Video-on-demand streaming services have gained popularity over the past few years. An increase in the speed of the access networks has also led to a larger number of users watching videos online. ...Online video streaming traffic is estimated to further increase from the current value of 57% to 69% by 2017, Cisco, 2014. In order to retain the existing users and attract new users, service providers attempt to satisfy the user's expectations and provide a satisfactory viewing experience. The first step toward providing a satisfactory service is to be able to quantify the users' perception of the current service level. Quality of experience (QoE) is a quality metric that provides a holistic measure of the users' perception of the quality. In this survey, we first present a tutorial overview of the popular video streaming techniques deployed for stored videos, followed by identifying various metrics that could be used to quantify the QoE for video streaming services; finally, we present a comprehensive survey of the literature on various tools and measurement methodologies that have been proposed to measure or predict the QoE of online video streaming services.
The MapReduce (M/R) framework used in Hadoop has become the de facto standard for big data analytics. However, the lack of network-awareness of the default M/R resource manager in a traditional IP ...network can cause unbalanced job scheduling and network bottlenecks; such factors can eventually lead to an increase in the Hadoop M/R job completion time. In this paper, we propose a software-defined network (SDN) approach in an application-aware network (AAN) platform that provides both underlying networks functions as well M/R particular forwarding logics. We measure the resources' usage for M/R workloads using the HiBench benchmark suite to identify the traffic pattern. We then demonstrate that by using our AAN-SDN framework, which uses an adaptive traffic engineering mechanism, the job completion time can be noticeably improved.
Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE ...models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance
during
streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose
Moving QoE
(MQoE, in short) models to measure QoE
during
periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.
Recently, IoT (Internet of Things) has been an attractive area of research to develop smart home, smart city environment. IoT sensors generate data stream continuously and majority of the IoT based ...applications are highly delay sensitive. The initially used cloud based IoT services suffers from higher delay and lack of efficient resources utilization. Fog computing is introduced to improve these problems by bringing cloud services near to edge in small scale and distributed nature. This work considers an integrated fog-cloud environment to minimize resource cost and reduce delay to support real-time applications at a lower operational cost. We first present a cooperative three-layer fog-cloud computing environment, and propose a novel optimization model in this environment. This model has a composite objective function to minimize the bandwidth cost and provide load balancing. We consider balancing load in both links' bandwidth and servers' CPU processing capacity level. Simulation results show that our framework can minimize the bandwidth cost and balance the load by utilizing the cooperative environment effectively. We assign weight factors to each objective of the composite objective function to set the level of priority. When minimizing bandwidth cost gets higher priority, at first, the demand generated from the traffic generator sensors continues to be satisfied by the regional capacity of layer-1 fog. If the demand of a region goes beyond the capacity of that region, remaining demand is served by other regions layer-1 fog, then by layer-2 fog, and finally by the cloud. However, when load balancing is the priority, the demand is distributed among these resources to reduce delay. Link level load balancing can reduce the queueing delay of links while server level load balancing can decrease processing delay of servers in an overloaded situation. We further analyzed how the unit bandwidth cost, the average and maximum link utilization, the servers' resources utilization, and the average number of servers used vary with different levels of priority on different objectives. As a result, our optimization formulation allows tradeoff analysis in the cooperative three-layer fog-cloud computing environment.
Network providers are often interested in providing dynamically provisioned bandwidth to customers based on periodically measured nonstationary traffic while meeting service level agreements (SLAs). ...In this paper, we propose a dynamic bandwidth provisioning framework for such a situation. In order to have a good sense of nonstationary periodically measured traffic data, measurements were first collected over a period of three weeks excluding the weekends in three different months from an Internet access link. To characterize the traffic data rate dynamics of these data sets, we develop a seasonal autoregressive conditional heteroskedasticity (ARCH) based model with the innovation process (disturbances) generalized to the class of heavy-tailed distributions. We observed a strong empirical evidence for the proposed model. Based on the ARCH-model, we present a probability-hop forecasting algorithm, an augmented forecast mechanism using the confidence-bounds of the mean forecast value from the conditional forecast distribution. For bandwidth estimation, we present different bandwidth provisioning schemes that allocate or deallocate the bandwidth based on the traffic forecast generated by our forecasting algorithm. These provisioning schemes are developed to allow trade off between the underprovisioning and the utilization, while addressing the overhead cost of updating bandwidth. Based on extensive studies with three different data sets, we have found that our approach provides a robust dynamic bandwidth provisioning framework for real-world periodically measured nonstationary traffic.
In this paper, we address the network virtualization problem of embedding a unique shortest path-based IP topology using lightpaths in a wavelength-routed network. We present an integer linear ...programming formulation and propose a 2-phase heuristic approach to solve this problem. We extend the model and the heuristic by addressing survivability in an integrated cross-layer framework, where the objective is to allocate a lightpath topology that remains connected in the event of any single physical link failure while providing the IP network with unique shortest paths for all node-pairs. We consider a number of measures to show effectiveness of our approach and to discuss the impact on normal and survivable topology design, in terms of the number of transreceivers deployed.
Dynamic adaptive HTTP (DASH) based streaming is steadily becoming the most popular online video streaming technique. DASH streaming provides seamless playback by adapting the video quality to the ...network conditions during the video playback. A DASH server supports adaptive streaming by hosting multiple representations of the video and each representation is divided into small segments of equal playback duration. At the client end, the video player uses an adaptive bitrate selection (ABR) algorithm to decide the bitrate to be selected for each segment depending on the current network conditions. Currently, proposed ABR algorithms ignore the fact that the segment sizes significantly vary for a given video bitrate. Due to this, even though an ABR algorithm is able to measure the network bandwidth, it may fail to predict the time to download the next segment In this paper, we propose a segment-aware rate adaptation (SARA) algorithm that considers the segment size variation in addition to the estimated path bandwidth and the current buffer occupancy to accurately predict the time required to download the next segment We also developed an open source Python based emulated DASH video player, that was used to compare the performance of SARA and a basic ABR. Our results show that SARA provides a significant gain over the basic algorithm in the video quality delivered, without noticeably impacting the video switching rates.
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