Online Resource Scheduling Research Articles

Stacked Autoencoder-Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the Internet of things (IoT) users, by optimizing offloading decision, transmission power and resource allocation in the large-scale mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) based solution is proposed, which includes the following components. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is applied to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks.

Interactive Context for Mobile OS Resource Management

This paper presents an application-transparent execution context for mobile operating systems that reflects the criticality of current execution on the user interactivity. We track Android / Linux system-level events including semantic syscalls, UI actions, and standard inter-process and intra-process communication interfaces that signal the initiation and propagation of interactivity-related executions. This interactive context enables new optimizations in CPU scheduling and power state management. We recognize that online resource scheduling is susceptible to inaccurate contexts—e.g., misidentifying an interactive execution as background leads to possible priority inversion by schedulers. In particular, low-level OS events such as kernel block / wake-up carry ambiguous semantics that could induce false context propagations. We selectively expose such uncertainties to the schedulers so that executions with uncertain contexts are prioritized behind interactive executions but otherwise run as fast as possible to minimize the priority inversion. We further recognize the importance of prompt propagation of the interactivity context at the earliest possible moment to facilitate immediate prioritization. We utilize the interactivity context to enable differential per-core frequency control and background workload consolidation. Experiments on a quad-core smartphone with a dozen mobile workload scenarios show that we can, on average, save 13 percent energy with only 1 percent degradation on the interactive performance.

A multi-objective trade-off framework for cloud resource scheduling based on the Deep Q-network algorithm

Cloud computing, now a mature computing service, provides an efficient and economical solution for processing big data. As such, it attracts a lot of attention in academia and plays an important role in industrial applications. With the recent increase in the scale of cloud computing data centers, and improvements in user service quality requirements, the structure of the whole cloud system has become more complex, which has also made the resource scheduling management of these systems more challenging. Thus, the goal of this research was to resolve the conflict between cloud service providers (CSPs) who aim to minimize energy costs and those who seek to optimize service quality. Based on the excellent environmental awareness and online adaptive decision-making ability of deep reinforcement learning (DRL), we proposed an online resource scheduling framework based on the Deep Q-network (DQN) algorithm. The framework could make a trade-off of the two optimization objectives of energy consumption and task makespan by adjusting the proportion of the reward of different optimization objectives. Experimental results showed that this framework could effectively be used to make a trade-off of the two optimization objectives of energy consumption and task makespan, and exhibited obvious optimization effects compared with the baseline algorithm. Therefore, our proposed framework can dynamically adjust the optimization objective of the system according to the different requirements of the cloud system.

Joint Stochastic Computational Resource and UAV Trajectory for Wireless-Powered Space-Air-Ground IoRT Networks

The space-air-ground (SAG) network boosts the application for the imperfect ground infrastructure in Internet of remote things (IoRT) networks. Considering the limited battery life of IoRT devices and the difficulty of replacement, unmanned aerial vehicle (UAV) is deployed in SAG networks to assist wireless power transmission (WPT) in order to achieve sustainable device operation and enhanced computational capability. In this article, a three-layer SAG network is proposed to serve IoRT devices. Given the intricate and unpredictable environment of the IoRT SAG network, the tasks need to be timely processed by the IoRT devices without prior knowledge, which remains an ongoing challenge on available resources management. Thus, an online resource scheduling scheme that jointly optimizes CPU cycle frequency, power control and UAV trajectory planning is developed. We aim to maximize the long-term time-averaged total system computation rate while satisfying network stability and sustainability. The studied problem is a nonlinear stochastic optimization problem, which is decoupled into three sub-problems by leveraging Lyapunov optimization. Furthermore, we propose an online algorithm, namely JCPUI, to obtain the optimal CPU cycle frequency, power control, and UAV trajectory planning. Besides, performance analysis is provided for the proposed JCPUI, which elaborates that the control parameter V affects the trade-off of the total system computation rate and system stability. Simulation results validate the theoretical analysis and demonstrate the effectiveness of JCPUI.

Resource Scheduling for Distributed Multi-Target Tracking in Netted Colocated MIMO Radar Systems

In this paper, an effective solution is proposed for joint beam and power scheduling (JBPS) in the netted Colocated MIMO (C-MIMO) radar systems for distributed multi-target tracking (MTT). At its core, the proposed solution includes a distributed fusion architecture that reduces the communication requirements while maintaining the overall robustness of the system. The distributed fusion architecture employs the covariance intersection (CI) fusion to address the unknown information correlations among radar nodes. Each C-MIMO radar node in the network can generate a time-varying number of beams with controllable transmitting power by waveform synthesis, thus is capable of accomplishing multiple tracking tasks simultaneously. To maximize the global MTT performance of the radar network, the proposed JBPS solution implements an online resource scheduling, regarding both the generated beams and the transmitted power of all radar nodes, based on the feedback of the MTT results. A scaled accuracy-based objective function is designed to quantify the global MTT performance while properly taking into account different target priorities on resource allocation. The Bayesian Cramer-Rao lower bound (BCRLB) for CI fusion rule is derived and utilized as the constituent of the objective function since it provides a lower bound on the accuracy of the target state estimates. As the formulated JBPS problem is non-convex, we propose a fast reward-based iterative descending approach to solve it effectively. Numerical results show that the proposed JBPS can deliver superior performance in terms of maximizing the overall MTT performance while possessing high flexibility on the resource allocation regarding different target priorities.

