Dynamic Resource Scheduling Research Articles

Optimization of warship formation air defense combat model based on dynamic scheduling of combat resources

Aiming at the problem of limited resources and operational time combat air defense combat, naval combat formation analysis dynamic resources scheduling needs, on the basis of the establishment of ship dynamic scheduling model of warship formation combat resources dynamic combat formation resource scheduling, the operational resources and operational objectives, relationship between reasonable allocation of resources to play the warship formation combat operations the maximum efficiency. The design of three kinds of air defense command models: centralized platform free attack mode, command mode, request attack mode of air defense strategy, put forward the dynamic scheduling hybrid algorithm for solving the model, according to the naval fleet air defense combat scenario example for simulation. The simulation results show that the air defense strategy with the request attack mode can have effects on multi-target attacks under the shared combat situation, and the hybrid algorithm based on combat resource dynamic scheduling can meet the real-time operational requirements.

Dynamic resource scheduling strategy for 5G network slicing based on improved greedy algorithm

Dynamic resource scheduling strategy for 5G network slicing based on improved greedy algorithm

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.

Dynamic Resource Scheduling in Mobile Edge Cloud with Cloud Radio Access Network

Nowadays, by integrating the cloud radio access network (C-RAN) with the mobile edge cloud computing (MEC) technology, mobile service provider (MSP) can efficiently handle the increasing mobile traffic and enhance the capabilities of mobile devices. But the power consumption has become skyrocketing for MSP and it gravely affects the profit of MSP. Previous work often studied the power consumption in C-RAN and MEC separately while less work had considered the integration of C-RAN with MEC. In this paper, we present an unifying framework for the power-performance tradeoff of MSP by jointly scheduling network resources in C-RAN and computation resources in MEC to maximize the profit of MSP. To achieve this objective, we formulate the resource scheduling issue as a stochastic problem and design a new optimization framework by using an extended Lyapunov technique. Specially, because the standard Lyapunov technique critically assumes that job requests have fixed lengths and can be finished within each decision making interval, it is not suitable for the dynamic situation where the mobile job requests have variable lengths. To solve this problem, we extend the standard Lyapunov technique and design the VariedLen algorithm to make online decisions in consecutive time for job requests with variable lengths. Our proposed algorithm can reach time average profit that is close to the optimum with a diminishing gap (1/V) for the MSP while still maintaining strong system stability and low congestion. With extensive simulations based on a real world trace, we demonstrate the efficacy and optimality of our proposed algorithm.

Optimization of Dynamic Resource Scheduling Algorithm in Grid Computing Environment

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Cooperative Data Aggregation and Dynamic Resource Allocation for Massive Machine Type Communication

The accommodation of massive machine-type communication (mMTC) in cellular networks brings up serious technical challenges due to concurrent massive access of MTC devices. These challenges may further be aggravated by the presence of delay tolerant and intolerant services in an MTC network. This paper proposes a cooperative data aggregation (CDA) scheme by employing fixed data aggregator (FDA) and multiple mobile data aggregators (MDAs) to cater MTC devices having variable quality of service (QoS) requirements. In this vein, a distributed MDA selection algorithm is also proposed to designate appropriate user equipment as aggregator. The proposed CDA scheme effectively caters the massive access and provides ubiquitous availability of the aggregating devices in the MTC network. In addition, the limited channel resources impel an FDA to schedule resources besides data aggregation. Therefore, a resource allocation scheme is also proposed to dynamically allocate channels to the MTC devices subject to their QoS requirements. The proposed resource scheduling scheme ensures that transmission requests from delay intolerant MTC devices are contented on priority basis. The proposed CDA and dynamic resource scheduling schemes are analyzed and compared with the existing data aggregation and resource scheduling schemes, respectively. The numerical results corroborate that our proposed CDA scheme in conjunction with dynamic resource allocation improves the outage probability, energy efficiency, and system capacity by 30%, 25%, and 44%, respectively, as compared to the single FDA scheme.

Subway dynamic resource scheduling model and simulation based on distance game

Subway dynamic resource scheduling model and simulation based on distance game

An accurate resource scheduling system for virtual machines based on CPU load monitoring and assessment

An accurate resource scheduling system (RSS) for virtual machines based on CPU monitoring and load assessment is presented to solve the shortcoming of resource scheduling in cloud computing systems. A new architecture is designed to improve Credit scheduler, including three core components: CPU load monitoring component (CLMA), CPU load assessment component (CLAA), and the resource adjustment component (RSA). On the basis of the prototype design, we make an evaluation between Credit scheduler and our system with a typical example in Xen platform. The experimental results show that the proposed system could satisfy the personalized resources requirements from users with higher tasks resource utilization and lower system resource utilization when compared with Credit scheduler. RSS has a strong sensitivity to meet the requirements of cloud computing systems, since it can accelerate the executions of applications via dynamic resource scheduling.

