Method and framework for generating an optimized deployment of software applications in a distributed computing environment using layered model descriptions of services and servers

• US 7,035,930 B2
• Filed: 10/26/2001
• Issued: 04/25/2006
• Est. Priority Date: 10/26/2001
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Graupner, Sven, Kotov, Vadim, Trinks, Holger
• Primary Examiner(s): Wiley, David
• Assistant Examiner(s): Avellino, Joseph E.
• Application Number:
  US10/046,516
• Publication Number:
  US 20030084156A1
• Time in Patent Office: 1,642 Days
• Field of Search: 709217-226, 715733-747, 707100-1041, 719311-316
• US Class Current: 709/226
• CPC Class Codes: G06F 9/5088 involving task migration
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/1001 for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/10015 Access to distributed or replicated servers, e.g. using brokers
• CPC Class Codes: H04L 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30
• CPC Class Codes: H04L 67/34 involving the movement of software or configuration parameters network booting or remote initial program loading [RIPL] G06F9/4416
• CPC Class Codes: H04L 69/329 in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 Network security protocols

Abstract

Method and framework for identifying optimal allocations of computing resources in a complex, distributed data processing environment. A plurality of server models are established, with each server model including one or more server nodes and each server node having an associated set of capacity attributes. Similarly, a plurality of service models are established, each service model including one or more service nodes and each service node having an associated set of demand attributes. The server models are defined with a layered relationship as are the service models. A node that is part of a model in one layer corresponds to a model in the next-lower layer. The invention generates optimized mappings of service nodes that are described in user-selected service models to server nodes that are described in user-selected server models, as a function of the associated sets of demand and capacity attributes.

Claims

1. A computer-implemented method for identifying optimal allocations of computing resources in a data processing arrangement having a plurality of computing machines that host a plurality of application processes, comprising:

establishing a plurality of server models, each server model including one or more server nodes, wherein each server node represents a set of hardware and has an associated set of hardware capacity attributes;

designating a layered relationship between the server models, wherein for a first server-model layer immediately above a second server-model layer, the second server-model layer includes respective models that represent the nodes in the first server-model layer;

establishing a plurality of service models, each service model including one or more service nodes, wherein each service node represents one or more software components and has an associated set of demand attributes and an associated set of capacity attributes;

designating a layered relationship between the service models, wherein for a first service-model layer immediately above a second service-model layer, the second service-model layer includes respective models that represent the nodes in the first server-model layer;

generating an optimized mapping of service nodes in a first user-selected service model in a first layer to service nodes in a second user-selected service model in a second layer as a function of the demand attributes of service nodes of the first service model and capacity attributes of service nodes the second service model, wherein the demand attribute of a service node in the first layer includes processing demand requirements of a number of tasks per unit time and the capacity attribute of a service node in the second layer includes processing capacity of a number of tasks per unit time of a process represented by the service node in the second layer; and

generating an optimized mapping of service nodes in a user-selected service model to server nodes in a user-selected server model as a function of the demand and capacity attributes.

2. The method of claim 1, further comprising:

monitoring, while the applications processes are executing, levels of demand for computing resources that correspond to selected ones of the demand attributes;

storing the levels of demand; and

generating an alternate optimized mapping of service nodes in a user-selected service model to server nodes in a user-selected server model using the stored levels of demand and the capacity attributes.

3. The method of claim 2, further comprising:

establishing one or more service-node relationships between selected pairs of the service nodes, wherein each service-node relationship has an associated transport demand attribute specifying a quantity of communication resources required for communication between the associated pair of service nodes;

establishing one or more server-node relationships between selected pairs of the server nodes, wherein each server-node relationship has an associated transport capacity attribute specifying a quantity of communication resources available for communication between the associated pair of server nodes; and

generating the optimized mapping as a function of the service-node relationships and server-node relationships.

4. The method of claim 3, wherein each server node has an associated set of demand attributes and further comprising generating an optimized mapping of server nodes in a first user-selected server model to server nodes in a second user-selected server model as a function of the demand attributes of the first server model and capacity attributes of the second server model.

