nondifferentiable objective functions as expected, we show that good quality, near-optimal solutions can be achieved in shorter runtimes by using our exact approach. We also develop two heuristic approaches, which are partially based on our exact algorithm, and show that the merit of the proposed exact approach lies in both providing an [Formula: see text]-optimal solution and providing good quality near-optimal solutions by laying the foundation for efficient heuristic approaches.

Optimal Algorithm for Shared Network Communication Bandwidth in IoT Applications

In recent years, a variety of wired and wireless network communication protocols in the field of industrial control have become increasingly mature. The purpose of this paper is to provide a Shared network communication bandwidth optimization management algorithm for large-scale industrial networked control systems in Internet of things applications. This algorithm is based on the generalized geometric convex optimization method and can realize the optimal allocation of Shared network communication bandwidth resources. L2 networked control systems is used in this paper for the establishment of various numerical relations between the control performance and the communication network parameters. Based on the generalized geometric convex optimization method for the numerical relationship between convex analysis and fitting, convexity, and with the convex analysis and the numerical relationship between convexity fitting as constraint conditions, the results of integrity for networked control systems with large-scale resource allocation target will share the optimal management of network resources as a generalized geometric convex optimization problem. Using convex optimization software package for optimizing the optimal global solution of management problem, i. e. the optimal allocation of resources, the algorithm realizes the stability of each networked control system and achieve optimal L2 control performance. It is concluded that the predetermined transmission rate between the network node one and network node two, the data flow information sent by the network node two to the network node one is read, the delay time and packet loss rate between the two nodes are determined, the delay time is reduced by about 8 seconds, and the packet loss rate is greatly reduced by 78%.

Allocating resources for land protection using continuous optimization: biodiversity conservation in the United States.

Spatial optimization approaches that were originally developed to help conservation organizations determine protection decisions over small spatial scales are now used to inform global or continental scale priority setting. However, the different decision contexts involved in large-scale resource allocation need to be considered. We present a continuous optimization approach in which a decision-maker allocates funding to regional offices. Local decision-makers then use these funds to implement habitat protection efforts with varying effectiveness when evaluated in terms of the funder's goals. We illustrate this continuous formulation by examining the relative priority that should be given to different counties in the coterminous United States when acquiring land to establish new protected areas. If weighting all species equally, counties in the southwest United States, where large areas can be bought cheaply, are priorities for protection. If focusing only on species of conservation concern, priorities shift to locations rich in such species, particularly near expanding exurban areas facing high rates of future habitat conversion (e.g., south-central Texas). Priorities for protection are sensitive to what is assumed about local ecological and decision-making processes. For example, decision-makers who doubt the efficacy of local land protection efforts should focus on a few key areas, while optimistic decision-makers should disperse funding more widely. Efforts to inform large-scale conservation priorities should reflect better the types of choice that decision-makers actually face when working over these scales. They also need to report the sensitivity of recommended priorities to what are often unstated assumptions about local processes affecting conservation outcomes.

Heterogeneous Space and Terrestrial Integrated Networks for IoT: Architecture and Challenges

The number of IoT devices has been growing significantly, and mobile network traffic is also increasing explosively. In order to meet the human desire to explore unknown areas, edgeless communications have always been one of the directions for the development of wireless networks. H-STIN is a promising solution to face the problem of large bandwidth requirement, massive machine type communication, and edgeless communication requirements. However, it is difficult to integrate communication protocols, routing problems, and resource allocation in the large-scale and heterogeneous network architecture. Therefore, this article proposes a potential H-STIN architecture based on the development trends of the Internet of Things, mobile networks, and satellite networks. Since TN has adequate computation resource and routing architecture, the backbone network is given to support an entire network, and AS is adopted to achieve regional self-optimization. The SSTIS is composed of the perception layer, the cognition layer, and the intelligence layer. It integrates IoT, SDN, and network functions virtualization technologies to achieve self-monitoring, crisis forecasting, and optimal control. Finally, the promising technical challenge, including integrated route planning and large-scale resource allocation, is given.

Bayesian Exploration for Approximate Dynamic Programming

Approximate dynamic programming has been applied to solve large-scale resource allocation problems in many domains, including transportation, energy, and healthcare. The main algorithmic challenge in these applications is to accurately estimate the value of a resource in a certain state (for example, a battery that is half full, or a truck en route to a certain destination). This “value function” must be learned as part of the decision-making process, giving rise to the “exploration/exploitation” challenge: we may, and often should, choose to experiment with seemingly suboptimal actions because they have high potential to be better than we believe. In “Bayesian Exploration for Approximate Dynamic Programming,” Ilya O. Ryzhov, Martijn R. K. Mes, Warren B. Powell and Gerald van den Berg present a principled framework for modeling uncertainty about the value function and measuring the potential of a state to improve a decision-making policy. This method is scalable and performs well in experiments.

Data challenges in dynamic, large-scale resource allocation in remote regions

Resource allocation is difficult in large-scale systems in remote locations with sparse infrastructure and dynamic, demanding requirements, as can be the case in disaster management, emergency and oil spill response, and search and rescue operations. Resource allocation challenges are particularly acute in Arctic oil spill response, where the lack of infrastructure makes logistics challenging and heightens the need for comprehensive and thoughtful resource allocation models. This paper describes data challenges associated with the development of resource allocation models for dynamic network scheduling in support of Arctic oil spill response. A marine transportation traffic analysis and an Arctic resource allocation database, developed to support the resource allocation model development described, were developed using public and private information sources. This paper outlines the challenges associated with the data acquisition and analysis efforts, and provides recommendations for addressing the data challenges, which are similar to those in other complex, safety–critical resource allocation problem settings.

A New Hybrid Method for Large Scale Resource Allocation Problems

A New Hybrid Method for Large Scale Resource Allocation Problems