An Optimal Trade-off between Content Freshness and Refresh Cost

Caching is an effective mechanism for reducing bandwidth usage and alleviating server load. However, the use of caching entails a compromise between content freshness and refresh cost. An excessive refresh allows a high degree of content freshness at a greater cost of system resource. Conversely, a deficient refresh inhibits content freshness but saves the cost of resource usages. To address the freshness-cost problem, we formulate the refresh scheduling problem with a generic cost model and use this cost model to determine an optimal refresh frequency that gives the best tradeoff between refresh cost and content freshness. We prove the existence and uniqueness of an optimal refresh frequency under the assumptions that the arrival of content update is Poisson and the age-related cost monotonically increases with decreasing freshness. In addition, we provide an analytic comparison of system performance under fixed refresh scheduling and random refresh scheduling, showing that with the same average refresh frequency two refresh schedulings are mathematically equivalent in terms of the long-run average cost

Deterministic Object Management in Large Distributed Systems

Caching is a widely used technique to improve the scalability of distributed systems. A central issue with caching is maintaining object replicas consistent with their master copies. Large distributed systems, such as the Web, typically deploy heuristic-based consistency mechanisms, which increase delay and place extra load on the servers, while not providing guarantees that cached copies served to clients are up-to-date. Server-driven invalidation has been proposed as an approach to strong cache consistency, but it requires servers to keep track of which objects are cached by which clients. We propose an alternative approach to strong cache consistency, called MONARCH, which does not require servers to maintain per-client state. Our approach builds on a few key observations. Large and popular sites, which attract the majority of the traffic, construct their pages from distinct components with various characteristics. Components may have different content types, change characteristics, and semantics. These components are merged together to produce a monolithic page, and the information about their uniqueness is lost. In our view, pages should serve as containers holding distinct objects with heterogeneous type and change characteristics while preserving the boundaries between these objects. Servers compile object characteristics and information about relationships between containers and embedded objects into explicit object management commands. Servers piggyback these commands onto existing request/response traffic so that client caches can use these commands to make object management decisions. The use of explicit content control commands is a deterministic, rather than heuristic, object management mechanism that gives content providers more control over their content. The deterministic object management with strong cache consistency offered by MONARCH allows content providers to make more of their content cacheable. Furthermore, MONARCH enables content providers to expose internal structure of their pages to clients. We evaluated MONARCH using simulations with content collected from real Web sites. The results show that MONARCH provides strong cache consistency for all objects, even for unpredictably changing ones, and incurs smaller byte and message overhead than heuristic policies. The results also show that as the request arrival rate or the number of clients increases, the amount of server state maintained by MONARCH remains the same while the amount of server state incurred by server invalidation mechanisms grows

Adaptive Pull-Based Data Freshness Policies for Diverse Update Patterns

An important challenge to effective data delivery in wide area environments is maintaining the data freshness of objects using solutions that can scale to a large number of clients without incurring significant server overhead. Policies for maintaining data freshness are traditionally either push-based or pull-based. Push-based policies involve pushing data updates by servers; they may not scale to a large number of clients. Pull-based policies require clients to contact servers to check for updates; their effectiveness is limited by the difficulty of predicting updates. Models to predict updates generally rely on some knowledge of past updates. Their accuracy of prediction may vary and determining the most appropriate model is non-trivial. In this paper, we present an adaptive pull-based solution to this challenge. We first present several techniques that use update history to estimate the freshness of cached objects, and identify update patterns for which each technique is most effective. We then introduce adaptive policies that can (automatically) choose a policy for an object based on its observed update patterns. Our proposed policies improve the freshness of cached data and reduce costly contacts with remote servers without incurring the large server overhead of push-based policies, and can scale to a large number of clients. Using trace data from a data-intensive website as well as two email logs, we show that our adaptive policies can adapt to diverse update patterns and provide significant improvement compared to a single policy. (UMIACS-TR-2004-01

Optimization and Evaluation of Service Speed and Reliability in Modern Caching Applications

