Resource Allocation Mechanism for Media Handling Services in Cloud Multimedia Conferencing

Soltanian, Abbas; Naboulsi, Diala; Glitho, Roch; Elbiaze, Halima

doi:10.1109/jsac.2019.2906806

Cited by 12 publications

(8 citation statements)

References 28 publications

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“…In each time interval, the allocated VM resources are calculated by adding up the unexpired VMs. When allocating resources to cloud-based software services, a common objective of cloud engineers or self-adaptive systems is to balance the relationship between the QoS and resource costs, which can be expressed by using an objective function [27].…”

Section: Problem Formulationmentioning

confidence: 99%

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

et al. 2020

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Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods. INDEX TERMS Cloud-based software services, resource allocation, QoS prediction, workload-time windows, particle swarm optimization, genetic algorithm.

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Section: Problem Formulationmentioning

confidence: 99%

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

et al. 2020

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“…In this work, we refer to these scenarios and adopt two conference scenarios as the basis for evaluation. Recently, there have been many video conferencing systems configured on top of SDN [12]- [14] and deployed in the cloud environment [15], [16].…”

Section: Related Workmentioning

confidence: 99%

“…As shown in Step 1: When R 1 (1, 4) arrives, the dynamic timeslot rounds down and up the request times, respectively, to 0 and 10, which are then sort inserted into TD; thus, TD is updated to TD = [0, 10,15]. Correspondingly, time points 0, 10, 1, and 4 are sort inserted into TE; thus, TE is updated to TE = [0, 1, 4, 10, 15].…”

Section: An Examplementioning

confidence: 99%

Elastic Timeslot-Based Advance Reservation Algorithm for Enterprise Video Conferencing Systems

Liao

Zhang

2020

IEEE Access

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In an enterprise network, it is common to have hundreds of video conferences held simultaneously such that a large number of video streams need to be transmitted between participants in different geographical locations. For such a large transmission demand, an advance reservation (AR) system deployed for the video conferencing system can provide quality-of-service (QoS) guarantees to users and improve the resource utilization of the network. However, the effective resource reservation strategies for a heavy traffic case still remains open. In this paper, we propose an algorithm called the elastic timeslot-based advance reservation algorithm (ETARA), which aims at improving the resource utilization and reducing the computational complexity. A new time processing method, elastic timeslot, is proposed for the time domain management. Moreover, ETARA takes not only the processing time but also the resource usage into account. Comparative simulations with the existing two popular approaches, the dynamic timeslot-based approach and timeslot-based flexible approach, have been performed in terms of the acceptance ratio and runtime. The results show that with the same acceptance ratio, the runtime of ETARA can be up to 57 times lower than that of the flexible timeslot-based approach. Though ETARA has a slightly longer runtime than the dynamic approach, the acceptance ratio of ETARA can be twice as high as that of the dynamic timeslotbased approach. INDEX TERMS Enterprise video conferencing system, advance reservation (AR), bandwidth reservation, QoS.

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“…For the fixed path scheme, the AR system calculates the total cost of the link for the whole session time for the request r k . (18) calculates the cost of the jth link for the request r k for the whole session time based on (17). Finally, the AR system calculates the shortest path based on the cost of each link.…”

Section: A Overviewmentioning

confidence: 99%

“…Recently, software-defined networking (SDN) has exhibited effective performance in cloud network [17]. It decouples the control plane from the data plane and leverages centralized network control and management.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Dynamic Multicast Requests in Advance Reservation Environment for Enterprise Video Conferencing Systems

Liao

Zhang

2020

IEEE Access

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In an enterprise video conferencing system, the enterprise network can be shared by hundreds of video conferences. On the other hand, participants may not arrive at the same time and stay until the end of the conference. The abilities to reserve resources in advance, as well as effective dynamic multicast when participants can join and leave the conference at any time, are essential in the distributed multi-party video conferencing systems. However, the effective advance reservation strategies of the dynamic multicast requests for a heavy traffic case still remains open. In this paper, we investigate the problem of Maximizing the number of admitted Dynamic Multicast requests in the Advance Reservation environment (MDMAR) for the enterprise video conferencing system. We take two path schemes of a fixed path and variable paths, as well as a heterogeneous bandwidth reservation model into account. Firstly, we prove that the MDMAR problem is NP-complete and formulate it mathematically as an integer linear program (ILP) for small networks. Then, we develop greedy algorithms and simulated annealing (SA) algorithms for enterprise networks. Comparative simulations are performed to evaluate the heuristic algorithms for both small networks and enterprise networks. We find that the SA algorithms can provide within 6% lower optimal solutions than the ILP algorithms for our small network, and up to 10% improvement over the greedy algorithms for the large campus or enterprise network.INDEX TERMS Enterprise video conferencing system, advance reservation (AR), integer linear program (ILP), dynamic multicast.

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Resource Allocation Mechanism for Media Handling Services in Cloud Multimedia Conferencing

Cited by 12 publications

References 28 publications

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

Elastic Timeslot-Based Advance Reservation Algorithm for Enterprise Video Conferencing Systems

Scheduling Dynamic Multicast Requests in Advance Reservation Environment for Enterprise Video Conferencing Systems

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