Scheduling Of Distributed Resources Research Articles

Global Resource Scheduling for Distributed Edge Computing

Recently, there has been a surge in interest surrounding the field of distributed edge computing resource scheduling. Notably, applications like intelligent traffic systems and Internet of Things (IoT) intelligent monitoring necessitate the effective scheduling and migration of distributed resources. In addressing this challenge, distributed resource scheduling must weigh the costs associated with resource scheduling, aiming to identify an optimal strategy amid various feasible solutions. Different application scenarios introduce diverse optimization objectives, including considerations such as cost, transmission delay, and energy consumption. While current research predominantly focuses on the optimization problem of local resource scheduling, there is a recognized need for increased attention to global resource scheduling. This paper contributes to the field by defining a global resource scheduling problem for distributed edge computing, demonstrating its NP-Hard nature through proof. To tackle this complex problem, the paper proposes a heuristic solution strategy based on the ant colony algorithm (ACO), with optimization of ACO parameters achieved through the use of particle swarm optimization (PSO). To assess the effectiveness of the proposed algorithm, an experimental comparative analysis is conducted. The results showcase the algorithm’s notable accuracy and efficient iteration cost performance, highlighting its potential applicability and benefits in the realm of distributed edge computing resource scheduling.

Distributed resource scheduling in edge computing: Problems, solutions, and opportunities

Edge computing has become popular in the last decade and will advance in future to support real-time actionable analytics at the devices. One of the fundamental problems for future edge computing is to make distributed resource scheduling (DRS) decisions both at the end devices and edge devices to support requirements including autonomous computation, scalability, low-latency, etc. Several surveys in the literature on edge computing have considered some aspects related to DRS, such as challenges and solution approaches, particularly for computation offloading and data management. However, to the best of our knowledge, there is no comprehensive survey on DRS in edge computing. This paper surveys the challenging issues, motivations, and existing works for enabling DRS in edge computing. We define and identify the unique issues for DRS in edge computing compared to traditional works on parallel and distributed systems. The motivations for DRS in edge computing have been described by pointing out the benefits and emerging application scenarios. This paper also provides a taxonomy to classify the existing works from three perspectives, i.e., systems, problems, and solution approaches. Finally, we have outlined several future directions that can help researchers to advance the state-of-the-art.

Blockchain-Based Hardware-in-the-Loop Simulation of a Decentralized Controller for Local Energy Communities

The development of local energy communities observed in the last years requires the reorganization of energy consumption and production. In these newly considered energy systems, the commercial and technical decision processes should be decentralized in order to reduce their maintenance costs. This will be allowed by the progressive spreading of IoT systems capable of interacting with distributed energy resources, giving local sources the ability to be optimally coordinated in terms of network and energy management. In this context, this paper presents a decentralized controlling architecture that performs a wide spectrum of power system optimization procedures oriented to the local market management. The controller framework is based on a decentralized genetic algorithm. The manuscript describes the structure of the tool and its validation, considering an automated distributed resource scheduling for local energy markets. The simulation platform permits implementing the blockchain-based trading process and the automated distributed resource scheduling. The effectiveness of the tool proposed is discussed with a hardware-in-the-loop case study.

Distributed Resource Scheduling for Large-Scale MEC Systems: A Multiagent Ensemble Deep Reinforcement Learning With Imitation Acceleration

In large-scale mobile edge computing (MEC) systems, the task latency, and energy consumption are important for massive resource-consuming and delay-sensitive Internet of Things Devices (IoTDs). Against this background, we propose a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task latency and energy consumption for all IoTDs, which can be formulated as a mixed-integer nonlinear programming. The DIRS framework includes centralized training relying on the global information and distributed decision making by each agent deployed in each MEC server. Specifically, we first introduce a novel multiagent ensemble-assisted distributed deep reinforcement learning (DRL) architecture, which can simplify the overall neural network structure of each agent by partitioning the state space and also improve the performance of a single agent by combining decisions of all the agents. Second, we apply action refinement to enhance the exploration ability of the proposed DIRS framework, where the near-optimal state-action pairs are obtained by a novel Levy flight search. Finally, an imitation acceleration scheme is presented to pretrain all the agents, which can significantly accelerate the learning process of the proposed framework through learning the professional experience from a small amount of demonstration data. The simulation results in three typical scenarios demonstrate that the proposed DIRS framework is efficient and outperforms the existing benchmark schemes.

