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Adaptive Peer Selection Strategy in P2P-VoD systems Based on Dynamic Metaheuristic

Adaptive Peer Selection Strategy in P2P-VoD Systems Based on...

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Thibaud Rohmer Amir Nakib Julien Lepagnot Abdelhamid Nafaa UPEC LISSI Vitry sur seine France MIA UHA Mulhouse France UCD PEL Belfield Dublin 4 Ireland

During the past decade, Peer-to-Peer Video-on Demand (VoD) systems have proved their efficiency for large deployments. They raise new challenges such as peers resource allocation. Most literature on resource allocation tackle the problem with optimal static rules found at offline study of the system. In this paper, we use a dynamic metaheuristic, called Multiple Local-Search Algorithm for Dynamic Optimization (MLSDO) to optimize the problem at hand. The obtained results show that using a dynamic resource allocation reduces the rejection rate while enhancing the entropy of the system, in the face of a dynamically changing title demand.

关键词： Heuristic algorithms resource management Entropy Optimization Dynamic scheduling Peer-to-peer computing Measurement

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Bag of Tasks Rescheduling within Real Grid Environments: Different Approaches

Bag of Tasks Rescheduling within Real Grid Environments: Dif...

引用

Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Luis Tomás Blanca Caminero Carmen Carrión Department of Computing Systems University of Castilla-La Mancha Albacete Spain

Providing Quality of Service (QoS) in Grid environments is still a challenging task because advance reservation of resources is not always possible. By contrast, scheduling the use of resources in advance is a way of enhancing the provision of QoS, without physically reserving them. However, in a Grid scenario fragmentation may appear as a logic result of the allocation process, leading to poor resource utilization. To try to avoid that problem a rescheduling technique has been developed and applied periodically depending on the existing fragmentation. Nevertheless, reallocating jobs in an optimal way is an NP-Hard problem. Consequently, simple and scalable ways of distributing and allocating those jobs into the resources are presented and evaluated in a real Grid environment involving heterogeneous computing resources distributed across different national organizations, highlighting the benefits of using them to increase the resource usage.

关键词： resource management Quality of service scheduling Time factors Processor scheduling NP-hard problem Heuristic algorithms

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Utilization-Aware Allocation for Multi-Tenant Datacenters

Utilization-Aware Allocation for Multi-Tenant Datacenters

引用

international Conference on distributed Computing systems workshop

作者： Yang Gao Yibo Xue Jun Li Research Institute of Information Technology Tsinghua University Beijing China Tsinghua University Beijing Beijing CN Tsinghua National Lab for Information Science and Technology Beijing China

Cloud datacenters need efficient resource management that is able to orchestrate bulks of different resources. In current public cloud datacenters, there is a mismatch between the requirements of tenant requests and resource utilization. Multiple resource types in datacenters make the situation even more complex. Intelligent resource allocation methods are demanded to improve the overall utilization of multiple resources. This paper presents Multiple resource Utilization (MRU), a novel resource allocation method for multi-tenant datacenters. MRU allocates multiple types of datacenter resources according to the diverse and dynamic requests from tenants, and simultaneously maximize resource utilization of datacenter and guarantee the resource availability and performance for tenants. Experiments demonstrate that MRU significantly improves the utilization of multiple resources and thus save the cost.

关键词： resource management Servers Standards Subscriptions Bandwidth Virtual machining Dynamic scheduling

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Holistic Load-Balancing in a distributed Spatial Cache

Holistic Load-Balancing in a Distributed Spatial Cache

引用

international Conference on Mobile Data management

作者： Carlos Lubbe Bernhard Mitschang Institute of Parallel and Distributed Systems Universitat Stuttgart Stuttgart Baden-WÃ¼rttemberg DE Institute of Parallel and Distributed Systems Universität Stuttgart Stuttgart Germany

ISBN: (纸本)9781467360685

A steadily growing number of people using location based services (LBS) inflict massive query loads on the data tier of an LBS. As such queries usually possess considerable overlap, multiple cache nodes collaborating in a distributed spatial cache can provide scalable access to frequently used data. To preserve high throughput throughout the complete execution process, it is necessary to balance the accumulating load among the participating cache nodes. In this work, we identify three key-indicators to improve resource utilization during the load-balancing process: data skew, anticipated data access patterns and dynamic load peaks. For this reason, we introduce a comprehensive mathematical model to express the key-indicators as probability distribution functions. We fuse the different key-indicators into a single holistic distribution model. In the course of this, we devise a methodology from our holistic distribution model towards a distributed spatial cache offering improved load-balancing facilities.

关键词： Load modeling resource management Distribution functions Abstracts distributed databases Data models Dynamic scheduling

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Comparing Provisioning and scheduling Strategies for Workflows on Clouds

Comparing Provisioning and Scheduling Strategies for Workflo...

引用

IEEE international Symposium on parallel and distributed Processing workshops and Phd Forum (IPDPSW)

作者： Marc E. Frincu Stéphane Genaud Julien Gossa ICube – UMR 7357 Université de Strasbourg Illkirch France

ISBN: (纸本)9781479913725

Cloud computing is emerging as a leading solution for deploying on-demand applications in both the industry and the scientific community. An important problem which needs to be considered is that of scheduling tasks on existing resources. Since clouds are linked to grid systems much of the work done on the latter can be ported with some modifications due to specific aspects that concern clouds, e.g., virtualization, scalability and on-demand provisioning. Two types of applications are usually considered for cloud migration: bag-of-tasks and workflows. This paper deals with the second case and investigates the impact virtual machine provisioning policies have on the scheduling strategy when various workflow types and execution times are used. Five provisioning methods are proposed and tested on well known workflow scheduling algorithms such as CPA, Gain and HEFT. We show that some correlation between the application characteristics and provisioning method exists. This result paves the way for adaptive scheduling in which based on the workflow properties a specific provisioning can be applied in order to optimize execution times or costs.

关键词： resource management Processor scheduling Job shop scheduling Clouds Cloud computing Schedules

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Joint Spectrum Selection and Radio resource management for distributed Antenna systems with Cognitive Radio and Space Division Multiplexing

Joint Spectrum Selection and Radio Resource Management for D...

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WSA 2013;17th international ITG workshop on Smart Antennas

作者： Ramiro Samano-Robles Atilio Gameiro Instituto de Telecomunicacoes Campus Universitario

distributed antenna systems (DASs) and cognitive radio (CR) technology are expected to boost the performance of future wireless networks. However, opportunistic access to distributed spectrum resources with different network and economic characteristics requires new approaches for an appropriate spectrum and radio resource management (RRM). To partially fill this gap, this paper proposes a joint spectrum selection and RRM algorithm for the down-link of a DAS system assisted by CR. In this paper, each radio and spectrum resource is considered as a financial asset whose allocation has to be optimized based on economic metrics such as return and risk (variation of the return). The core of the RRM is based on an iterative weighted least squares (WLS) optimization where power levels and beamforming vectors are jointly calculated so as to comply with a different SINR (signal-to-interference-plus-noise ratio) for each scheduled user. This SINR level ensures the transmission of the selected modulation and coding scheme (MCS) with a given value of BLER (block error rate). The WLS scheme allows a smooth integration of scheduling and adaptive modulation and coding schemes with the underlying space division multiplexing layer. Each WLS iteration is solved by the method of gradient steepest descent, whose convergence speed is improved by reusing the outcome of previous WLS iterations. The weights of the WLS optimization contain network (queue length and fairness), as well as economic metrics (return and risk). This process is complemented by a multi-objective portfolio optimization for joint spectrum selection and radio resource allocation that aims to maximize return and reduce risk, where risk is found to be closely related to the interference created by secondary transmissions. It is shown that network and economic performance metrics can be both optimized given an appropriate trade-off between return and risk functions.

关键词： Cognitive radio Multi-objective portfolio optimization Spectrum aggregation/selection Cognitive radio distributed antenna system space division multiplexing Loops WLS gene management of resources Spectra resource utilization ANTENNA SYSTEM Performance metrics block error probability labeling scheme

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VGRIS: virtualized GPU resource isolation and scheduling in cloud gaming 13

VGRIS: virtualized GPU resource isolation and scheduling in ...