HydroMP – a computing platform for hydrodynamic simulation based on cloud computing

Abstract Modern water management decisions are increasingly dependent on efficient numerical simulations of multiple scenarios with multi-models. In this paper, a service mode for the hydrodynamic simulation based on cloud computing is proposed, and the relevant frameworks of the Hydrologic/Hydraulic Modeling Platform (HydroMP) are designed and implemented. Various hydro-models can be integrated into HydroMP dynamically without the need of program recompiling, since it achieves the scheduling of computing resources to provide end users with the rapid computing capacity of concurrent scenario simulations in the form of a Web service. The present study focuses on the dynamic model integration, resource scheduling, system communication and data structure design. To use the present one-dimensional hydrodynamic cloud computing as a prototype, two integration methods (including the EXE integration and PIIM integration) are applied to construct the CE-QUAL-RIV1 and JPWSPC (Joint Point Water Stage Prediction and Correction) models, thereby to investigate real-time scheduling of the water transfer channels in the South-to-North Water Diversion (SNWD) project. The results showed that massive modeling scenarios by use of different hydrodynamic models, if submitted concurrently, can be processed simultaneously in the HydroMP. The data structure of the proposed framework can also be extended to two-dimensional and three-dimensional hydrodynamic situations.

A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine

Live migration of virtual machines is an important approach for dynamic resource scheduling in cloud environment. The hybrid-copy algorithm is an excellent algorithm that combines the pre-copy algorithm with the post-copy algorithm to remedy the defects of the pre-copy algorithm and the post-copy algorithm. Currently, the hybrid-copy algorithm only copies all memory pages once in advance. In a write-intensive workload, copy memory pages once may be enough. However, more iterative copy rounds can significantly reduce the page faults in a read-intensive workload. In this paper, we propose a new parameter to decide the appropriate time to stop the iterative copy phase based on real-time situation. We use a Markov model to forecast the memory access pattern. Based on the predicted results and the analysis of the actual situation, the memory page transfer order would be adjusted to reduce the invalid transfers. The novel hybrid-copy algorithm is implemented on the Xen platform. The experimental results demonstrate that our mechanism has good performance both on read-intensive workloads and write-intensive workloads.

Research of Dynamic Scheduling of Resources Based on Virtual Machine Migration in Cloud Environment

Research of Dynamic Scheduling of Resources Based on Virtual Machine Migration in Cloud Environment

Dynamic scheduling and optimization of resources in hyper-cellular network}{Dynamic scheduling and optimization of resources in hyper-cellular network

With the explosive growth of wireless data traffic, the energy consumption of wireless communication systems is increasingly dramatically. This has become one of the main sources of global energy consumption. In order to reduce the energy consumption of wireless communication systems, this paper discusses resource dynamic scheduling and optimization methods in hyper-cellular architecture. Specifically, we model the energy efficiency of separation architecture and propose adaptive sleeping strategies for hyper-cellular network. We then elaborate on the tradeoff between latency and energy efficiency, and simultaneously optimize energy efficiency and delay.

System design and software-defined implementation for hyper-cellular network

The rapid growth of traffic data in mobile networks has led to a shortage in spectrum resources and a significant increase in energy consumption. In order to solve these problems, hyper-cellular networks provide dynamic sleep and resource scheduling schemes for base stations through careful separation of control coverage and traffic coverage, thus greatly improving network spectral and energy efficiency. However, how to separate network control coverage and traffic coverage without destroying existing terminal protocol stacks is a problem that still needs to be solved. We analyze the signaling flow and protocol architecture of GSM and LTE systems, and then propose a separation scheme for these systems in a hyper-cellular architecture. The basic idea is to accomplish the separation of base station functionality by dividing the logical channel, thus achieving separation of control coverage and traffic coverage. With the aid of software radio equipment, we build an experimental platform to verify the proposed solutions. Combined with other technologies such as cloud radio access networks and Software Defined Networks, we propose a software-defined hyper cellular architecture and create a cloud-based demonstration platform.

A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems

The resource distribution in post-disaster is an important part of emergency resource scheduling. In this paper, we first design a multi-objective optimization model for multi-period dynamic emergency resource scheduling (ERS) problems. Then, using the framework of multi-objective evolutionary algorithm based on decomposition (MOEA/D), an MOEA is proposed to solve this model. In the proposed algorithm, new evolutionary operators are designed with the intrinsic properties of multi-period dynamic ERS problems in mind. The experimental results show that the proposed algorithm can get a set of better candidate solutions than the non-dominated sorting genetic algorithm II (NSGA-II).

