Di Niu

Gearing resource-poor mobile devices with powerful clouds: architectures, challenges, and applications

Mobile cloud computing, with its promise to meet the urgent need for richer applications and services of resource-constrained mobile devices, is emerging as a new computing paradigm and has recently attracted significant attention. However, there is no clear definition and no well defined scope for mobile cloud computing due to commercial hype, and diverse ways of combining cloud computing and mobile applications. This article makes the first attempt to present a survey of mobile cloud computing from the perspective of its intended usages. Specifically, we introduce three common mobile cloud architectures and classify comprehensive existing work into two fundamental categories: computation offloading and capability extending. Considering the energy bottleneck and user context of mobile devices, we discuss the research challenges and opportunities of introducing cloud computing to assist mobile devices, including energy-efficient interactions, virtual machine migration overhead, privacy, and security. Moreover, we demonstrate three real-world applications enabled by mobile cloud computing, in order to stimulate further discussion and development of this emerging field.

Quality-assured cloud bandwidth auto-scaling for video-on-demand applications

There has been a recent trend that video-on-demand (VoD) providers such as Netflix are leveraging resources from cloud services for multimedia streaming. In this paper, we consider the scenario that a VoD provider can make reservations for bandwidth guarantees from cloud service providers to guarantee the streaming performance in each video channel. We propose a predictive resource auto-scaling system that dynamically books the minimum bandwidth resources from multiple data centers for the VoD provider to match its short-term demand projections. We exploit the anti-correlation between the demands of video channels for statistical multiplexing and for hedging the risk of under-provision. The optimal load direction from channels to data centers is derived with provable performance. We further provide suboptimal solutions that balance bandwidth and storage costs. The system is backed up by a demand predictor that forecasts the demand expectation, volatility and correlations based on learning. Extensive simulations are conducted driven by the workload traces from a commercial VoD system.

Demand forecast and performance prediction in peer-assisted on-demand streaming systems

Peer-assisted on-demand video streaming services are extremely large-scale distributed systems on the Internet. Automated demand forecast and performance prediction, if implemented, can help with capacity planning and quality control so that sufficient server bandwidth can always be supplied to each video channel without incurring wastage. In this paper, we use time-series analysis techniques to automatically predict the online population, the peer upload and the server bandwidth demand in each video channel, based on the learning of both human factors and system dynamics from online measurements. The proposed mechanisms are evaluated on a large dataset collected from a commercial Internet video-on-demand system.

Pricing cloud bandwidth reservations under demand uncertainty

In a public cloud, bandwidth is traditionally priced in a pay-as-you-go model. Reflecting the recent trend of augmenting cloud computing with bandwidth guarantees, we consider a novel model of cloud bandwidth allocation and pricing when explicit bandwidth reservation is enabled. We argue that a tenants utility depends not only on its bandwidth usage, but more importantly on the portion of its demand that is satisfied with a performance guarantee. Our objective is to determine the optimal policy for pricing cloud bandwidth reservations, in order to maximize social welfare, i.e., the sum of the expected profits that can be made by all tenants and the cloud provider, even with the presence of demand uncertainty. The problem turns out to be a large-scale network optimization problem with a coupled objective function. We propose two new distributed solutions --- based on chaotic equation updates and cutting-plane methods --- that prove to be more efficient than existing solutions based on consistency pricing and subgradient methods. In addition, we address the practical challenge of forecasting demand statistics, required by our optimization problem as input. We propose a factor model for near-future demand prediction, and test it on a real-world video workload dataset. All included, we have designed a fully computerized trading environment for cloud bandwidth reservations, which operates effectively at a fine granularity of as small as ten minutes in our trace-driven simulations.

Dynamic Cloud Resource Reservation via Cloud Brokerage

Infrastructure-as-a-Service clouds offer diverse pricing options, including on-demand and reserved instances with various discounts to attract different cloud users. A practical problem facing cloud users is how to minimize their costs by choosing among different pricing options based on their own demands. In this paper, we propose a new cloud brokerage service that reserves a large pool of instances from cloud providers and serves users with price discounts. The broker optimally exploits both pricing benefits of long-term instance reservations and multiplexing gains. We propose dynamic strategies for the broker to make instance reservations with the objective of minimizing its service cost. These strategies leverage dynamic programming and approximate algorithms to rapidly handle large volumes of demand. Our extensive simulations driven by large-scale Google cluster-usage traces have shown that significant price discounts can be realized via the broker.