Research on the Application of Computer Cloud Computing Technology in Laboratory Management Based on Docker Platform

The balance of experimental resources needs the help of distributed resource scheduling algorithm to integrate the discrete resources logically, and plan the scheduling and application uniformly, so as to ameliorate the utilization efficiency of resources, which provides a broad space for the application of Docker tech. Based on this, this paper first analyses the concept and connotation of Docker container platform, then studies the construction of laboratory cloud platform based on Docker platform, and finally gives the utilization effect of laboratory management cloud platform based on Docker tech.

Multiagent Task Planning Based on Distributed Resource Scheduling under Command and Control Structure

For task planning of the command and control structure, the existing algorithms exhibit low efficiency and poor replanning quality under abnormal conditions. Given the requirements of the current accusation architecture, a distributed command and control structure model is built in this paper based on multiagents, which exploits the superiority of multiagents in achieving complex tasks. The concept of MultiAgent-HTN is proposed based on the framework. The original hierarchical task network planning algorithm is optimized, the multiagent collaboration framework is redefined, and the coordination mechanism of local conflict is developed. With the classical resource scheduling problem as the experimental background, the proposed algorithm compared with the classical HTN algorithm is drawn. According to the experimental results, the proposed algorithm exhibits higher quality and higher efficiency than the existing algorithm and the space anomaly is significant in the course of processing. The planning is more efficient and the time is more complicated and superior in solving the same problem, and the algorithm exhibits good convergence and adaptability. In the conclusion, it is proved that the distributed command and control structure proposed in this paper exhibits high practicability in relevant fields and can solve the problem of distributed command and control structure in a multiagent scenario.

Distributed tasks-platforms scheduling method to holonic-C2 organization

To solve the problem of distributed tasks-platforms scheduling in holonic command and control (C2) organization, the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality, a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally, a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.

A Key-Value Store Based Data Center Resource Scheduling System

Efficiently scheduling resources in large scale data center is a key problem that distributed resource management systems face. In the cloud computing environment, the quantity of resources expand and the size of users raises dramatically. It brought up following challenges such as accessing to vast amount of resource information, handling of highly concurrent user requests, tremendous pressure of the system brought up by the update of the mass resources and so on. Traditional resource management systems based on centralized or hierarchical structure have pool expansibility and can’t satisfy the new large scale applications. And existing distributed resource scheduling methods (such as P2P routing based and DHTs based resource scheduling methods) can’t process user requests with high concurrency rate and high-frequency resource updates well enough. We propose a resource schedule model based on Key-Value Store. The model solves the problem of storing mass resources and efficiently accessing resource information using Key-Value Store. Distributed resource scheduling method based on range-partition can locate the appropriate resources rapidly and also reduce the cost of resource updates by extended invalid push protocol. The evaluation on data from Planetlab shows that, compared to current, and to improve the scheduling efficiency.

Distributed Resource Scheduling Based on Potential Game in Dense Cellular Network

Based on a potential game model, a distributed resource scheduling scheme is proposed to coordinate severe interference in a dense cellular network. First, the subcarrier and power scheduling problem is modeled as a non-cooperative potential game. Then, individual utility and overall potential functions are proposed by considering attainable throughput and consumed power. According to the definition of the potential game, the difference in individual utilities caused by strategy changing has the same value as the difference in overall potential value, which guarantees that players can maximize respective utility without worsening system profit. Afterward, an iterative searching algorithm is designed to obtain optimal resource scheduling strategy. Theoretical analysis and simulation results both validate that the proposed scheduling scheme makes a performance improvement in spectral and resource efficiency. Meanwhile, the proposed scheme can also guarantee the quality of service by providing superior user signal to interference plus noise ratio.

