Shudong Jin

Popularity-aware greedy dual-size Web proxy caching algorithms

Web caching aims at reducing network traffic, server load and user-perceived retrieval delays by replicating popular content on proxy caches that are strategically placed within the network. While key to effective cache utilization, popularity information (e.g. relative access frequencies of objects requested through a proxy) is seldom incorporated directly in cache replacement algorithms. Rather other properties of the request stream (e.g. temporal locality and content size), which are easier to capture in an online fashion, are used to indirectly infer popularity information, and hence drive cache replacement policies. Recent studies suggest that the correlation between these secondary properties and popularity is weakening due in part to the prevalence of efficient client and proxy caches. This trend points to the need for proxy cache replacement algorithms that directly capture popularity information. We present an on-line algorithm that effectively captures and maintains an accurate popularity profile of Web objects requested through a caching proxy. We propose a novel cache replacement policy that uses such information to generalize the well-known greedy dual-size algorithm, and show the superiority of our proposed algorithm by comparing it to a host of recently-proposed and widely-used algorithms using extensive trace-driven simulations and a variety of performance metrics.

A hierarchical characterization of a live streaming media workload

We present a thorough characterization of what we believe to be the first significant live Internet streaming media workload in the scientific literature. Our characterization of over 3.5 million requests spanning a 28-day period is done at three increasingly granular levels, corresponding to clients, sessions, and transfers. Our findings support two important conclusions. First, we show that the nature of interactions between users and objects is fundamentally different for live versus stored objects. Access to stored objects is user driven, whereas access to live objects is object driven. This reversal of active/passive roles of users and objects leads to interesting dualities. For instance, our analysis underscores a Zipf-like profile for user interest in a given object, which is in contrast to the classic Zipf-like popularity of objects for a given user. Also, our analysis reveals that transfer lengths are highly variable and that this variability is due to client stickiness to a particular live object, as opposed to structural (size) properties of objects. Second, by contrasting two live streaming workloads from two radically different applications, we conjecture that some characteristics of live media access workloads are likely to be highly dependent on the nature of the live content being accessed. This dependence is clear from the strong temporal correlation observed in the traces, which we attribute to the impact of synchronous access to live content. Based on our analysis, we present a model for live media workload generation that incorporates many of our findings, and which we implement in GISMO.

G ISMO : a Generator of Internet Streaming Media Objects and workloads

This paper presents a tool called GISMO (Generator of Internet Streaming Media Objects and workloads). GISMO enables the specification of a number of streaming media access characteristics, including object popularity, temporal correlation of request, seasonal access patterns, user session durations, user inter-activity times, and variable bit-rate (VBR) self-similarity and marginal distributions. The embodiment of these characteristics in GISMO enables the generation of realistic and scalable request streams for use in the benchmarking and comparative evaluation of Internet streaming media delivery techniques. To demonstrate the usefulness of GISMO, we present a case study that shows the importance of various workload characteristics in determining the effectiveness of proxy caching and server patching techniques in reducing bandwidth requirements.

A spectrum of TCP-friendly window-based congestion control algorithms

The increasing diversity of Internet application requirements has spurred recent interest in transport protocols with flexible transmission controls. In window-based congestion control schemes, increase rules determine how to probe available bandwidth, whereas decrease rules determine how to back off when losses due to congestion are detected. The control rules are parameterized so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and loss rate. This paper presents a comprehensive study of a new spectrum of window-based congestion controls, which are TCP-friendly as well as TCP-compatible under RED. Our controls utilize history information in their control rules. By doing so, they improve the transient behavior, compared to recently proposed slowly responsive congestion controls such as general additive-increase and multiplicative-decrease (AIMD) and binomial controls. Our controls can achieve better tradeoffs among smoothness, aggressiveness, and responsiveness, and they can achieve faster convergence. We demonstrate analytically and through extensive ns simulations the steady-state and transient behavior of several instances of this new spectrum.

Sources and characteristics of Web temporal locality

Temporal locality of reference in Web request streams emerges from two distinct phenomena: the long-term popularity of Web documents and the short-term temporal correlations of references. We show that the commonly-used distribution of inter-request times is predominantly determined by the power law governing the long-term popularity of documents. This inherent relationship tends to disguise the existence of short-term temporal correlations. We propose a new and robust metric that enables accurate characterization of that aspect of temporal locality. Using this metric, we characterize the locality of reference in a number of representative proxy cache traces. Our findings show that there are measurable differences between the degrees (and sources) of temporal locality across these traces.

