We introduce and evaluate a middleware clustering technology capable of allocating resources to web applications through dynamic application instance placement. We define application instance placement as the problem of placing application instances on a given set of server machines to adjust the amount of resources available to applications in response to varying resource demands of application clusters. The objective is to maximize the amount of demand that may be satisfied using a configured placement. To limit the disturbance to the system caused by starting and stopping application instances, the placement algorithm attempts to minimize the number of placement changes. It also strives to keep resource utilization balanced across all server machines. Two types of resources are managed, one load-dependent and one load-independent. When putting the chosen placement in effect our controller schedules placement changes in a manner that limits the disruption to the system.

Video-on-demand (VoD) service providers are intensely interested in transport, storage, streaming and caching in content delivery networks. Today’s 5,000-hour library may grow toward the 750,000-hour “Long Tail ” movie and TVseries catalog. We propose a method to calculate how much of a library should be cached. Much previous work focused on theoretical caching concepts, or the dynamics of cache filling and reclamation. Our method explicitly considers the impact of the available video server equipment; we present a VoD design tool comprising a novel cost function, hit ratio estimation and heuristic. 1.

Abstract. This paper addresses the problem of dynamic system reconfiguration and resource sharing for a set of applications in large-scale services environments. It presents a decentralized application placement scheme that dynamically provisions enterprise applications with heterogeneous resource requirements. Potential benefits, including improved scalability, resilience, and continuous adaptation to external events, motivate a decentralized approach. In our design, all nodes run a placement controller independently and asynchronously, which periodically reallocates a node’s local resources to applications based on state information from a fixed number of neighbors. Compared with a centralized solution, our placement scheme incurs no additional synchronization costs. We show through simulations that decentralized placement can achieve accuracy close to that of state-of-the-art centralized placement schemes (within 4% in a specific scenario). In addition, we report results on scalability and transient behavior of the system. 1

The scalability of a distributed service can be improved if the number of replicated instances of the service can vary with the load experienced by the system. To ensure that the number and placement of the replicas can be dynamically changed based on network and load conditions, a peer node must know when it should create a new replica and when such a replica can be removed. A scheme that makes such decisions centrally suffers from a number of problems. In this paper, we propose a self-managing replication algorithm for a peer-to-peer system where a peer node makes its decisions based on its locally maintained information. This algorithm offers several benefits. They include being responsive to heterogeneous load and ensuring that each peer node fairly contributes its available resources to meet the needs of the overall system. It is evaluated with simulations of a system that consists of one thousand nodes. Our results show that this self-managing algorithm can maintain a replication degree that adapts to load in the system and achieve a low response time.

We present a peer-to-peer service management middleware that dynamically allocates system resources to a large set of applications. The system achieves scalability in number of nodes (1000s or more) through three decentralized mechanisms that run on different time scales. First, overlay construction interconnects all nodes in the system for exchanging control and state information. Second, request routing directs requests to nodes that offer the corresponding applications. Third, application placement controls the set of offered applications on each node, in order to achieve efficient operation and service differentiation. The design supports a large number of applications (100s or more) through selective propagation of configuration information needed for request routing. The control load on a node increases linearly with the number of applications in the system. Service differentiation is achieved through assigning a utility to each application, which influences the application placement process. Simulation studies show that the system operates efficiently for different sizes, adapts fast to load changes and failures and effectively differentiates between different applications under overload. I.

The success of the Web in the last decade has caused an evolution of the resources that are disseminated through the Internet. The initial static contents have been enriched by an increasing amount of multimedia and dynamically generated resources. This evolution has shifted the research focus from a nowadays mature content delivery scenario to a Web service generation and delivery scenario. The overall complexity is further increased by the so-called ubiquitous Web access that aims to allow the users to access Web-based services from any location through every class of devices. The key feature of the ubiquitous Web is represented by the content adaptation services that tailor the Web content and the Web-based services to the characteristics of the client devices and to the preferences of the users. This feature of the ubiquitous Web introduces new performance and security problems to the infrastructure that has to generate and disseminate the resources, but it also offers a wide range of novel service opportunities. The tutorial is divided in three parts: the first related to the services for the ubiquitous Web access, the second to the architectures to build scalable services, the third to the presentation of some case studies. In the first part, we present the adaptation services by differentiating transcoding from personalization services. Transcoding services tailor Web resources to the capabilities of the client and network infrastructure, while personalization requires more sophisticated services that aim to adapt the content to (a combination

Abstract—The great diffusion of Mobile Web-enabled devices allows the implementation of novel personalization, location and adaptation services that will place unprecedented strains on the server infrastructure of the content provider. This paper has a twofold contribution. First, we analyze the five-years trend of Mobile Web-based applications in terms of workload characteristics of the most popular services and their impact on the server infrastructures. As the technological improvements at the server level in the same period of time are insufficient to face the computational requirements of the future Mobile Web-based services, we propose and evaluate adequate resource management strategies. We demonstrate that pre-adaptating a small fraction of the most popular resources can reduce the response time up to one third thus facing the increased computational impact of the future Mobile Web services.

Abstract The success of the Mobile Web is driven by the combination of novel Web-based services with the diffusion of advanced mobile devices that require personalization, location-awareness and content adaptation. The evolutionary trend of the Mobile Web workload places unprecedented strains on the server infrastructure of the content provider at the level of computational and storage capacity, to the extent that the technological improvements at the server and client level may be insufficient to face some resource requirements of the future Mobile Web scenario. This paper presents a twofold contribution. We identify some performance bottlenecks that can limit the performance of future Mobile Web, and we propose and evaluate novel resource management strategies. They aim to address computational requirements through a pre-adaptation of the most popular resources even in the presence of irregular access patterns and short resource lifespan that will characterize the future Mobile Web. We investigate a large space of alternative workload scenarios. Our analysis allows to identify when the proposed resource management strategies are able to satisfy the computational requirements of future Mobile Web, and even some conditions where further research is necessary.

Version 5.2- Last modified on 2005-04-27

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