K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of the 3rd USENIX Symposium on Internet Technologies and Systems, pages 197--208, San Francisco, CA, Mar. 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

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K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of the 3rd USENIX Symposium on Internet Technologies and Systems, pages 197--208, San Francisco, CA, Mar. 2001.

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K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In USITS, San Francisco, CA, Mar. 2001.

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K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of the 3rd USENIX Symposium on Internet Technologies and Systems, pages 197--208, San Francisco, CA, March 2001.

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K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of 3rd USENIX Symposium on Internet Technologies and Systems, San Francisco, CA, Mar. 2001.

.... quickly to run on a cluster environment for handling a large volume of concurrent request traffic with large scale persistent service data This dissertation investigates techniques in building a middleware system, called Neptune, that provides clustering support for scalable network services [69, 70, 71]. In particular, it contains the following contributions to establish my thesis: ffl The development of a flexible and scalable clustering architecture with efficient load balancing support for fine grain services. ffl The design and implementation of an integrated resource management framework ....

K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of the 3rd USENIX Symposium on Internet Technologies and Systems, pages 197--208, San Francisco, CA, March 2001.

....the image store service is partitioned into two partition groups. Figure 1. Architecture of a service cluster. While previous research has addressed the issues of scalability, availability, extensibility, and service replication support in building large scale network service infrastructures [3, 16, 18, 19, 26, 28], there is still a lack of comprehensive study on load balancing support in this context. This paper studies the issue of providing efficient load balancing support for accessing replicated services inside the service cluster. The request distribution between wide area external clients and ....

....random polling policy so strongly that we do not consider the broadcast policy in the prototype system. 3.1. System Architecture This implementation is a continuation of our previous work on Neptune, a cluster based infrastructure for aggregating and replicating partitionable network services [28]. Neptune allows services ranging from read only to frequently updated be replicated and aggregated in a cluster environment. Neptune encapsulates an application level network service through a service access interface which contains several RPC like access methods. Each service access through one ....

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K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of the 3rd USENIX Symposium on Internet Technologies and Systems, pages 197--208, San Francisco, CA, Mar. 2001.

....may be partitioned based on categories; or in an Internet search engine where data may be partitioned based on their URL domains. Supporting efficient data aggregation is not straightforward. Several previous works on cluster based service programming rely on a fixed node to aggregate results [7, 21], which could quickly run into scalability problems when a large number of partitions are involved. On the other hand, it is desirable to provide a high level data aggregation primitive to aid service programming, and hide the complexity of implementation details behind an easy to use interface. A ....

....or failed nodes from delaying the completion of DAC requests, we introduce a staged timeout scheme that eagerly prunes out slow or failed servers from a reduction tree. The work described in this paper is a critical building block in Neptune, a middleware system that provides replication support [21], and quality aware resource management [19, 20] for scalable cluster based network services. We have applied the DAC primitive in the implementation and deployment of several applications: a search engine document retriever, a parallel protein sequence matcher, and an online parallel facial ....

[Article contains additional citation context not shown here]

K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In USITS, San Francisco, CA, Mar. 2001.

....the image store service is partitioned into two partition groups. Figure 1. Architecture of a service cluster. While previous research has addressed the issues of scalability, availability, extensibility, and service replication support in building large scale network service infrastructures [10, 12, 13, 20, 23, 24], there is still a lack of comprehensive study on load balancing support for service accesses inside the service cluster. A large amount of work has been done by the industry and research community to optimize HTTP request distribution among a cluster of Web servers [2, 3, 5, 14, 18, 19, 25] Most ....

....strongly that we do not consider the broadcast policy in the prototype system. 4. 1 Clustering infrastructure and system architecture This implementation is a continuation of our previous work on Neptune, a cluster based infrastructure for aggregating and replicating partitionable network services [23]. Neptune allows services ranging from read only to frequently updated be replicated and aggregated in a cluster environment. Neptune encapsulates an application level network service through a service access interface which contains several RPC like access methods. Each service access through one ....

[Article contains additional citation context not shown here]

K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of the 3rd USENIX Symposium on Internet Technologies and Systems, pages 197--208, San Francisco, CA, March 2001.

.... static content [1, 4, 6, 16, 19, 26] With the increasing demand to provide highly scalable, available, and easy to manage services, the deployment of largescale complex server clusters has been rapidly emerging in which service components are usually partitioned, replicated, and aggregated [13, 14, 20, 23]. Limited studies have been conducted on service differentiation for cluster based servers [27] and there is still a lack of comprehensive QoS support for these large scale cluster based network services. This paper presents the design and implementation of a flexible and efficient QoS framework ....

....framework should be flexible enough to allow service providers to express a variety of desired service qualities. Most previous studies have been using a monolithic metric to measure system utilization and define QoS constraints, be it system throughput, mean response time, or mean stretch factor [1, 23, 27]. On the contrary, we give service providers the flexibility of choosing the system utilization metrics that best suit their own needs or the nature of individual services. To be more specific, we consider the fulfillment of a service request generates certain yield, called QoS yield, which may be ....

[Article contains additional citation context not shown here]

K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu. Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services. In Proc. of the 3rd USENIX Symposium on Internet Technologies and Systems, pages 197--208, San Francisco, CA, March 2001.

....using tries and parameter classifiers allows for fast searching of URL classes. Cachuma integrates the proposed techniques and interoperates with standard Web servers without modifying server source code. Our future work is to extend cache management for cluster based network services [7] [24]. Instant precomputing has better cache hit ratios than selective precomputing, however, it aggravates resource contention during load peaks and leads to higher response times, unless a Web site has sufficient computing resources. Selective precomputing strikes a necessary balance between ....

K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu, "Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services," To appear in the 3rd USENIX Symposium on Internet Technologies and Systems (USITS '01), Mar. 2001.

....while maximizing resource utilization and that it can substantially outperform static server partitioning. I. INTRODUCTION The deployment of cluster based network services keeps increasing to meet the demand for scalability and availability for the fast growing client population [1] 2] 3] [4]. However, it is not always possible for a Web site to accurately predict peak load and prepare enough computing resources because client request rates tend to be bursty and fluctuate dramatically [5] 6] Such fluctuations might not only be caused by people s working habit, or users ....

K. Shen, T. Yang, L. Chu, J. L. Holliday, D. A. Kuschner, and H. Zhu, "Neptune: Scalable Replication Management and Programming Support for Cluster-based Network Services," To appear in the 3rd USENIX Symposium on Internet Technologies and Systems (USITS '01), Mar. 2001.

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