P. Baratloo, V. Dasgupta, Z.M. Karamcheti, Kedem. "Metacomputing with MILAN". Heterogeneous Computing Workshop, International Parallel Processing Symposium, April 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....discussed in [13] see also [5] A number of systems (based on algorithms that perform a set of tasks) have been developed to incorporate fault tolerance into distributed applications. They use various techniques such as random scheduling of tasks [11] scheduling based on Manager Worker models ([2]) stable tuple spaces ( 1] This paper focuses on an analytical performance model ( 14] and on a prototype ( 15] for analyzing systems that perform a fixed set of tasks (i.e. no arrival process) The former is modeled as a transient M G P queue while the latter is based on an asynchronous ....

A. Baratloo, P. Dasgupta, V. Karamcheti, Z. M. Kedem, "Metacomputing with MILAN", In Proceeding of the 8 Heterogeneous Computing Workshop, April 1999.

....practicality in developing distributed systems. When constructing an analytic performance model, it is imperative that one focus on a particular application domain. In the eld of high performance scienti c computing, applications that are based on processing a bag of tasks form a large domain [2, 3, 27]. These applications are also referred to as iterative, grid and data parallel. Some examples are: Simulation, Image Processing, Discrete Optimization, Transformation, and Computational Geometry. A task can easily be de ned in these applications. For example, in image processing, classifying a ....

A. Baratloo, P. Dasgupta, V. Karamcheti, Z. M. Kedem, \Metacomputing with MILAN ", In Proceeding of the 8 Heterogeneous Computing Workshop, April 1999.

....in a reliable storage. In the event of a process failure, either the a ected process or the whole computation is restarted from its last checkpointed state. In the eld of high performance scienti c computing, applications that are based on processing a bag of tasks form a large domain [3, 4, 11]. Such applications are also referred to as iterative, grid and data parallel. Some examples are: Simulation, Image Processing, Discrete Optimization, Transformation, and Computational Geometry. A task can easily be A preliminary version of this paper appeared in [13] de ned in these ....

....Performing a task may require access to more than one object and hence task descriptions are also stored in the repository. The structuring technique used by these two programming models provide automatic load balancing. The programming models Calypso, Chime, and Charlotte of the MILAN project [4] enable shared memory parallel applications to be executed on distributed platforms that Algorithm to perform N tasks (Executed by all processors asynchronously) step 1 create a unique task partition TP k from T step 2 t = get a Task(tempTP ) Initially, tempTP = TP k while t NOT null ....

A. Baratloo, P. Dasgupta, V. Karamcheti, Z. M. Kedem, \Metacomputing with MILAN ", In Proc. of the 8 th Heterogeneous Computing Workshop, April 1999.

....Cashmere style release consistency within tightly coupled clusters, and InterArt style version based consistency for wide area systems. The grain of coherence unit on heterogeneous distributed machines are shared segments, which is an object based approach, while our approach is page based. MILAN[3] project, including three prototypes, Calypso, Chime, and Charlotte, provides several fundamental contributions in metacomputing. Where Charlotte is the first parallel programming system provides one click computing on world wide web. Both the system and applications use Java as programming ....

A. Baratloo, P. Dasgupta, V. Karamcheti, and Z.M.Kedem. Metacomputing with MILAN. In Eighth Heterogeneous Computing Workshop, April 1999.

....resource management architecture, respectively, for supporting tunability. The performance impact of tunability is characterized in Section 6. Section 7 places our work in context with related efforts and we conclude in Section 8. 2. BACKGROUND AND PROJECT CONTEXT The MILAN metacomputing system [1] provides middleware layers that enable the efficient, reliable, predictable execution of applications on an unreliable and dynamically changing set of machines. MILAN takes advantage of two execution techniques with strong theoretical foundations [17, 11] two phase idempotent execution ....

A. Baratloo, P. Dasgupta, V. Karamcheti, and Z. Kedem. Metacomputing with MILAN. In Proc. 8th Heterogeneous Computing Workshop, 1999.

....resource management architecture, respectively, for supporting tunability. The performance impact of tunability is characterized in Section 6. Section 7 places our work in context with related efforts and we conclude in Section 8. 2. BACKGROUND AND PROJECT CONTEXT The MILAN metacomputing system [1] provides middleware layers that enable the efficient, reliable, predictable execution of applications on an unreliable and dynamically changing set of machines. MILAN takes advantage of two execution techniques with strong theoretical foundations [11, 17]##two phase idempotent execution strategy ....

A. Baratloo, P. Dasgupta, V. Karamcheti, and Z. Kedem, Metacomputing with MILAN, in Proc. 8th Heterogenous Computing Workshop," 1999.

....about resource availability and resource sharing agreements, a resource scheduler can make better scheduling decisions. The techniques described in this paper are part of a flexible execution substrate for distributed applications that we are constructing in the Computing Communities project [3] at New York University. Other techniques (e.g. resourceconstrained sandboxing [4] being developed as part of this project focus on the complementary problem of security concerns, e.g. ensuring that a malicious application does not misuse a resource that it has been allocated. In the rest of ....

....based on simulation show that resource sharing under agreements has advantages and the structure of agreements has significant effects on the benefits. Future work involves implementing and evaluating this scheme in the context of resource management system for the Computing Community (CC) Project [3], which provides a distributed, collaborative environment in which a dynamic changing set of partners work together to share resources. Acknowledgments We would like to thank Zvi Kedem, who frequently participated in and provided many helpful insights during discussions. This research was ....

A. Baratloo, P. Dasgupta, V. Karamcheti, and Z. M. Kedem. Metacomputing with milan. In Proceedings of International Parallel Processing Symposium (IPPS/SPDP), 1999.

No context found.

P. Baratloo, V. Dasgupta, Z.M. Karamcheti, Kedem. "Metacomputing with MILAN". Heterogeneous Computing Workshop, International Parallel Processing Symposium, April 1999.

No context found.

A. Baratloo, P. Dasgupta, V. Karamcheti, Z. M. Kedem, "Metacomputing with MILAN", In Proceeding of the 8 Heterogeneous Computing Workshop, April 1999.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC