Towards optimal resource allocation for differentiated multimedia services in cloud computing environment

Nan, Xiaoming; He, Yejun; Guan, Ling

doi:10.1109/icassp.2014.6853683

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…Previous studies have considered how to manage multimedia services in single-DC-based cloud environment [10]- [14]. Nan et al investigated the workload scheduling and resource allocation in such clouds, and used the queueing theory to evaluate the QoS of multimedia services [10]- [12]. In [13], the authors leveraged the queueing networks to analyze the capacity of video-on-demand (VoD) in single-DC clouds and designed an algorithm for dynamic resource allocation.…”

Section: Related Workmentioning

confidence: 98%

“…1 The energy consumption in a private DC in TS is calculated as where and are the energy consumption per server when it is in working and idle state for a TS, respectively, is the power usage efficiency (PUE) of each DC, which is defined as the ratio of its total power consumption to that of the servers [29]. Then, the energy cost of the private DCs is (12) where is the electricity price for DC . And the time-average expectation energy cost (i.e., ) of the private DCs is .…”

Section: Profit-driven Service Modelmentioning

confidence: 99%

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Distributed Online Hybrid Cloud Management for Profit-Driven Multimedia Cloud Computing

Sun

et al. 2015

IEEE Trans. Multimedia

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It is known that with a hybrid cloud, a multimedia cloud service provider (MCSP) can quickly extend its services to multiple geographical locations with quality-of-service (QoS) guarantees. Meanwhile, to maximize its profit, the MCSP needs an online management mechanism to operate the hybrid cloud efficiently. In this paper, we study how to maximize an MCSP's profit from provisioning multimedia services to geographically distributed users with a hybrid cloud. We first design a service provisioning model to manage the resources in the hybrid cloud. Here, in order to make the model practical and address the different situations in private and public clouds, we consider different time granularities for resource reservations. Then, we leverage the Lyapunov optimization technique to maximize the profit of MCSP and propose an online algorithm that can manage the hybrid cloud in the distributed manner. Specifically, the algorithm determines the access control and routing of each multimedia service request, and allocates the resources in the hybrid cloud accordingly. We also apply the -persistent technique to ensure that the worst-case latency of the provisioned requests is bounded. Finally, the proposed algorithm is evaluated with extensive simulations using both synthetical and real traces. Simulation results indicate that the algorithm can manage the hybrid cloud efficiently and maximize the profit of MCSP.Index Terms-Datacenter management, hybrid cloud, Lyapunov optimization, QoS-aware requests scheduling.

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Section: Related Workmentioning

confidence: 98%

Section: Profit-driven Service Modelmentioning

confidence: 99%

Distributed Online Hybrid Cloud Management for Profit-Driven Multimedia Cloud Computing

Sun

et al. 2015

IEEE Trans. Multimedia

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“…Authors in [7,8] present a three-tier architecture for multimedia data center which comprises three different types of servers; master, computing and transmission, as depicted in Fig 1. In the beginning phase, the master servers distribute the workload to the computing servers and the cloud provider utilizes different types of VM clusters to complete each service task. In the end phase, the transmission server allocates bandwidth resources to transfer the results to the users.…”

Section: Related Workmentioning

confidence: 99%

“…Multimedia processing enforces new challenges to cloud computing, according to attributes such as being delay sensitive, high computation intensity and significant bandwidth demands [13]. For delay sensitive applications, the authors in [8] consider the total round trip time (RTT) (e.g., both service time and transmission time), whereas in [12] only service time is considered. Moreover, to minimize delay, authors in [6,14] proposed cloudlets model to process multimedia service requests.…”

Section: Related Workmentioning

confidence: 99%

Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing

et al. 2018

Self Cite

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This paper focuses on an integrated workload scheduling and pre-fetching model in a multimedia mobile cloud computing environment to enhance the performance of response time and reduce the cost to process multimedia data. The response time and cost optimization problems are presented along with the computation resources such as virtual machines (VMs) allocation, workload conservation, queueing stability constraints, and to overcome the total response time and cost, a heuristic approach of workload scheduling method is proposed. The integrated workload scheduling at pre-fetcher and cloud are considered to study the effects of various parameters such as VM's processing speed, pre-fetcher's utilization, the user requests arrival rate. The performance analysis results reveal that the cost and transmission speed are directly relevant factors, meaning that, once the rate of data transmission is increasing, the cost is also increasing and vice versa. Hence, the time and cost efficient workload scheduling is essential to satisfy both delay and cost in pre-fetcher enabled multimedia cloud systems.

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“…A key challenge for service differentiation is how to satisfy different users. In [9], for the purpose of minimizing resource cost under response time constraints of multimedia services, Nan et al proposed a queueing model to describe service differentiation, and optimized cloud resources in the first-come first-served (FCFS) scenario and the priority scenario, respectively. In [10], in order to share CPU service in clusters of servers, Katsalis et al proposed Dynamic Weighted Round Robin (DWRR) algorithms, and used stochastic control theory to demonstrate that the objective of service differentiation is achieved by DWRR.…”

Section: Introductionmentioning

confidence: 99%

A Task Scheduling Strategy in Cloud Computing with Service Differentiation

Xue¹,

Jin²,

Wang³

2018

TIIS

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Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

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Towards optimal resource allocation for differentiated multimedia services in cloud computing environment

Cited by 8 publications

References 8 publications

Distributed Online Hybrid Cloud Management for Profit-Driven Multimedia Cloud Computing

Distributed Online Hybrid Cloud Management for Profit-Driven Multimedia Cloud Computing

Performance of integrated workload scheduling and pre-fetching in multimedia mobile cloud computing

A Task Scheduling Strategy in Cloud Computing with Service Differentiation

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