A power saving scheme for cloud service into set-top box

This work shows the implementation of a hybrid STB (Set Top Box) that receives real-time digital signals for ISDB-Tb and IPTV. The implementation integrates middleware GINGA for interactive DTT applications based on low cost platforms. The features of the hardware platform for this implementation are analyzed so that they can integrate USB reception modules for digital TV, support interactive middleware versions and also be compatible with TV playback software by means of internet protocols. After the analysis and evaluation, we found that the most suitable board to achieve this implementation was Raspberry Pi 3. The development of the Android mobile graphic interface is also shown, which emulates a remote control by Bluetooth technology. Finally, the results of the implemented hybrid STB’s evaluation are presented; evaluation performed by reception tests of DTT channels with interactivity and free IPTV channels. Also, quantitative readings were made to test the performance and correct operation, as well as qualitative MOS polls to get the quality of user experience when handling the decoder.

This article introduces a packet-layer model that estimates users' quality of experience (QoE) of audiovisual communication services from packet header information.This model can be incorporated into a client terminal (e.g., home gateway and set-top box) because the computational load for quality estimation is very light.Therefore, this model is applicable to in-service quality management at user premises.

In recent years Digital TV receivers (DTV) and Set- Top Boxes (STB) have been advancing in terms of new functionalities they offer to the end-users. This has mainly been triggered by the appearance of new multimedia formats and applications needed to be adequately handled by the DTV/STB. In such complex system within the environment consisting of wide range of non-interactive as well as interactive applications and services for television, internet, gamming, healthcare, surveillance, etc., efficient and reliable DTV functional verification becomes highly necessary and challenging task. Since efficient DTV testing is considered as one of the most important issues for the reduced time-to-market, its optimization is of crucial importance to the DTV manufacturing process. Having in mind the end-users as the main target of the DTV, optimal test generation process must also include estimated usage-profiles in addition to the pre-defined DTV designed specification. This is especially important considering new emerging multimedia technologies for DTV which are not yet fully standardized and evaluated, and which exact employment (from the point of end-users) is not known in advance. In this paper we propose a novel approach for user driven test-case generation that would enable adequate and efficient testing of the DTV/STB in different development stages before reaching the market, i.e., the end-users. The proposed user driven approach provides optimal test-case generation in terms of both test execution time and adequacy according to the real end-user DTV functional DTV operation and quality of experience (QoE). This is obtained by incorporating a novel design and selection scheme for test-cases generation based on the modeled end-users most required functional operations on the DTV/STB under test and its expectations of it.

This article describes the architecture and design of an IPTV network monitoring system and some of the use cases it enables. The system is based on distributed agents within IPTV terminal equipment (set-top box), which collect and send the data to a server where it is analyzed and visualized. In the article we explore how large amounts of collected data can be utilized for monitoring the quality of service and user experience in real time, as well as for discovering trends and anomalies over longer periods of time. Furthermore, the data can be enriched using external data sources, providing a deeper understanding of the system by discovering correlations with events outside of the monitored domain. Four supported use cases are described, among them using weather information for explaining away the IPTV quality degradation. The system has been successfully deployed and is in operation at the Slovenian IPTV provider Telekom Slovenije.

Quality of Experience (QoE) is referred as level of user's satisfaction, enjoyment, learning and evaluation about the services or products. Recently quality of experience is used for improvement in product development life cycle after getting feedback from end users and service providers also use QoE to measure quality of services (QoS) of their services. Cloud computing provides services such as storage, web hosting, operating system environment, application development platform and CPU resources pay per use. End users are accessing cloud services via mobile apps, but enormous amount of temporary/cache data is generated by apps, so internal storage of mobile devices are filled quickly. Mobile device without any space in internal storage has huge impact on the performance when accessing the cloud services, which degrade the QoE of end users for particular cloud app and services. This paper presents the results of experiments conducted using two mobile devices HTC and Samsung to analyze the impact on end user's QoE during accessing cloud, when internal storage of HTC mobile device is filled and Samsung having 10 GB free space. Finally, on the basis of experimental results future changes in cloud apps are suggested for service provider to improve end user's QoE.

