The Internet is undergoing substantial changes from a communication and browsing infrastructure to a medium for conducting business and marketing a myriad of services. The World Wide Web provides a uniform and widely-accepted application interface used by these services to reach multitudes of clients. These changes place the web server at the center of a gradually emerging eservice infrastructure with increasing requirements for service quality and reliability guarantees in an unpredictable and highly-dynamic environment.

As Internet services become more popular and pervasive, a critical problem that arises is managing the performance of services under extreme overload. This paper presents a set of techniques for managing overload in complex, dynamic Internet services. These techniques are based on an adaptive admission control mechanism that attempts to bound the 90th-percentile response time of requests flowing through the service. This is accomplished by internally monitoring the performance of the service, which is decomposed into a set of event-driven stages connected with request queues. By controlling the rate at which each stage admits requests, the service can perform focused overload management, for example, by filtering only those requests that lead to resource bottlenecks. We present two extensions of this basic controller that provide class-based service differentiation as well as application-specific service degradation. We evaluate these mechanisms using a complex Webbased e-mail service that is subjected to a realistic user load, as well as a simpler Web server benchmark.

In this paper we address power conservation for clusters of workstations or PCs. Our approach is to develop systems that dynamically turn cluster nodes on -- to be able to handle the load imposed on the system efficiently -- and off -- to save power under lighter load. The key component of our systems is an algorithm that makes load balancing and unbalancing decisions by considering both the total load imposed on the cluster and the power and performance implications of turning nodes off. The algorithm is implemented in two different ways: (1) at the application level for a cluster-based, localityconscious network server; and (2) at the operating system level for an operating system for clustered cycle servers. Our experimental results are very favorable, showing that our systems conserve both power and energy in comparison to traditional systems.

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This paper presents a method for admission control and request scheduling for multiply-tiered e-commerce Web sites, achieving both stable behavior during overload and improved response times. Our method externally observes execution costs of requests online, distinguishing different request types, and performs overload protection and preferential scheduling using relatively simple measurements and a straightforward control mechanism. Unlike previous proposals, which require extensive changes to the server or operating system, our method requires no modifications to the host O.S., Web server, application server or database. Since our method is external, it can be implemented in a proxy. We present such an implementation, called Gatekeeper, using it with standard software components on the Linux operating system. We evaluate the proxy using the industry standard TPC-W workload generator in a typical three-tiered e-commerce environment. We show consistent performance during overload and throughput increases of up to 10 percent. Response time improves by up to a factor of 14, with only a 15 percent penalty to large jobs.

Service differentiation that provides prioritized service qualities to multiple classes of client requests can effectively utilize available server resources. This paper studies how demand-driven service differentiation in terms of end-user performance can be supported in cluster-based network servers. Our objective is to deliver better services to high priority request classes without over-sacrificing low priority classes. To achieve this objective, we propose a dynamic scheduling scheme, called DDSD, that adapts to fluctuating request resource demands by periodically repartitioning servers. This scheme also employs priority-based admission control to drop excessive user requests and achieve soft performance guarantees. For each scheduling period, our scheme monitors the system status and uses a queuing model to approximate server behaviors and guide resource allocation. Our experiments show that the proposed technique achieves demand-driven service differentiation while maximizing resource utilization and that it can substantially outperform static server partitioning.

Achieving crisp interactive response in resource-intensive applications such as augmented reality, language translation, and speech recognition is a major challenge on resource-poor wearable hardware. In this paper we describe a solution based on multi-fidelity computation supported by predictive resource management. We show that such an approach can substantially reduce both the mean and the variance of response time. On a benchmark representative of augmented reality, we demonstrate a 60 % reduction in mean latency and a 30 % reduction in the coefficient of variation. We also show that a history-based approach to demand prediction is the key to this performance improvement. 1

Transcoding is a technique employed by network proxies to dynamically customize multimedia objects for prevailing network conditions and individual client characteristics. Transcoding can be performed along a number of different axes and the specific transcoding technique used depends on the type of multimedia object. Our goal in this paper is to understand the nature of typical Internet images and their transcoding characteristics. We focus our attention on transcodings intended to customize an image for file size savings. Our results allow the developers of a transcoding proxy server to choose the appropriate transcoding techniques for the important classes of Internet images. We analyze the characteristics of images available on the Web through a representative trace. We show that most GIF images accessed on the Internet are small

In this paper we study the problem of multimedia streaming and transcoding in P2P systems. We propose a multimedia streaming architecture in which transcoding services coordinate to transform the streaming data into different formats and adapt to both the QoS requirements of the applications and to the availability of the system resources. Our techniques are entirely distributed, use only local knowledge and scale well with the size of the system. Extensive simulation results validate the performance of our approach.

We propose a method that can find the optimal tradeo# point between transcoding overhead (CP cost) and storage needed for the various pre-processed content variants (I/O cost). The method selectively pre-adapts a subset of content variants and leaves the generation of the residue to dynamic content adaptation with this pre-adapted subset as an input. We prove bounds regarding the optimality of the algorithm employed. The proposed model creates a collaborative environment across the components of client, proxy and server, based on which we study the distribution of adaptation complexity across these components. We use simulation to verify the projected benefits. The method has been successfully implemented in a trial PDF document content adaptation system.