Modeling, identifying, and emulating dynamic adaptive streaming over HTTP

As HTTP-based streaming video applications have grown to become a major source of Internet traffic, and as the new ISO standard Dynamic Adaptive Streaming over HTTP (DASH) gains industry acceptance, researchers need the ability to both (i) study real-world viewing data and (ii) emulate realistic DASH streams in network experiments. The first effort is complicated by the fact that researchers are often restricted to anonymized, header-only (i.e. payload-truncated) traces. The second effort is difficult since the process of encoding videos for DASH results in numerous large files and since popular videos are subject to restrictive copyright law. In this thesis we present our work towards developing a model for DASH traffic and show how the model can be applied to identify specific DASH videos in anonymized, header-only traces. We also present our solution for emulating DASH using compact representations of both DASH services (e.g. Netflix and Amazon) and videos.Master of Scienc

Effective and Economical Content Delivery and Storage Strategies for Cloud Systems

Cloud computing has proved to be an effective infrastructure to host various applications and provide reliable and stable services. Content delivery and storage are two main services provided by the cloud. A high-performance cloud can reduce the cost of both cloud providers and customers, while providing high application performance to cloud clients. Thus, the performance of such cloud-based services is closely related to three issues. First, when delivering contents from the cloud to users or transferring contents between cloud datacenters, it is important to reduce the payment costs and transmission time. Second, when transferring contents between cloud datacenters, it is important to reduce the payment costs to the internet service providers (ISPs). Third, when storing contents in the datacenters, it is crucial to reduce the file read latency and power consumption of the datacenters. In this dissertation, we study how to effectively deliver and store contents on the cloud, with a focus on cloud gaming and video streaming services. In particular, we aim to address three problems. i) Cost-efficient cloud computing system to support thin-client Massively Multiplayer Online Game (MMOG): how to achieve high Quality of Service (QoS) in cloud gaming and reduce the cloud bandwidth consumption; ii) Cost-efficient inter-datacenter video scheduling: how to reduce the bandwidth payment cost by fully utilizing link bandwidth when cloud providers transfer videos between datacenters; iii) Energy-efficient adaptive file replication: how to adapt to time-varying file popularities to achieve a good tradeoff between data availability and efficiency, as well as reduce the power consumption of the datacenters. In this dissertation, we propose methods to solve each of aforementioned challenges on the cloud. As a result, we build a cloud system that has a cost-efficient system to support cloud clients, an inter-datacenter video scheduling algorithm for video transmission on the cloud and an adaptive file replication algorithm for cloud storage system. As a result, the cloud system not only benefits the cloud providers in reducing the cloud cost, but also benefits the cloud customers in reducing their payment cost and improving high cloud application performance (i.e., user experience). Finally, we conducted extensive experiments on many testbeds, including PeerSim, PlanetLab, EC2 and a real-world cluster, which demonstrate the efficiency and effectiveness of our proposed methods. In our future work, we will further study how to further improve user experience in receiving contents and reduce the cost due to content transfer

Decentralising resource management in operating systems

This dissertation explores operating system mechanisms to allow resource-aware applications to be involved in the process of managing resources under the premise that these applications (1) potentially have some (implicit) notion of their future resource demands and (2) can adapt their resource demands. The general idea is to provide feedback to resource-aware applications so that they can proactively participate in the management of resources. This approach has the benefit that resource management policies can be removed from central entities and the operating system has only to provide mechanism. Furthermore, in contrast to centralised approaches, application specific features can be more easily exploited. To achieve this aim, I propose to deploy a microeconomic theory, namely congestion or shadow pricing, which has recently received attention for managing congestion in communication networks. Applications are charged based on the potential "damage" they cause to other consumers by using resources. Consumers interpret these congestion charges as feedback signals which they use to adjust their resource consumption. It can be shown theoretically that such a system with consumers merely acting in their own self-interest will converge to a social optimum. This dissertation focuses on the operating system mechanisms required to decentralise resource management this way. In particular it identifies four mechanisms: pricing & charging, credit accounting, resource usage accounting, and multiplexing. While the latter two are mechanisms generally required for the accurate management of resources, pricing & charging and credit accounting present novel mechanisms. It is argued that congestion prices are the correct economic model in this context and provide appropriate feedback to applications. The credit accounting mechanism is necessary to ensure the overall stability of the system by assigning value to credits