Nowadays, short video feed has attracted billions of mobile users all around the world to interact with content effortlessly, yielding an explosive growth of short video commerce. Typically, users watch full-screen short videos of a few seconds one-by-one in a watch-list generated by recommender systems, skipping those they are not interested in. However, the recommender system at the cloud makes a user-interest-specific decision mostly based on the users' behavior data collected within the application itself (e.g., users' view history), without examining the lower-layer network and communication statistics. When the playback choked due to the limited network bandwidth, the user will probably skip the video, leading to a waste of bandwidth and degradation of the user's quality of experience (QoE). Meanwhile, the excessive number of user requests to video contents raises a heavy computational load and communication cost for the recommender system at the cloud to determine which videos to be recommended and delivered to each user in a real-time manner. The advance of edge computing provides a promising avenue of deploying edge nodes with caches (e.g., household devices) beyond cloud and edge servers, such that the recommender system in the cloud can place popular video contents closer to client users, and meanwhile the contents are delivered to client users with good network condition. In this paper, we propose CFP, a cross-layer recommender system for short video streaming with fine-grained preloading technique at the network edge. CFP jointly optimizes the recommendation effect of the video application and the content preloading efficiency under various network conditions at the network edge. CFP takes a two-stage approach: the cloud server first seeks to perform edge-wise instead of user-interest-specific recommendation with neural collaborative filtering recommender, preloading a list of candidate videos to edge nodes, and each edge node, deploying the GRU with attention, then delivers the proper video contents to the client user device according to the user's preference. Trace-driven emulations demonstrate the efficiency of the proposed CFP scheme.

Massively Multiplayer Online Cloud Game (MMOCG) is a combination of cloud gaming and Massively Multiplayer Online Game (MMOG). Running MMOGs on the cloud can reduce the cost for the players, attract potential players, and meet the nowadays preference of mobile devices. However, the MMOCGs introduce new challenges that need to be addressed. MMOGs usually consume more computational resources because of their sophisticated graphics. Hosting MMOCGs can be more expensive for game operators than hosting other genres of games on the cloud. In order to earn a decent profit, the cost of hosting MMOCGs must be reduced to an acceptable cost. Moreover, MMOCG introduce extra cloud servers to MMOG, which increased the burden of network transmission. So, the MMOCGs must have a good quality of service (QoS) and quality of experience (QoE) to retain the players. In this thesis, we study the server allocation problem to improve MMOCGs in many aspects, including money cost, QoS, and QoE. We proposed three different server allocation strategies to address the challenges of MMOCGs in different situations. We first consider the MMOCG system with several cloud gaming platforms that lie in different locations in the world, each can provide a type of server that can host MMOG on them. The cloud gaming system only obtains the information necessary for gaming. With only the log-in information, a server allocation problem is defined to reduce the money cost of hosting MMOCGs and provide an acceptable network latency for the players. The problem is formulated into an optimization problem that aims to minimize the weighted combination of server rental cost, data transfer cost, and network latency. A genetic algorithm is developed to solve the minimization problem. Extensive experiments are conducted to evaluate the proposed method and compare it with the state-of-the-art as well. The experimental results show that the method gives a lower money cost and a shorter network latency most of the time. We then consider the MMOCG system that can collect the past logging in and out data of the players. We proposed a server allocation problem that aims to reduce the cost of cloud server rental and improve the QoE of the system. A genetic algorithm with gaming time prediction is proposed to solve the problem. The gaming time of the players logging into the system is predicted by the model trained by a neural network. We conduct experiments to evaluate our method and the proposed gaming time prediction model. The results show that our prediction model can achieve almost highly accurate prediction and the server allocation strategy we proposed outperforms the prior art in both server rental and QoE. At last, we consider the influence of QoE on the movement of players during gaming time and propose a server allocation problem aiming to improve the QoE of players during gaming time. We propose a genetic algorithm with trace prediction to address this problem. A trace prediction that predicts the future positions of players for a few time steps when they log into the MMOCG system. Experiments are conducted to evaluate our method by measuring the network latency, mean opinion score, and the time consuming computation. The results show that our method outperforms the genetic algorithm without trace prediction in over 50% of the cases.