Distributed resource scheduling in smart grid with electric vehicle deployment using fireworks algorithm

Global warming and climate change are two key probing issues in the present context. The electricity sector and transportation sector are two principle entities propelling both these issues. Emissions from these two sectors can be offset by switching to greener ways of transportation through the electric vehicle (EV) and renewable energy technologies (RET). Thus, effective scheduling of both resources holds the key to sustainable practice. This paper presents a scheduling scenario-based approach in the smart grid. Problem formulation with dual objective function including both emissions and cost is developed for conventional unit commitment with EV and RET deployment. In this work, the scheduling and commitment problem is solved using the fireworks algorithm which mimics explosion of fireworks in the sky to define search space and the distance between associated sparks to evaluate global minimum. Further, binary coded fireworks algorithm is developed for the proposed scheduling problem in the smart grid. Thereafter, possible scenarios in conventional as well as smart grid are put forward. Following that, the proposed methodology is simulated using a test system with thermal generators.

Analysis of virtualized cloud server together with shared storage and estimation of consolidation ratio and TCO/ROI

Purpose – The physical server transition to virtualized infrastructure server have encountered crucial problems such as server consolidation, virtual machine (VM) performance, workload density, total cost of ownership (TCO), and return on investments (ROIs). In order to solve the problems mentioned above, the purpose of this paper is to perform the analysis of virtualized cloud server together with shared storage as well as the estimation of consolidation ratio and TCO/ROI in server virtualization. Design/methodology/approach – This paper introduces five distinct virtualized cloud computing servers (VCCSs), and provides the appropriate assessment to five well-known hypervisors built in VCCSs. The methodology the authors proposed in this paper will gives people an insight into the problem of physical server transition to virtualized infrastructure server. Findings – As a matter of fact, VM performance seems almost to achieve the same level, but the estimation of VM density and TCO/ROI are totally different among hypervisors. As a result, the authors have the recommendation to choose the hypervisor ESX server if you need a scheme with higher ROI and lower TCO. Alternatively, Proxmox VE would be the second choice if you like to save the initial investment at first and own a pretty well management interface at console. Research limitations/implications – In the performance analysis, instead of ESX 5.0, the authors adopted ESXi 5.0 that is free software, its function is limited, and does not have the full functionality of ESX server, such as: distributed resource scheduling, high availability, consolidated backup, fault tolerance, and disaster recovery. Moreover, this paper do not discuss the security problem on VCCS which is related to access control and cryptograph in VMs to be explored in the further work. Practical implications – In the process of virtualizing the network, ESX/ESXi has restrictions on the brand of the physical network card, only certain network cards can be detected by the VM. For instance: Intel and Broadcom network cards. The newer versions of ESXi 5.0.0 and above now support parts of Realtek series (Realtek 8186, Realtek 8169, and Realtek 8111E). Originality/value – How to precisely assess the hypervisor for server/desktop virtualization is also of hard question needed to deal with it crisply before deploying new IT with VCCS on site. The authors have utilized the VMware calculator and developed an approach to server/desktop consolidation, virtualization performance, VM density, TCO, and ROIs. As a result, in this paper the authors conducted a comprehensive approach to analyze five well-known hypervisors and will give the recommendation for IT manager to choose a right solution for server virtualization.

Study on CDIO Talents Training System Based on the Internet of Things and Cloud Computing in Big Data Era

Since CDIO talents training informatization system is employed in Big Data era, this paper shows some studies on the application of the internet of things , cloud computing, Big Data service and other advanced technologies in CDIO talents training informatization system, an informatization framework for the internet of things and cloud computing is constructed based on CDIO talents training, in order to solve the difficulties in managing and monitoring the fully distributed architecture, a distributed resource scheduling mechanism with QoS is proposed.