BORG: Block-reORGanization and Self-optimization in Storage Systems

This paper presents the design, implementation, and evaluation of BORG, a self-optimizing storage system that performs automatic block reorganization based on the observed I/O workload. BORG is motivated by three characteristics of I/O workloads: non-uniform access frequency distribution, temporal locality, and partial determinism in non-sequential accesses. To achieve its objective, BORG manages a small, dedicated partition on the disk drive, with the goal of servicing a majority of the I/O requests from within this partition with significantly reduced seek and rotational delays. BORG is transparent to the rest of the storage stack, including applications, file system(s), and I/O schedulers, thereby requiring no or minimal modification to storage stack implementations. We evaluated a Linux implementation of BORG using several real-world workloads, including individual user desktop environments, a web-server, a virtual machine monitor, and an SVN server. These experiments comprehensively demonstrate BORG’s effectiveness in improving I/O performance and its incurred resource overhead

HAPPE: Human and Application-Driven Frequency Scaling for Processor Power Efficiency

Abstract-Conventional dynamic voltage and frequency scaling techniques use high CPU utilization as a predictor for user dissatisfaction, to which they react by increasing CPU frequency. In this paper, we demonstrate that for many interactive applications, perceived performance is highly dependent upon the particular user and application, and is not linearly related to CPU utilization. This observation reveals an opportunity for reducing power consumption. We propose Human and Application driven frequency scaling for Processor Power Efficiency (HAPPE), an adaptive user-and-application-aware dynamic CPU frequency scaling technique. HAPPE continuously adapts processor frequency and voltage to the learned performance requirement of the current user and application. Adaptation to user requirements is quick and requires minimal effort from the user (typically a handful of key strokes). Once the system has adapted to the user's performance requirements, the user is not required to provide continued feedback but is permitted to provide additional feedback to adjust the control policy to changes in preferences. HAPPE was implemented on a Linux-based laptop and evaluated in 22 hours of controlled user studies. Compared to the default Linux CPU frequency controller, HAPPE reduces the measured system-wide power consumption of CPU-intensive interactive applications by 25 percent on average while maintaining user satisfaction. Index Terms-Power, CPU frequency scaling, user-driven study, mobile systems Ç 1I NTRODUCTION P OWER efficiency has been a major technology driver for battery-powered mobile systems, such as mobile phones, personal digital assistants, MP3 players, and laptops. Power efficiency has also become a new focus for line-powered desktop systems and data centers because of its impact on power dissipation and chip temperature, which affect performance, reliability, and lifetime. Processor power consumption is often a substantial portion of system power consumption in mobile systems Traditional CPU power management approaches can lose sight of an important fact: The ultimate goal of any computer system is to satisfy its users, not to execute a particular number of instructions per second. Although CPU utilization is a good indication of processor performance, the actual perceivable system performance depends on individual users and applications, and user satisfaction is not linearly related to CPU utilization. We conducted a study on 10 users with four interactive applications and found that for some applications, some users are satisfied with system performance when the processor is at the lowest frequency, while other users may not be satisfied even when it operates at the highest frequency. We also found that users may be insensitive to varying processor frequency for one application, but may be very sensitive to such changes for another application. Traditional DVFS policies that consider only CPU utilization or other useroblivious performance metrics are often too pessimistic about user performance requirements, and use a high frequency to satisfy all users, resulting in wasted power. Similar findings were also reported in other studies In this paper, we propose Human and Application driven frequency scaling for Processor Power Efficiency (HAPPE), a CPU DVFS technique that adapts voltage and frequency to the performance requirement of the curren