HMM: A Cluster Membership Service

نویسندگان

  • Francesc D. Muñoz-Escoí
  • Óscar Gomis
  • Pablo Galdámez
  • José M. Bernabéu-Aubán
چکیده

The Hidra Membership Monitor (HMM) is a distributed service that maintains the current set of active nodes in a cluster of machines. This protocol allows the detection of multiple machine joins or failures in a unique reconfiguration, using a low amount of messages (with a cost that is linear on the number of nodes). These membership services are needed to detect cluster changes as soon as possible, initiating then the reconfiguration of the cluster state, where support for replicated objects has been included. The HMM also manages and synchronises the reconfiguration steps needed by the kernel and Hidra components of each node, ensuring that all of them take the same steps at once. Thus, our system does not need an atomic multicast protocol to deliver the messages in these reconfiguration steps. All these services provide the basis to develop reliable intracluster transport protocols and to reduce the reconfiguration time of replicated objects and services.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

An Efficient Topology-Adaptive Membership Protocol for Large-Scale Network Services

A highly available large-scale service cluster often requires the system to discover new nodes and identify failed nodes quickly in order to handle a high volume of traffic. Determining node membership efficiently in such an environment is critical to location-transparent service invocation, load balancing and failure shielding. In this paper, we present a topology-aware hierarchical membership...

متن کامل

Name Tagging with Word Clusters and Discriminative Training

We present a technique for augmenting annotated training data with hierarchical word clusters that are automatically derived from a large unannotated corpus. Cluster membership is encoded in features that are incorporated in a discriminatively trained tagging model. Active learning is used to select training examples. We evaluate the technique for named-entity tagging. Compared with a state-of-...

متن کامل

MAN-MACHINE INTERACTION SYSTEM FOR SUBJECT INDEPENDENT SIGN LANGUAGE RECOGNITION USING FUZZY HIDDEN MARKOV MODEL

Sign language recognition has spawned more and more interest in human–computer interaction society. The major challenge that SLR recognition faces now is developing methods that will scale well with increasing vocabulary size with a limited set of training data for the signer independent application. The automatic SLR based on hidden Markov models (HMMs) is very sensitive to gesture's shape inf...

متن کامل

Discovering Clusters in Motion Time-Series Data

A new approach is proposed for clustering time-series data. The approach can be used to discover groupings of similar object motions that were observed in a video collection. A finite mixture of hidden Markov models (HMMs) is fitted to the motion data using the expectation-maximization (EM) framework. Previous approaches for HMM-based clustering employ a k-means formulation, where each sequence...

متن کامل

Data Driven Profiling of Dynamic System Behavior using Hidden Markov Model based Combined Unsupervised and Supervised Classification

Dynamic systems are often best characterized by a combination of static and temporal features, with the static features describing time-invariant properties of the system, and the temporal features capturing dynamic aspects of the system. Our goal is to construct context based temporal behavior models of dynamic systems using information from both types of features. Our dynamic system profiling...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2001