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Proceedings of the 22nd international symposium on High-performance parallel and distributed computing

作者： Miao Yu Chao Zhang Zhengwei Qi Jianguo Yao Yin Wang Haibing Guan Shanghai Key Laboratory of Scalable Computing and Systems. School of Software Shanghai Jiao Tong University Shanghai China HP Labs Palo Alto USA

ISBN: (纸本)9781450319102

Fueled by the maturity of virtualization technology for Graphics Processing Unit (GPU), there is an increasing number of data centers dedicated to GPU-related computation tasks in cloud gaming. However, GPU resource sharing in these applications is usually poor. This stems from the fact that the typical cloud gaming service providers often allocate one GPU exclusively for one game. To achieve the efficiency of computational resource management, there is a demand for cloud computing to employ the multi-task scheduling technologies to improve the utilization of *** this paper, we propose VGRIS, a resource management framework for Virtualized GPU resource Isolation and scheduling in cloud gaming. By leveraging the mature GPU paravirtualization architecture, VGRIS resides in the host through library API interception, while the guest OS and the GPU computing applications remain unmodified. In the proposed framework, we implemented three scheduling algorithms in VGRIS for different objectives, i.e., Service Level Agreement (SLA)-aware scheduling, proportional-share scheduling, and hybrid scheduling that mixes the former two. By designing such a scheduling framework, it is possible to handle different kinds of GPU computation tasks for different purposes in cloud gaming. Our experimental results show that each scheduling algorithm can achieve its goals under various workloads.

关键词： GPU scheduling cloud gaming resource management

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An economic allocation of resources in Grid environment

An economic allocation of resources in Grid environment

引用

international Conference on Information systems and Computer Networks (ISCON)

作者： Saurabh Singhal Manoj Kumar Krishna Kant Department of CEA GLA University Mathura India

ISBN: (纸本)9781467359870

A Grid is a large-scale, heterogeneous, dynamic collection of independent systems which are geographically distributed and interconnected with high speed networks to meet ever increasing demand of high performance computing. Different strategies have been reported in the literature to discover and allocate resources that are suitable for jobs to achieve performance up to users' satisfaction at competitive price. The related resource sharing becomes complex when job and data are partitioned for parallel execution. A simple resource index based allocation algorithm is proposed for such jobs in this paper. The technique is analytically examined and implemented in C to obtain results under various environment conditions.

关键词： resource management Biological system modeling Economics Heuristic algorithms Computational efficiency Indexes scheduling

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Cloud task scheduling based on ant colony optimization

Cloud task scheduling based on ant colony optimization

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international Conference on Computer Engineering and systems

作者： Medhat A. Tawfeek Ashraf El-Sisi Arabi E. Keshk Fawzy A. Torkey Department of Computer Science Menoufia University

Cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimization algorithm compared with different scheduling algorithms FCFS and round-robin, has been presented. The main goal of these algorithms is minimizing the makespan of a given tasks set. Ant colony optimization is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that the ant colony optimization outperformed FCFS and round-robin algorithms.

关键词： Processor scheduling Cloud computing scheduling Heuristic algorithms resource management Computational modeling Optimization

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Adaptive Power and resource management Techniques for Multi-threaded Workloads

Adaptive Power and Resource Management Techniques for Multi-...

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IEEE international Symposium on parallel and distributed Processing workshops and PhD Forum

作者： Can Hankendi Ayse K. Coskun Electrical and Computer Engineering Department Boston University Boston MA 02215

ISBN: (纸本)9781479913725

As today's computing trends are moving towards the cloud, meeting the increasing computational demand while minimizing the energy costs in data centers has become essential. This work introduces two adaptive techniques to reduce the energy consumption of the computing clusters through power and resource management on multi-core processors. We first present a novel power capping technique to constrain the power consumption of computing nodes. Our technique combines Dynamic Voltage-Frequency Scaling (DVFS) and thread allocation on multi-core systems. By utilizing machine learning techniques, our power capping method is able to meet the power budgets 82% of the time without requiring any power measurement device and reduces the energy consumption by 51.6% on average in comparison to the state-of-the-art techniques. We then introduce an autonomous resource management technique for consolidated multi-threaded workloads running on multi-core servers. Our technique first classifies applications according to their energy efficiency measure, then proportionally allocates resources for co-scheduled applications to improve the energy efficiency. The proposed technique improves the energy efficiency by 17% in comparison to state-of-the-art co-scheduling policies.

关键词： increasing computational demand energy efficiency state-of-the-art techniques

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