This paper shows security and privacy of user data stored in cloud services is of utmost concern especially for commercial public cloud platforms and service providers. The user data stored on cloud is encrypted, and it is desired that data mining operations on encrypted data must be executed efficiently. The proposed system implements a data mining operation namely clustering on encrypted data on cloud platforms using TCP / IP protocol. The clustering operation is performed using standard K-Means, Spectral, Hierarchical, Hybrid DBSCAN and simple DBSCAN clustering technique optimized for encrypted data.

We propose new architectures for collaborative video services using software defined networks (SDN) and cloud computing technologies. In the proposed architectures, the video service provider (VSP) is configured as a cloud service. The network service provider (NSP) employs SDN as a cloud service, consisting of a controller cloud and multiple edge network clouds. The proposed centralized and distributed collaborative cloud video services overcome quality of experience (QoE) fluctuations per flow and unfairness between multiple DASH clients employing TCP receive window adaptation over a network slice with reserved capacity as we show that quality of service (QoS) reservation alone is not sufficient to supply fair and steady QoE to all clients. In the centralized approach, the fair-share bitrate of each client is calculated by the traffic engineering module of the NSP, while in the distributed approach the clients collaborate with each other to calculate their own fair shares. Experimental results show that both collaboration architectures perform better than the competitive approach using standard TCP.

IaaS clouds offer customers on-demand computing resources such as virtual machine, network and storage. To provision and manage these resources, cloud users must rely on a variety of cloud services. However, a wide range of vulnerabilities have been identified in these cloud services that may enable an adversary to compromise customers' computations or even the cloud platform itself. Using the motivation for adding mandatory access to commercial operating systems, we argue for the development of a secure cloud operating system (SCOS) to enforce mandatory access control (MAC) over cloud services and customer instances. To better understand the concrete challenges of building a SCOS, we examine the OpenStack cloud platform from two perspectives: (1) how attacks propagate across cloud services and (2) how adversaries leverage vulnerabilities in cloud services to attack hosts. Using this information, we review the application of three MAC approaches employed by "secure" commercial systems to evaluate their practical effectiveness for controlling cloud services. While MAC enforcement can improve security for cloud services, several threats remain unchecked. We outline a set of additional security policy goals that a SCOS must enforce to control threats from potentially compromised cloud services comprehensively. While we do not actually construct a SCOS in this paper, we hope that this study will initiate discussions that may lead to practical designs.

Dynamic requests of viewers from sparse and dispersed locations for crowdsourced-live-streaming (CSLS) service make current cloud service providers (CSPs) inadequate to provide sufficient quality of experience (QoE). To solve this issue, we propose a multi-CDN-assisted-CSLS (MCACLS) architecture, a novel cloud architecture complemented by multiple content delivery networks (Multi-CDNs). MCACLS architecture can enhance a CSP’s capacity of video distribution service and improve the quality of CSLS service for end-users while reducing the overall operational cost. MCACLS adaptively adjusts resources between a CSP and its leased CDN service in a fine granularity to deal with the volatility of user requests. However, scheduling resources cost-effectively in response to user requests from different regions is a critical issue that must be addressed. We formulate the above problem into a constrained stochastic optimization problem and propose an algorithm based on the Nash bargaining solution. Our proposed algorithm makes tradeoff between QoE of users and the overall operational cost for CSPs. Illustrative studies validate the advantages of MCACLS and show that it is more cost-effective, reducing the overall operational cost by up to 15% compared with other alternatives while achieving sufficient QoE for viewers.

