an introduction to distributed and parallel computing joel m crichlow pdf

An Introduction To Distributed And Parallel Computing Joel M Crichlow Pdf

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This book provides a comprehensive overview of both the hardware and software issues involved in designing state-of-the-art distributed and parallel computing systems. Essential for both students and practitioners, this book explores distributedMoreThis book provides a comprehensive overview of both the hardware and software issues involved in designing state-of-the-art distributed and parallel computing systems. The book also includes coverage of parallel language design, including Occam and Linda. Each chapter ends with questions, and the book contains an extensive glossary and list of reference sources. Dutton Skira Architecture Library, Milano

An Introduction To Distributed And Parallel Computing - Joel M. Crichlow

This article describes the system architecture, a flaw in the. Consider the follow in g situation. A collection of onl in e. An airl in e flight with a certa in number of available seats is. In the se and o the r similar situations, depend in g on. COPAR [1, 2, 3, 4, 5] is a system support in g the. One count is called the permanent or pessimistic count; the. When the system is in itialized, the permanent count. P j at each node j is set to the in itial resource availability. For example, if the re are four nodes, if A in itial is , and.

The system simulates a person access in g an onl in e. At the end of the two-phase commit cycle, the owner. If that is not the case, transaction r t is discarded; o the rwise,. When a node l receives. If this is the first such message, the n if the transactionr. When the permanent processor in a node j coord in ates. The temporary. The net effect is that the temporary. Consider the follow in g small example.

Suppose the. Note in Table 2 the re are no temporarily. We see this happen in g in Table 3, which conta in s. Fix in g the flaw is simple: remove the step in the permanent.

Table 3. Orig in al results from [4] with the flaw. We see the improvement in Table 5, which conta in s. In simulations in volv in g a remote node, the re. We are in vestigat in g ways to deal with the follow in g possibly. Table 5. Results from [4] after fix in g the flaw. We are in vestigat in g. We are also in vestigat in g ways to in clude in the system. A flaw was discovered and fixed, preserv in g. Future enhancements. System that comb in es Optimism and Pessimism in.

Crichlow, S. Hartley, M. Hose in , D. The dist in guish in g feature is that the nodes of the system are connected by a high-latency, low-b and width, or congested network. An example is the distribution of relief supplies in a large-scale disaster situation. To implement responses to allocation and deallocation requests at a particular node in a timely manner, the database of available resources is replicated at each node.

Also provided is a comparison of the post-fix performance of the system with the orig in al. A collection of onl in e travel agency Web sites is scattered over a large geographical area and connected by a slow or congested network. An airl in e flight with a certa in number of available seats is scheduled to take off at a date and time in the future.

Each onl in e agency h and les the flight reservation requests and cancellations of customers access in g its Web site. A similar situation is the follow in g. A collection of dispersed disaster relief centers h and les the requests for relief supplies by distressed people near each center.

Supplies are delivered from a central warehouse to relief centers based on the ir need. The centers and a central warehouse are connected by a slow or congested network. Trucks will carry the requested supplies from the central warehouse to the relief centers in the near future. In the se and o the r similar situations, depend in g on the slow network to synchronize updates to a s in gle shared database of available resources would result in unacceptably lengthy response times to transactions allocation and deallocation requests.

COPAR [1, 2, 3, 4, 5] is a system support in g the replication of the database of available resources at each node onl in e travel agency, relief center in order to shorten transaction response times. One count is called the permanent or pessimistic count; the o the r count is called the temporary or optimistic count. Thus, this count is always identical at all the nodes and represents true resource availability on the airl in e flight or at the central relief supply warehouse.

The temporary count is ma in ta in ed separately and in dependently by each node. In general, resource counts for availability are a s in gle non-negative in teger A, such as for airl in e seats, or a vector of non-negative in tegers A 1 ,A 2 , Similarly, resource counts for transactions allocation and deallocation requests are a s in gle in teger r, negative for an allocation and positive for a deallocation or release, or a vector of in tegers r 1 ,r 2 , When the system is in itialized, the permanent count P j at each node j is set to the in itial resource availability A in itial , such as seats on an airplane or first aid kits, blankets, and bottles of water at a central warehouse for disaster relief.

The temporary count T j at each node is set to the in itial permanent count divided by the number of nodes n. Most reservation system s allow some overbook in g to compensate for reservations that are not used, such as passengers not show in g up for an airl in e flight or people not pick in g up supplies when delivered to a relief center.

There is a cost of overbook in g, though, such as compensat in g passengers denied board in g on an airl in e flight or worsen in g the situation of people need in g relief supplies.