Accelerating Internet streaming media delivery using network-aware partial caching

Internet streaming applications are affected by adverse network conditions such as high packet loss rates and long delays. This paper aims at mitigating such effects by leveraging the availability of client-side caching proxies. We present a novel caching architecture and associated cache management algorithms that turn edge caches into accelerators of streaming media delivery. A salient feature of our caching algorithms is that they allow partial caching of streaming media objects and joint delivery of content from caches and origin servers. The caching algorithms we propose are both network-aware and stream-aware; they take into account the popularity of streaming media objects, their bit-rate requirements, and the available bandwidth between clients and servers. Using realistic models of Internet bandwidth derived from proxy cache logs and measured over real Internet paths, we have conducted simulations to evaluate the performance of various cache management alternatives. Our experiments demonstrate that network-aware caching algorithms can significantly reduce service delay and improve overall stream quality. Our experiments also show that partial caching is particularly effective when bandwidth variability is not very high.

TCP-friendly SIMD congestion control and its convergence behavior

The increased diversity of Internet application requirements has spurred interest in flexible congestion control mechanisms. Window-based congestion control schemes use increase rules to probe available bandwidth, and decrease rules to back off when congestion is detected. The control rules are parameterized so as to ensure that the resulting protocol is TCP-friendly in terms of the relationship between throughput and packet loss rate. We propose a novel window-based congestion control algorithm called SIMD (Square-Increase/Multiplicative-Decrease). Contrary to previous memoryless controls, SIMD utilizes history information in its control rules. It uses multiplicative decrease but the increase in window size is in proportion to the square of the time elapsed since the detection of the last loss event. Thus, SIMD can efficiently probe available bandwidth. Nevertheless, SIMD is TCP-friendly as well as TCP-compatible through RED routers. Furthermore, SIMD has much better convergence behavior than TCP-friendly AIMD and binomial algorithms proposed previously.

Small-world characteristics of internet topologies and implications on multicast scaling

Recent work has shown that the physical connectivity of the Internet exhibits small-world behavior. Characterizing such behavior is important not only for generating realistic Internet topology, but also for the proper evaluation of large-scale content delivery mechanisms. Along this line, this paper tries to understand how small-world behavior arises in the Internet topologies and how it impacts the performance of multicast techniques. First, we attribute small-world behavior to two possible causes-namely the variability of vertex degree and the preference for local connections for vertices. We have found that both factors contribute with different relative degrees to the small-world behavior of autonomous system (AS) level and router level Internet topologies. For AS level topology, we observe that high variability of vertex degree is sufficient to cause small-world behavior, but for router level topology, preference for local connectivity plays a more important role. Second, we propose better models to generate small-world Internet topologies. Our models incorporate both causes of small-world behavior, and generate graphs closely resemble real Internet graphs. Third, using simulation we demonstrate the importance of our work by studying the scaling behavior of multicast techniques. We show that multicast tree size largely depends on network topology. If topology generators capture only the variability of vertex degree, they are likely to underestimate the benefit of multicast techniques.

Osmosis: scalable delivery of real-time streaming media in ad-hoc overlay networks

Ad-hoc overlay networks are increasingly used for sharing static bulk content but their promise for scaling the delivery of on-demand, real-time content is yet to be tapped. In this paper, we show that overlay networks could be used efficiently to distribute popular real-time streaming media on-demand to a large number of clients. We propose and evaluate OSMOSIS a cache-and-relay end-system multicast approach, whereby a client joining a multicast session caches the stream, and if needed, relays that stream to neighboring clients which may join the multicast session at some later time. OSCMOSIS is fully distributed, scalable, and efficient in terms of network link costs. We present analytical and empirical results of our evaluation of OSMOSIS. Our analysis establishes OSMOSIS scalability characteristics under a variety of assumptions. Our simulations are over large, synthetic random networks, power-law degree networks, and small-world networks (all of which could well be representative of ad-hoc overlay topologies, as well as over large real router-level Internet maps.

Scalability of multicast delivery for non-sequential streaming access

To serve asynchronous requests using multicast, two categories of techniques---stream merging and periodic broadcasting---have been proposed. For sequential streaming access, where requests are uninterrupted from the beginning to the end of an object, these techniques are highly scalable: the required server bandwidth for stream merging grows logarithmically as request arrival rate, and the required server bandwidth for periodic broadcasting varies logarithmically as the inverse of start-up delay. A sequential access model, however, is inappropriate to model partial requests and client interactivity observed in various streaming access workloads. This paper analytically and experimentally studies the scalability of multicast delivery under a non-sequential access model where requests start at random points in the object. We show that the required server bandwidth for any protocol providing immediate service grows at least as the square root of request arrival rate, and the required server bandwidth for any protocol providing delayed service grows linearly with the inverse of start-up delay. We also investigate the impact of limited client receiving bandwidth on scalability. We optimize practical protocols which provide immediate service to non-sequential requests. The protocols utilize limited client receiving bandwidth, and they are near-optimal in that the required server bandwidth is very close to its lower bound.