Random Network Coding in Peer-to-Peer Networks: From Theory to Practice

With random network coding, network nodes between the source and receivers are able to not only relay and replicate data packets, but also code them using randomly generated coding coefficients. From a theoretical perspective, it has been recognized that network coding maximizes the network flow rates in multicast sessions in directed acyclic network graphs. To date, random network coding has seen practical and real-world applications in peer-to-peer (P2P) networks, in which overlay network topologies are formed among participating end hosts, called “peers.” Due to uncertainties and dynamics involved with peer arrivals and departures, these network topologies are usually randomly generated in practice, and are referred to as “random mesh” topologies. Unlike structured topologies such as trees, random mesh topologies are practical to be implemented, and are resilient to the level of volatility typically experienced in peer-to-peer networks. It has been shown, from both theoretical and practical perspectives, that random network coding leads to performance benefits in these peer-to-peer networks with random mesh topologies. This paper presents a survey of existing results with respect to practical applications of random network coding in peer-to-peer networks. We focus on bulk content distribution and media streaming systems, as well as the computational overhead introduced by random network coding in modern off-the-shelf servers and mobile devices. Throughout the paper, we also show theoretical insights on why random network coding may become beneficial in practice.

A theory of cloud bandwidth pricing for video-on-demand providers

Current-generation cloud computing is offered with usage-based pricing, with no bandwidth capacity guarantees, which is however unappealing to bandwidth-intensive applications such as video-on-demand (VoD). We consider a new type of service where VoD providers, such as Netflix and Hulu, make reservations for bandwidth guarantees from the cloud at negotiable prices to support continuous media streaming. We argue that it is beneficial to multiplex such bandwidth reservations in the market using a profit-making broker while controlling the performance risks. We ask the question-in such a market, how much should each VoD provider pay for bandwidth reservation? We prove that the market has a unique Nash equilibrium where the bandwidth reservation price for a VoD provider critically depends on its demand correlation to the market. Real-world traces verify that our theory can significantly lower the market price for cloud bandwidth reservation.

Understanding demand volatility in large VoD systems

Bandwidth usage in large-scale Video on Demand (VoD) systems varies rapidly over time, due to unpredictable dynamics in user demand and network conditions. Such bandwidth volatility makes it hard to provision the exact amount of server resources that matches the demand in each video channel, posing significant challenges to achieving quality assurance and efficient resource allocation at the same time. In this paper, we seek to statistically model time-varying traffic volatility in VoD servers, leveraging heteroscedastic models first used to interpret economic time series, with the goal of forecasting not only traffic patterns but also traffic volatility. We present the application of volatility forecast to efficient resource allocation that provides probabilistic service level guarantees to user groups. We also discuss volatility reduction from diversification, and its implications to new strategies for cost-effective server management. Our study is based on monitoring the workload of a large-scale commercial VoD system widely deployed on the Internet.

An efficient distributed algorithm for resource allocation in large-scale coupled systems

In modern large-scale systems, fast distributed resource allocation and utility maximization are becoming increasingly important. Traditional solutions to such problems rely on primal/dual decomposition and gradient methods, whose convergence is sensitive to the choice of the stepsize and may not be sufficient to satisfy the requirement of large-scale real-time applications. We propose a new iterative approach to distributed resource allocation in coupled systems. Without complicating message-passing, the new approach is robust to parameter choices and expedites convergence by exploiting problem structures. We theoretically analyze the asynchronous algorithm convergence conditions, and empirically evaluate its benefits in a case of cloud network resource reservation based on real-world data.

On the Resilience-Complexity Tradeoff of Network Coding in Dynamic P2P Networks

Most current-generation P2P content distribution protocols use line-granularity blocks to distribute content to all the peers in a decentralized fashion. Such protocols often suffer from a significant degree of imbalance in block distributions, such that certain blocks become rare or even unavailable, adversely affecting content availability. It has been pointed out that randomized network coding may improve block availability in P2P networks, as coded blocks are equally innovative and useful to peers. However, the computational complexity of network coding mandates that, in reality, network coding needs to be performed within segments, each containing a subset of blocks. In this paper, using both theoretical analysis and simulations, we quantitatively evaluate how segment-based network coding may improve resilience to peer dynamics and content availability. The objective of this paper is to explore the fundamental tradeoff between the resilience gain of network coding and its inherent coding complexity. We introduce a differential equations approach to quantify the resilience gain of network coding as a function of the number of blocks in a segment, as well as various other tunable parameters. We conclude that a small number of blocks in each segment is sufficient to realize the major benefits of network coding, with acceptable coding complexity.