Parallel VS Distributed

Transcription

1 Parallel VS Distributed The distributed systems tend to be multicomputers whose nodes made of processor plus its private memory whereas parallel computer refers to a shared memory multiprocessor. In Parallel Computing, all the different "processor" have the access to a shared memory. This memory can also be used to share information between different processors rather than explicitly sending messages. In Distributed Computing, all the different processors have their own private non-shareable memory. Information is exchanged between the processors solely on the basis of messages.

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4 Parallel computers can be roughly classified according to the level at which the hardware supports parallelism, with multi-core and multiprocessor computers having multiple processing elements within a single machine While clusters, MPPs, and grids use multiple computers to work on the same task.

5 Cluster Computing A computer cluster is a group of linked computers, working together closely. The components of a cluster are connected to each other through fast local area networks The cluster is a tightly coupled system and have centralized job management and scheduling system. All the computers in the cluster use the same hardware and operating system, and are at same physical location connected with a very high speed connection to perform as a single computer Types of Cluster High Availability Cluster Load Balancing Cluster HPC Cluster

6 High Availability Clusters

7 Load Balancing Clusters

8 HPC Clusters

9 What is Grid?

10 The grid is a loosely coupled system Distributed Job Management and scheduling Computers in the grid are not required to be in the same physical location and can be operated independently Computers in a grid can run different OSs and can have different hardware You don t need to buy large servers for applications that can be split up and farmed out to smaller commodity type servers Grid environments are modular and thus don't have SpoF.

11 Virtual Organization Column 1 Column 2 Column Row 1 Row 2 Row 3 Row 4

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13 Cloud Overview A large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. It is sold on demand minute or a hour It is Elastic can add or remove the resources at any time Fully Managed by the Cloud computing provider Infinite availability of resources

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18 Autonomic Computing Autonomic computing Named after autonomic nervous system New components integrate as effortlessly as a new cell establishes itself in the body Systems can manage themselves according to an administrator s goals Self-governing operation of the entire system, not just parts of it 19

19 Self-management (1/2) Self-management maintain and adjust their operation in the face of changing components, workloads, demands, and external conditions and in the face of hardware or software failures, both innocent and malicious Ex. Component upgrade Continually check for component upgrades Download and install Reconfigure itself Run a regression test When it detects errors, revert to the older version 20

20 Problem diagnosis in an autonomic system upgrade Autonomic Upgrade Problem Determiner Dependency Analyzer 21

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22 Self-management (2/2) Four aspects of self-management Self-configuration Configure themselves automatically Self-optimization Hundreds of tunable parameters Continually seek ways to improve their operation Self-healing Analyze information from log files and monitors Self-protection Malicious attacks Cascading failures 23

23 Architectural considerations (1/2) Autonomic elements will manage Internal behavior Relationships with other autonomic elements Autonomic element will consist of Managed elements Hardware/software resource Autonomic manager Monitoring the managed elements and external env. 24

24 Architectural considerations (2/2) Fully autonomic computing Evolve as designers gradually add increasingly sophisticated autonomic managers to existing managed elements Autonomic elements will function at many levels At the lower levels Limited range of internal behaviors Hard-coded behaviors At the higher levels Increased dynamism and flexibility Goal-oriented behaviors 25

25 Engineering challenges (1/3) Life cycle of an autonomic element Design, test, and verification Testing autonomic elements will be challenging harder to anticipate their environment, especially when it extends across multiple administrative domains Installation and configuration Element registers itself in a directory service Monitoring and problem determination Elements will continually monitor themselves Adaptation, optimization, reconfiguration Upgrading Uninstallation or replacement 26

26 Engineering challenges (2/3) Relationships among autonomic elements Specification Set of output/input services of autonomic elements Expressed in a standard format (UDDI, OGSA) Establishing standard service ontology Description syntax and semantics Location Find input services that autonomic element needs Negotiation Demand-for-service, FCFS, Posted-price Provision Operation Autonomic manager oversees the operation Termination Log reputation 27

27 Engineering challenges (3/3) System-wide issues Authentication, encryption, signing Autonomic elements can identify themselves Autonomic system must be robust against insidious forms of attack Goal specification Humans provide the goal and constraints Ensure that goals are specified correctly in the first place, and, Ensure that systems behave reasonably even when they are not Autonomic systems will need to protect themselves from input goals that are inconsistent, implausible, dangerous, or unrealizable 28

28 Scientific challenges Behavioral abstractions and models Mapping from local behavior to global behavior is a necessary Inverse relationship Robustness theory Learning and optimization theory Agents continually adapt to their environment that consists of other agents There are no guarantees of convergence Negotiation theory Automated statistical modeling 29

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