The performance of caching systems in general, and Internet caches in particular, is evaluated by means of the user-perceived service speed, reliability of downloaded content, and system scalability. In this dissertation, we focus on optimizing the speed of service, as well as on evaluating the reliability and quality of data sent to users. In order to optimize the service speed, we seek optimal replacement policies in the first part of the dissertation, as it is well known that download delays are a direct product of document availability at the cache; in demand-driven caches, the cache content is completely determined by the cache replacement policy. In the literature, many ad-hoc policies that utilize document sizes, retrieval latency, probability of references, and temporal locality of requests, have been proposed. However, the problem of finding optimal policies under these factors has not been pursued in any systematic manner. Here, we take a step in that direction: Still under the Independent Reference Model, we show that a simple Markov stationary policy minimizes the long-run average metric induced by non-uniform documents under optional cache replacement. We then use this result to propose a framework for operating caches under multiple performance metrics, by solving a constrained caching problem with a single constraint. The second part of the dissertation is devoted to studying data reliability and cache consistency issues: A cache object is termed consistent if it is identical to the master document at the origin server, at the time it is served to users. Cached objects become stale after the master is modified, and stale copies remain served to users until the cache is refreshed, subject to network transmit delays. However, the performance of Internet consistency algorithms is evaluated through the cache hit rate and network traffic load that do not inform on data staleness. To remedy this, we formalize a framework and the novel hit* rate measure, which captures consistent downloads from the cache. To demonstrate this new methodology, we calculate the hit and hit* rates produced by two TTL algorithms, under zero and non-zero delays, and evaluate the hit and hit* rates in applications

POLICY-BASED MIDDLEWARE FOR MOBILE CLOUD COMPUTING

Mobile devices are the dominant interface for interacting with online services as well as an efficient platform for cloud data consumption. Cloud computing allows the delivery of applications/functionalities as services over the internet and provides the software/hardware infrastructure to host these services in a scalable manner. In mobile cloud computing, the apps running on the mobile device use cloud hosted services to overcome resource constraints of the host device. This approach allows mobile devices to outsource the resource-consuming tasks. Furthermore, as the number of devices owned by a single user increases, there is the growing demand for cross-platform application deployment to ensure a consistent user experience. However, the mobile devices communicate through unstable wireless networks, to access the data and services hosted in the cloud. The major challenges that mobile clients face when accessing services hosted in the cloud, are network latency and synchronization of data. To address the above mentioned challenges, this research proposed an architecture which introduced a policy-based middleware that supports user to access cloud hosted digital assets and services via an application across multiple mobile devices in a seamless manner. The major contribution of this thesis is identifying different information, used to configure the behavior of the middleware towards reliable and consistent communication among mobile clients and the cloud hosted services. Finally, the advantages of the using policy-based middleware architecture are illustrated by experiments conducted on a proof-of-concept prototype

A Dual Framework and Algorithms for Targeted Data Delivery

A variety of emerging wide area applications challenge existing techniques for data delivery to users and applications accessing data from multiple autonomous servers. In this paper, we develop a framework for comparing pull based solutions and present dual optimization approaches. Informally, the first approach maximizes user utility of profiles while satisfying constraints on the usage of system resources. The second approach satisfies the utility of user profiles while minimizing the usage of system resources. We present a static optimal solution (SUP) for the latter approach and formally identify sufficient conditions for SUP to be optimal for both. A shortcoming of static solutions to pull-based delivery is that they cannot adapt to the dynamic behavior of Web source updates. Therefore, we present an adaptive algorithm (fbSUP) and show how it can incorporate feedback to improve user utility with only a moderate increase in probing. Using real and synthetic data traces, we analyze the behavior of SUP and fbSUP under various update models

User-activity aware strategies for mobile information access

Information access suffers tremendously in wireless networks because of the low correlation between content transferred across low-bandwidth wireless links and actual data used to serve user requests. As a result, conventional content access mechanisms face such problems as unnecessary bandwidth consumption and large response times, and users experience significant performance degradation. In this dissertation, we analyze the cause of those problems and find that the major reason for inefficient information access in wireless networks is the absence of any user-activity awareness in current mechanisms. To solve these problems, we propose three user-activity aware strategies for mobile information access. Through simulations and implementations, we show that our strategies can outperform conventional information access schemes in terms of bandwidth consumption and user-perceived response times.Ph.D.Committee Chair: Raghupathy Sivakumar; Committee Member: Chuanyi Ji; Committee Member: George Riley; Committee Member: Magnus Egerstedt; Committee Member: Umakishore Ramachandra

Digital Preservation of Newspapers: Findings of the Chronicles in Preservation Project

In this paper, the authors describe research led by Educopia Institute regarding the preservation needs for digitized and born-digital newspapers. The 'Chronicles in Preservation' project, builds upon previous efforts (e.g. the U.S. National Digital Newspaper Program) to look more broadly at the needs of digital newspapers in all of their diverse and challenging forms. This paper conveys the findings of the first research phase, including substantive survey results regarding digital newspaper curation practices