Organizations us in g a reservation system must carefully evaluate the Figure 1. COPAR system architecture. Transactions from the generator are numbered sequentially. The node j is chosen at r and om from 1 to n, where n is the number of servers relief centers, travel agencies. Each transaction r i is a vector of m in tegers, r1 i,ri 2 , The m in tegers in a transaction are generated r and omly. Each node ma in ta in s two queues of transactions, called the parent or owner queue and the child queue.

Each nodej has two processors threads , one responsible for ma in ta in in g the parent queue and the permanent count P j at the node, and the o the r responsible for ma in ta in in g the child queue and the temporary count T j at the node.

See Figure 1. The permanent processor at each node participates in a two-phase commit cycle with all the o the r node permanent processors. If that is not the case, all P j are left unchanged and the transaction r i is marked as a violation. The temporary processor at each node j removes the transactionr t at the head of its child queue, if any, and calculates ifr t can be allocated or satisfied from its temporary optimistic countT j.

When a node l receives such a message from nodej for transactionr t , node l makes two checks. COPAR message dispatch in g. If this is the first such message, the n if the transactionr t has not yet been done permanently pessimistically , node j send in g the message is marked as the node hav in g done transaction r t temporarily optimistically.

If this is the first such message, but transaction r t has already been done permanently, no node is recorded as hav in g done the transaction temporarily. Figure 2 shows how in com in g messages are dispatched at each node. Figure 3 gives pseudocode for the permanent and temporary processor algorithms.

The goal of the COPAR system is that temporary processors will be able to generate a reservation for an airl in e flight or a promise for relief supplies more quickly than the permanent processors. Do in g a transaction optimistically in volves a pair of messages between two nodes, whereas the two-phase commit of pessimistic process in g in volves a message count proportional to the number of nodes in the system.

COPAR permanent and temporary pseudocode. Nodes that are lightly loaded receive proportionately fewer transactions from the generator or are on a faster segment of the network or have faster or multiple CPUs might respond more quickly to transactions temporarily. This faster response might reduce the ir temporary counts more quickly towards zero, while o the r nodes reta in higher temporary counts, especially if the re are overall more reservations transactions request in g resources than cancellations transactions return in g or releas in g resources in the simulation of a system , as would be expected in reserv in g airplane seats or distribut in g disaster relief supplies.

A serious problem can occur if the permanent processors lag significantly beh in d the temporary processors. The usual cause of this lag is the numerous messages needed to implement the two-phase commit of the permanent processors compared to the much lighter communication needs of the temporary processors.

A contribut in g factor is if one of the nodes is connected to the o the rs with a higher latency, lower b and width, or more congested segment of the network compared to the in terconnections of the o the r nodes.

The former node will rarely be the first to respond temporarily to transactions. However, it must participate in all two-phase commit cycles of the permanent processors, slow in g the m down. Suppose the re are overall more reservations than cancellations in the simulation of a system. The temporary processors of the nodes with fast in terconnections will be able to respond quickly and might get ahead of the permanent processors.

Each time the permanent processors calculate the new temporary counts of the nodes, the y might be us in g the permanent countP j result in g from process in g a much older transaction than the temporary processors are currently process in g. Suppose the in itial number of resources A in itial is 60, the number of nodes n is four, the number of resources types m is one, the overbook in g allowancec is1. Each two-phase commit might take several seconds to perform, as shown in Table 1.

Small example with the flaw on a slow network. A violation V means that not enough resources are available to satisfy the transaction. This problem might not occur if all the nodes are connected by a low latency, high b and width, or uncongested network and if the permanent processors are not lagg in g and not us in g stale permanent counts P j to calculate new temporary counts T j.

We see this happen in g in Table 3, which conta in s some of the statistical performance data from [4]. The local nodes are on the same subnet of a local area network at Rowan University in New Jersey and the UWI node is at a remote location University of West Indies connected to the Rowan nodes over a wide area network the Internet.

Distributed Systems: Computing Over Networks

This article describes the system architecture, a flaw in the. Consider the follow in g situation. A collection of onl in e. An airl in e flight with a certa in number of available seats is. In the se and o the r similar situations, depend in g on. COPAR [1, 2, 3, 4, 5] is a system support in g the.


Language English. Title. An introduction to distributed and parallel computing. Author(S) Joel M. Crichlow. Publication. Data. London: Prentice-Hall. Publication.


finding and fixing a flaw in the copar system - Rowan University

Distributed Computer Control Overview. Critchlow, Pearson Education Ltd, Stout and T. Leigh, Second Edition. Give reasons why integrated DCCS might be preferable to multiple independent controllers.

Provides an overview of both the hardware and software issues involved in designing state-of-the-art distributed and parallel computing systems. Read more Please choose whether or not you want other users to be able to see on your profile that this library is a favorite of yours.

An introduction to distributed and parallel computing

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Each chapter ends with a series of questions. Download An Introduction to Distributed and Parallel Computing free book PDF Author: Joel M. Crichlow.


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Ralf G.

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