Slides on Problem Solving I - Distributed Software Develop | CS 682, Study notes of Software Engineering

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Distributed SoftwareDevelopment^ Problem Solving I

Chris Brooks Department of Computer ScienceUniversity of San Francisco^ Department of Computer Science — University of San Francisco – p. 1/

Distributed Problem Solving • The preliminary portion of the course focused on techniquesfor achieving properties or states in distributed systems. • Causal delivery, mutual exclusion, etc. • Now, we turn to the question of how to solve problems in adistributed fashion, assuming that we have implementedsome of these properties.

Department of Computer Science — University of San Francisco – p. 2/

Centrally controlled environments • At one extreme, all processes in a system are controlled by asingle individual or organization.^ •^ Beowulf cluster^ •^ Parallel computer^ •^ Intranet • This allows us to make fairly restrictive assumptions aboutthe behavior of system processes.^ •^ NFS, parallel computation (e.g. conjugate gradient)

Department of Computer Science — University of San Francisco – p. 4/

Cooperative processes

-^ We’ll also think about processes that are controlled byseparate individuals, but assumed to be cooperative.^ •^ SETI@Home, distributed.net^ •^ Meeting scheduling^ •^ TCP (originally) •^ In this case, we can assume that processes will actbenevolently, but that they will be heterogenous.

Department of Computer Science — University of San Francisco – p. 5/

TCP: an illustration

-^ TCP is an example of a protocol that was designed to workin a cooperative environment. •^ Recall that TCP is built on top of UDP^ •^ UDP provides packet-oriented delivery. •^ TCP provides reliable in-order delivery on top of UDP. •^ Sender A sends a packet to receiver B. •^ B returns an acknowledgment that the packet was received. •^ If A does not receive an ACK before a timer expires, thepacket is resent.

Department of Computer Science — University of San Francisco – p. 7/

TCP: an illustration

-^ To improve transmission efficiency, TCP uses a conceptcalled^

sliding windows

-^ The sender has a “window” of size

n. It sends all packets

within that window. • As the lowest-numbered packet in the window isacknowleged, the window “slides” upward, and morepackets are sent. • This improves transmission rates - the goal is for thenetwork to be completely saturated.

Department of Computer Science — University of San Francisco – p. 8/

TCP: an illustration

-^ The TCP congestion algorithm does the following (loosely):^ •^ When a packet is lost, halve the window size and doubletimeout.^ •^ If all packets in a window are transmitted successfully,increase window size by 1. •^ There are lots of details in the implementation of this thatI’m glossing over. •^ The key point is this: This protocol works wonderfully,

as

long as everyone else also uses it

-^ Designed to minimize congestion over the entireInternet.

Department of Computer Science — University of San Francisco – p. 10/

TCP: an illustration

-^ In the early days if the Internet, this was not a problem.^ •^ Small number of users, fewer bandwidth-saturatingapps. •^ Parallel download of images from web pages was the firstconcern. •^ Later, non-TCP protocols (RTSP, proprietary schemes)implemented their own congestion control algorithms. •^ These applications are not necessarily tuned to any sort ofglobal optimum.

Department of Computer Science — University of San Francisco – p. 11/

Distributing a Problem

-^ We’ll also need to think about how well a problem can bepartitioned. •^ Typically, a problem is distributed by dividing it intosubproblems. •^ Each node or process works on its own subproblem. •^ Processes may need to communicate with each other. •^ A center or coordinator is responsible for doling outsubproblems and collecting results.

Department of Computer Science — University of San Francisco – p. 13/

Problem Coupling

-^ We can characterize distributed problems by the degree ofinteraction that is required between nodes. •^ Tightly coupled: nodes must communicate frequently inorder to solve subproblems. •^ Loosely coupled: Subproblems are relatively independent ofeach other. •^ “Medium coupled”: Some interaction must take place.

Department of Computer Science — University of San Francisco – p. 14/

Tightly Coupled Problems

-^ Tightly coupled problems typically have a great deal of datadependency between subproblems.^ •^ Nodes must frequently share partial results in order toproceed. •^ This means that tightly coupled problems are best solved ina parallel computer or a LAN. •^ All nodes should have roughly the same computing power.^ •^ A slow process can act as a bottleneck.

Department of Computer Science — University of San Francisco – p. 16/

Loosely coupled problems

-^ At the other end of the spectrum are loosely coupledproblems. •^ The center can divide up a problem and allow processes towork independently on subproblems. •^ Nice for settings in which communication is slow, or nodesmay run at different speeds •^ Examples:^ •^ distributed.net^ •^ SETI@Home

Department of Computer Science — University of San Francisco – p. 17/

mmetric-key encryption: a brief digress^ •^ Symmetric key encryption (or secret-key encryption) usesone key to encrypt and decrypt a message.^ •

As opposed to public-key encryption, which uses pairs ofkeys. • A series of bit shifts and ANDs with a key are used toconceal a message. • Secret-key encryption is “more secure” than public keyencryption in the sense that a shorter key is needed toprovide the same level of security.

Department of Computer Science — University of San Francisco – p. 19/

mmetric-key encryption: a brief digress^ •^ Two well-known algorithms: DES, RC5.^ •

DES was developed by the government in the 50s • RC5 was developed at RSA labs in the 90s. • The only^ known

way to defeat them is through exhaustive search of all keys. • DES keyspace is

keysize 2

-^ 56-bit secret-key algorithm has a keyspace of

56 2 = 72

quadrillion keys.

Department of Computer Science — University of San Francisco – p. 20/