Innovative Schemes for Resource Allocation in the Cloud for Media Streaming Applications

Alasaad, Amr; Shafiee, Kaveh; Behairy, Hatim; Leung, Victor C. M.

doi:10.1109/tpds.2014.2316827

Cited by 43 publications

(18 citation statements)

References 14 publications

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“…However, measurement techniques are hard to use in computer service performance prediction especially for cloud environments. The authors in [27] propose a new scheme for resource reservation that maximally exploits discounted rates offered in the tariffs, while ensuring that sufficient resources are reserved in the cloud market. In order to make accurate resource allocation decisions, the scheme predicts the demand for streaming capacity.…”

Section: System Model and Related Workmentioning

confidence: 99%

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan

Itradat

Al‐Dubai

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In the current cloud business environment, the cloud provider (CP) can provide a means for offering the required quality of service (QoS) for multiple classes of clients. We consider the cloud market where various resources such as CPUs, memory, and storage in the form of Virtual Machine (VM) instances can be provisioned and then leased to clients with QoS guarantees. Unlike existing works, we propose a novel Service Level Agreement (SLA) framework for cloud computing, in which a price control parameter is used to meet QoS demands for all classes in the market. The framework uses reinforcement learning (RL) to derive a VM hiring policy that can adapt to changes in the system to guarantee the QoS for all client classes. These changes include: service cost, system capacity, and the demand for service. In exhibiting solutions, when the CP leases more VMs to a class of clients, the QoS is degraded for other classes due to an inadequate number of VMs. However, our approach integrates computing resources adaptation with service admission control based on the RL model. To the best of our knowledge, this study is the first attempt that facilitates this integration to enhance the CP's profit and avoid SLA violation. Numerical analysis stresses the ability of our approach to avoid SLA violation while maximizing the CP's profit under varying cloud environment conditions.

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Section: System Model and Related Workmentioning

confidence: 99%

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Alsarhan

Itradat

Al‐Dubai

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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“…The solutions are either the neighborhood is selected adaptively or the filter is adaptive in operation. For first solution, the adaptive neighborhood can be formulized as: (7) where f(y) represents an input image, f(x) is the smoothed image, N(x) is the adaptive neighborhood and M is the size of adaptive neighborhood. In (7), N(x) consists of pixels in Ω, which are homogeneous and have intensities close to that of x, for each x∊Ω.…”

Section: F Adaptive Filteringmentioning

confidence: 99%

“…Current video processing standards such as H.264 and H.265 are dealing with immense amount of data, which ultimately demands either parallel or distributed processing platforms. In the case of cloud computing, many Video Service Providers (VSPs) can rent out the distribution architecture from Cloud Service Providers (CSPs) to deliver video streams to a large number of mobile end users [6,7].…”

Section: Introductionmentioning

confidence: 99%

Frame Interpolation for Cloud-Based Mobile Video Streaming

Usman

Lam

et al. 2016

IEEE Trans. Multimedia

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Cloud-based High Definition (HD) video streaming is becoming more and more popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data on different servers at different locations from security and mobile availability point of views, especially for end users having small amount of storage in their mobile devices. On the other hand, it is becoming a big challenge for network service providers to provide constant and reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like Youtube. Packet losses and bit errors are very common in transmission networks, producing annoying effects such as frame freezing and blocky artifacts, which affects the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at decoder/receiver side to estimate the lost information. This paper proposes a time efficient and quality oriented EC method. The proposed method considers H.265/HEVC based Intra-encoded videos for the estimation of whole Intra-frame loss. The unsliced mode of H.265 is targeted for the proposed approach. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. The search to find the optimum MV is performed in parallel in nearby four sub-blocks in the reference frame. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a Packet Loss Rates of 1%, 3% and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1,788 seconds. The proposed technique can readily be applied for real-time cloudbased HD video streaming.

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“…The Nash bargaining game is responsible for assigning the desired commodities to the servers in the market in such a way that the social welfare is achieved and any VMs do not exceed their capacities. Resource allocation for media streaming applications Amr et al [31] proposed a prediction based resource allocation scheme for media streaming applications. Their proposed scheme mainly focuses on optimizing the tariff cost of media cloud by minimizing the media cloud reserved resources.…”

Section: Resource Management In Multimedia Cloud Computingmentioning

confidence: 99%

Taxonomy and issues for antifragile-based multimedia cloud computing

Haider

Abbas

Ali

et al. 2016

J Reliable Intell Environ

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Cloud computing has become one of the most dynamic and adoptable computing paradigms. Multimedia Cloud Computing (MCC) is one of today's hot research topic. MCC is proven to be a most dynamic and efficient platform for managing a large amount of multimedia contents with maximum deployment of computing and processing resources at the service provider instead of users. Resilience and dependability are two key constituents to assure the reliability and availability of any service in the presence of errors and system failures. The heterogeneous environ-B Muddesar Iqbal ment of MCC has given rise to various challenges related to resource allocation and task management. Antifragility is a key to such environments, to let the disorder drive the strength of these systems. This paper is mainly divided into three parts. The first part discusses in detail the available state-of-the-art related to resource management under MCC. Similarly, the second part presents the comprehensive literature review on the task management in MCC. The third part presents the critical analysis and open research issues in MCC which help the researcher to define their research objectives in the field of MCC.

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Innovative Schemes for Resource Allocation in the Cloud for Media Streaming Applications

Cited by 43 publications

References 14 publications

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Adaptive Resource Allocation and Provisioning in Multi-Service Cloud Environments

Frame Interpolation for Cloud-Based Mobile Video Streaming

Taxonomy and issues for antifragile-based multimedia cloud computing

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