Software-Defined Network Virtualization Platform for Enterprise Network Resource Management

Applications of a software-defined network (SDN) have increased. Notably an SDN has the benefits of programmability and automation for network control. It also realizes network virtualization. Based on the OpenFlow/FlowVisor network virtualization layer, this paper proposes a dynamic resource management scheme, called the EnterpriseVisor engine that manages the distribution of network resources among slices. The EnterpriseVisor engine first slices an enterprise’s network into virtual subnets, and then continues to monitor the resource utilization of each subnet. In the engine, dynamic resource scheduling of the subnet for each service uses the linear programming method. The engine then delivers the schedule as the resource allocation policy of the FlowVisor layer for data plane users. To demonstrate the feasibility of the proposed EnterpriseVisor engine, an OpenFlow testbed with four subnets is designed. Compared with the basic OpenFlow and OpenFlow/FlowVisor platforms, the EnterpriseVisor engine has higher network utilization, 25.7% and 13.4% higher, respectively, with only a minor sacrifice to control message latency.

An overview of dynamic resource scheduling on graphics processors

Abstract In this paper, we present a series of scheduling approaches targeted for massively parallel architectures, which in combination allow a wider range of algorithms to be executed on modern graphics processors. At first, we describe a new processing model which enables the efficient execution of dynamic, irregular workloads. Then, we present the currently fastest queuing algorithm for graphics processors, the most efficient dynamic memory allocator for massively parallel architectures, and the only autonomous scheduler for graphics processing units that can dynamically support different granularities of parallelism. Finally, we show how these scheduling approaches help to advance the state-of-the-art in the rendering, visualization and procedural modeling.

A Dynamic Resource Scheduling Method Based on Fuzzy Control Theory in Cloud Environment

The resources in cloud environment have features such as large-scale, diversity, and heterogeneity. Moreover, the user requirements for cloud computing resources are commonly characterized by uncertainty and imprecision. Hereby, to improve the quality of cloud computing service, not merely should the traditional standards such as cost and bandwidth be satisfied, but also particular emphasis should be laid on some extended standards such as system friendliness. This paper proposes a dynamic resource scheduling method based on fuzzy control theory. Firstly, the resource requirements prediction model is established. Then the relationships between resource availability and the resource requirements are concluded. Afterwards fuzzy control theory is adopted to realize a friendly match between user needs and resources availability. Results show that this approach improves the resources scheduling efficiency and the quality of service (QoS) of cloud computing.

Dynamic Resource Allocation and Scheduling for Cloud-Based Virtual Content Delivery Networks

This paper proposes a novel framework for virtual content delivery networks (CDNs) based on cloud computing. The proposed framework aims to provide multimedia content delivery services customized for content providers by sharing virtual machines (VMs) in the Infrastructure-as-a-Service cloud, while fulfilling the service level agreement. Furthermore, it supports elastic virtual CDN services, which enables the capabilities of VMs to be scaled to encompass the dynamically changing resource demand of the aggregated virtual CDN services. For this, we provide the system architecture and relevant operations for the virtual CDNs and evaluate the performance based on a simulation.

Cloud Computing Resource Schedule Strategy Based on PSO Algorithm

Resource schedule Strategy is the core technology of cloud computing. PSO algorithm is one of dynamic adaptation resource scheduling algorithms to cloud computing. The virtual machines and the hosts can be scheduled reasonable by adjusting parameters. The resource can be scheduled quickly because of the dynamic trend calculation of PSO algorithm, to ensure real-time of the Cloud Calculation.

Prediction-based Dynamic Resource Scheduling for Virtualized Cloud Systems

Virtualization and cloud computing technologies now make it possible to consolidate multiple online services, which are packed in virtual machines (VMs), into a smaller number of physical servers. However, it is still a challenging scheduling problem for cloud provider to dynamically manage the resource for VMs in order to handle variable workloads without service level agreement (SLA) violation. In this paper, we introduce a Prediction-based Dynamic Resource Scheduling (PDRS) solution to automate elastic resource scaling for virtualized cloud systems. Unlike traditional static consolidation or threshold-driven reactive scheduling, we both consider the dynamic workload fluctuations of each VM and the resource conflict handling problem. PDRS first employs an online resource prediction, which is a VM resource demand state predictor based on the Autoregressive Integrated Moving Average (ARIMA) model, to achieve adaptive resource allocation for cloud applications on each VM. Then we propose our prediction-based dynamic resource scheduling algorithms to dynamically consolidate the VMs with adaptive resource allocation to reduce the number of physical machines. Extensive experimental results show that our scheduling is able to realize automatic elastic resource allocation with acceptable effect on SLAs.