Virtual machine file system

The Virtual Machine File System (VMFS) is a scalable and high performance symmetric clustered file system for hosting virtual machines (VMs) on shared block storage. It implements a clustered locking protocol exclusively using storage links, and does not require network-based inter-node communication between hosts participating in a VMFS cluster. VMFS layout and IO algorithms are optimized towards providing raw device speed IO throughput to VMs. An adaptive IO mechanism masks errors on the physical fabric using contextual information from the fabric. The VMFS lock service forms the basis of VMware's clustered applications such as vMotion, Storage vMotion, Distributed Resource Scheduling, High Availability, and Fault Tolerance. Virtual machine metadata is serialized to files and VMFS provides a POSIX interface for cluster-safe virtual machine management operations. It also contains a pipelined data mover for bulk data initialization and movement. In recent years, VMFS has inspired changes to diskarray firmware and the SCSI protocol. These changes enable the file system to implement a hardware accelerated data mover and lock manager, among other things. In this paper, we present the VMFS architecture and its evolution over the years

Distributed resource scheduling in not-aligned optical cell switching

Most all-optical switching paradigms assume that different wavelengths are switched independently, which limits scalability. In optical cell switching (OCS), time is divided into time slots of fixed size by time-division multiplexing, and the wavelengths in a time slot are all bundled. Thus, each OCS switch (OCX) has a single switching plane and performs mere time-space switching. In OCS, each OCX requires optical slot synchronizers (OSYNs) at all inputs for the arrival slots to be aligned, so that cells can be simultaneously forwarded. In a recent OCS paradigm -not-aligned OCS-, the OSYNs and the alignment process are no longer required. Cell shifting still takes place inside the OCXs for minimizing the gaps between cells, but it is not necessary to align them to a reference time. Not-aligned OCS has clear advantages over aligned OCS: the total number of fiber delay loops (FDLs) and the hardware cost are reduced, and the number of switching operations is also lower. Moreover, cell arrival time to the switch is not critical, and the network becomes simpler and more flexible. In this paper, we propose a new distributed resource scheduling algorithm for not-aligned OCS networks, which takes connection blocking probability to reasonable values for practical loads.

Application-adaptive resource scheduling in a computational grid

Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly. This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler. In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm, to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications, only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the same. Experimental results are presented to demonstrate the adaptability of the HGSA.

On combinatorial auction and Lagrangean relaxation for distributed resource scheduling

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In ihis study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a first step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four different versions of the auction mechanism using job shop scheduling problems.

On combinatorial auction and Lagrangean relaxation for distributed resource scheduling

Most existing methods for scheduling are based on centralized or hierarchical decision making using monolithic models. In this study, we investigate a new method based on a distributed and locally autonomous decision structure using the notion of combinatorial auction. In combinatorial auction the bidders demand a combination of dependent objects with a single bid. We show that not only can we use this auction mechanism to handle complex resource scheduling problems, but there exist strong links between combinatorial auction and Lagrangean-based decomposition. Exploring some of these properties, we characterize combinatorial auction using auction protocols and payment functions. This study is a first step toward developing a distributed scheduling framework that maintains system-wide performance while accommodating local preferences and objectives. We provide some insights to this framework by demonstrating four different versions of the auction mechanism using job shop scheduling problems.

Proactive cooperative scheduling and buffer management for multimedia networks

Is it possible, and to what extent feasible, for link schedulers and buffer managers in multimedia networks with propagation delays to relieve effectively congestion by means of feedback signaling? We show that a proactive cooperative control algorithm for distributed resource scheduling and buffer management obviates the problem of propagation delays. The algorithm is based on traffic predictions of correlated input traffic streams into network nodes. Nodes projecting to be under stress signal their neighboring upstream nodes for the latter to readjust their flow rates, whileguaranteeing quality of service as negotiated at call setup. Detailed implementations of the adaptive, feedback control mechanisms of distributed asynchronous algorithms that project future overload conditions are presented. The design allows the underlying single-node link scheduling and buffer management algorithm to be run in either a cooperative or a noncooperative mode of operation.