Intelligent Resource Scheduling for 5G Radio Access Network Slicing

It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next-generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for radio access network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements, and network dynamics (including user mobility and channel states), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of an iRSS is to exploit a collaborative learning framework that consists of deep learning (DL) in conjunction with reinforcement learning (RL). Specifically, DL is used to perform large time-scale resource allocation, whereas RL is used to perform online resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, an iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of an iRSS satisfies online resource scheduling requirements and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms.

StreamTune: dynamic resource scheduling approach for workload skew in video data center

Video surveillance applications need video data center to provide elastic virtual machine (VM) provisioning. However, the workloads of the VMs are hardly to be predicted for online video surveillance service. The unknown arrival workloads easily lead to workload skew among VMs. In this paper, we study how to balance the workload skew on online video surveillance system. First, we design the system framework for online surveillance service which consists of video capturing and analysis tasks. Second, we propose StreamTune, an online resource scheduling approach for workload balancing, to deal with irregular video analysis workload with the minimum number of VMs. We aim at timely balancing the workload skew on video analyzers without depending on any workload prediction method. Furthermore, we evaluate the performance of the proposed approach using a traffic surveillance application. The experimental results show that our approach is well adaptive to the variation of workload and achieves workload balance with less VMs.

Cuckoo: flexible compute‐intensive task offloading in mobile cloud computing

AbstractMobile cloud computing (MCC) is an emerging technology to facilitate complex application execution on mobile devices. Mobile users are motivated to implement various tasks using their mobile devices for great flexibility and portability. However, such advantages are challenged by the limited battery life of mobile devices. This paper presents Cuckoo, a scheme of flexible compute‐intensive task offloading in MCC for energy saving. Cuckoo seeks to balance the key design goals: maximize energy saving (technical feasibility) and minimize the impact on user experience with limited cost for offloading (realistic feasibility). Specifically, using a combination of static analysis and dynamic profiling, compute‐intensive tasks are fine‐grained marked from mobile application codes offline. According to the network transmission technologies supported in mobile devices and the runtime network conditions, adopting “task‐bundled” strategy online offloads these tasks to MCC. In the task‐hosted stage, we propose a skyline‐based online resource scheduling strategy to satisfy the realistic feasibility of MCC. In addition, we adopt resource reservation to reduce the extra energy consumption caused by the task multi‐offloading phenomenon. Further, we evaluate the performance of Cuckoo using real‐life data sets on our MCC testbed. Our extensive experiments demonstrate that Cuckoo is able to balance energy consumption and execution performance. Copyright © 2016 John Wiley & Sons, Ltd.

Online Resource Scheduling Under Concave Pricing for Cloud Computing

With the booming cloud computing industry, computational resources are readily and elastically available to the customers. In order to attract customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service providers offer several pricing strategies such as pay as you go, pay less per unit when you use more (so called volume discount), and pay even less when you reserve. The diverse pricing schemes among different IaaS service providers or even in the same provider form a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers’ resource requests, a cloud broker can fully take advantage of the discounts offered by cloud service providers. In this paper, we focus on how a broker can help a group of customers to fully utilize the volume discount pricing strategy offered by cloud service providers through cost-efficient online resource scheduling. We present a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower bound of its competitive ratio. Three special cases of the offline concave cost scheduling problem and the corresponding optimal algorithms are introduced. Our simulation shows that ROSA achieves a competitive ratio close to the theoretical lower bound under the special cases. Trace-driven simulation using Google cluster data demonstrates that ROSA is superior to the conventional online scheduling algorithms in terms of cost saving.

Online Task Scheduling for LiDAR Data Preprocessing on Hybrid GPU/CPU Devices: A Reinforcement Learning Approach

In recent years, general-purpose graphics processing units (GP-GPUs) have steadily risen in popularity for remote sensing data processing. Interest has been growing in using hybrid GPU/CPU architectures to realize the full potential of computing devices. This paper studies LiDAR data preprocessing, which is a typical data-intensive remote sensing application. It is proposed to develop an online task scheduler for hybrid GPU/CPU systems using reinforcement learning. At the core of the task scheduler is a Q-learning module that can create the optimal task execution path according to rewards accumulated over time. Constraints and preferences are also encapsulated in the scheduler to support automatic online resource scheduling. Quantitative evaluation on a typical LiDAR data set demonstrates the usefulness and potential of this online task scheduling approach for remote sensing applications.