An increasing number of applications are being hosted on cloud based platforms. Cloud platforms are serving as a general computing facility and applications being hosted on these platforms range from simple multi-tier web applications to complex social networking, eCommerce and Big Data applications. High availability, performance and auto-scaling are key requirements of Cloud based applications. Cloud platforms serve these requirements using dynamic provisioning of resources in on-demand, multi-tenant fashion. A key challenge for cloud service providers is to ensure the Quality of Service (QoS), as a user / customer requires more explicit guarantees of QoS for provisioning of services. Cloud service performance problems can directly lead to extensive financial loses. Thus, control and verification of QoS become a vital concern for any production level deployment. Therefore, it is crucial to address performance as a managed objective. The success of cloud services depends critically on automated problem diagnostics and predictive analytics enabling organizations to manage their performance proactively. Moreover, effective and advance monitoring is equally important for performance management support in clouds. In this thesis, we explore the key techniques for developing monitoring and performance management systems to achieve robust cloud systems. At first, two case studies are presented as a motivation for the need of a scalable monitoring and analytics framework. It includes a case study on performance issues of a software service, which is hosted on a virtualized platform. In the second case study, cloud services are analyzed that are offered by a large IT service provider. A generalization of case studies forms the basis for the requirement specifications which are used for state-of-the-art analysis. Although, some solutions for particular challenges have already been provided, a scalable approach for performance problem diagnosis and prediction is still missing. For addressing this issue, a distributed scalable monitoring and analytics framework is presented in the first part of this thesis. We conducted a thorough analysis of technologies to be used by our framework. The framework makes use of existing monitoring and analytics technologies. However, we develop custom collectors to retrieve data non-intrusively from different layers of cloud. In addition, we develop the analytics subscriber and publisher components to retrieve service related events from different APIs and sends alerts to the SLA Management component for taking corrective measures. Further, we implemented an Open Cloud Computing Interface (OCCI) monitoring extension using OCCI Mixin mechanism. To deal with performance problem diagnosis, a novel distributed parallel approach for performance anomaly detection is presented. First all anomalous metrics are found from a distributed database of time-series for a particular window. For comparative analysis three light-weight statistical anomaly detection techniques are selected. We extend these techniques to work with MapReduce paradigm and assess and compare the methods in terms of precision, recall, execution time, speedup and scale up. Next, we correlate the anomalous metrics with the target SLO in order to locate the suspicious metrics. We implemented and evaluated our approach on a production Cloud encompassing Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) service models. Experimental results confirm that our approach is efficient and effective in capturing the metrics causing performance anomalies. Finally, we present the design and implementation of an online anomaly prediction system for cloud computing infrastructures. We further present an experimental evaluation of a set of anomaly prediction methods that aim at predicting upcoming periods of high utilization or poor performance with enough lead time to enable the appropriate scheduling, scaling, and migration of virtual resources. Using real data sets gathered from Cloud platforms of a university data center, we compare several approaches ranging from time-series (e.g. auto regression (AR)) to statistical classification methods (e.g. Bayesian classifier). We observe that linear time-series models, especially AR models, are most likely suitable to model QoS measures and forecast their future values. Moreover, linear time-series models can be integrated with Machine Learning (ML) methods to improve proactive QoS management.

Cloud computing is a pervasive technology and platform in IT for several years. Cloud service providers have developed and offered different service platforms to accommodate different needs of enterprise subscribers. However, there still exists the situation of enterprise customers' hesitation and reluctance to deploy their core applications using cloud service platforms. The term data visibility has been widely used in the IT industry especially from ICT product and solution vendors. However, there is not any practice guideline, nor standard in industry to define this term. This paper defined the characteristic and dimensions of data visibility, from conceptual model to framework architecture of customer centric data visibility (CCDV) on cloud platform. It propose to apply CCDV as reference model or practice guideline on cloud computing service, with enhancement of data visibility which can earn the trust from enterprise customer in adopting public cloud service.

A service level agreement (SLA) is part of a contract between a cloud service provider (CSP) and a cloud service user (CSU) which defines and describes different agreed properties of the service. The negotiation of the SLA between a CSU and a CSP is a pivotal process in the SLA life cycle. An SLA may include many negotiable quality of service (QoS) parameters with each QoS parameter having multiple acceptable values. Different combinations of acceptable values for all QoS parameters grow exponentially as number of QoS parameters and their selectable values increase. Manually negotiating for all of the QoS parameters considering their various options can be an erroneous and diligent task with lower utility. In this paper, a time efficient flip-flop negotiation strategy for the SLAs in cloud services is presented. The flip-flop negotiation strategy is based on a time dependent 3D utility function and on a dynamic concession strategy using the polynomial extrapolation of opponent's concession. This negotiation strategy is useful for time-critical cloud services as it operates with a motive of reaching an agreement at earlier time without compromising over the utility level of the agreement. Moreover, a multi-provider concurrent negotiation method is presented in this work using the flip-flop negotiation strategy. Multiple experiments are performed (using the implementation of flip-flop negotiation strategy) to test the presented work. The results of the experiments enforce the validity of the flip-flop negotiation strategy for such cloud services where time is an important factor during the SLA negotiation process.

ABSTRACTCloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.