5. The method of claim 4, further comprising:

representing the service models and server models in XML; and

generating a allocation matrix in XML that represents the optimized mapping.

6. The method of claim 1, further comprising:

establishing one or more service-node relationships between selected pairs of the service nodes, wherein each service-node relationship has an associated transport demand attribute specifying a quantity of communication resources required for communication between the associated pair of service nodes;

establishing one or more server-node relationships between selected pairs of the server nodes, wherein each server-node relationship has an associated transport capacity attribute specifying a quantity of communication resources available for communication between the associated pair of server nodes; and

generating the optimized mapping as a function of the service-node relationships and server-node relationships.

7. The method of claim 6, wherein each server node has an associated set of demand attributes and further comprising generating an optimized mapping of server nodes in a first user-selected server model to server nodes in a second user-selected server model as a function of the demand attributes of the first server model and capacity attributes of the second server model.

8. The method of claim 1, wherein each server node has an associated set of demand attributes and further comprising generating an optimized mapping of server nodes in a first user-selected server model to server nodes in a second user-selected server model as a function of the demand attributes of the first server model and capacity attributes of the second server model.

9. A system for identifying optimal allocations of computing resources in a data processing arrangement having a plurality of computing machines that host a plurality of application processes, comprising:

a model repository including a plurality of server models and a plurality of service models, each server model including one or more server nodes and each server node representing a set of hardware and having an associated set of hardware capacity attributes, each service model including one or more service nodes and each service node representing one or more software components and having an associated set of demand attributes and an associated set of capacity attributes, wherein the server models are defined in a layered relationship and for a first server-model layer immediately above a second server-model layer, the second server-model layer includes respective models that represent the nodes in the first server-model layer, and the service models are defined in a layered relationship and for a first service-model layer immediately above a second service-model layer, the second service-model layer includes respective models that represent the nodes in the first service-model layer; and

an optimization engine coupled to the model repository, the optimization engine configured to generate an optimized mapping of service nodes in a first user-selected service model in a first layer to service nodes in a second user-selected service model in a second layer as a function of the demand attributes of service nodes of the first service model and capacity attributes of service nodes the second service model, wherein the demand attribute of a service node in the first layer includes processing demand requirements of a number of tasks per unit time and the capacity attribute of a service node in the second layer includes processing capacity of a number of tasks per unit time of a process represented by the service node in the second layer, the optimization engine further configured to generate an optimized mapping of service nodes in a user-selected service model to server nodes in a user-selected server model as a function of the associated demand and capacity attributes.

10. The system of claim 9, further comprising:

means for monitoring, while the applications processes are executing, levels of demand for computing resources that correspond to selected ones of the demand attributes;

means for storing the levels of demand; and

wherein the optimization engine is further configured to generate an alternate optimized mapping of service nodes in a user-selected service model to server nodes in a user-selected server model using the stored levels of demand and the capacity attributes.

11. The system of claim 9, further comprising:

wherein the model repository further includes one or more service-node relationships between selected pairs of the service nodes, each service-node relationship having an associated transport demand attribute that specifies a quantity of communication resources required for communication between the associated pair of service nodes;

wherein the model repository further includes one or more server-node relationships between selected pairs of the server nodes, each server-node relationship having an associated transport capacity attribute that specifies a quantity of communication resources available for communication between the associated pair of server nodes; and

the optimization engine is further configured to generate the optimized mapping as a function of the service-node relationships and server-node relationships.

12. The system of claim 9, wherein each server node has an associated set of demand attributes and the optimization engine is further configured to generate an optimized mapping of server nodes in a first user-selected server model to server nodes in a second user-selected server model as a function of the demand attributes of the first server model and capacity attributes of the second server model.

13. An apparatus for identifying optimal allocations of computing resources in a data processing arrangement having a plurality of computing machines that host a plurality of application processes, comprising:

means for establishing a plurality of server models, each server model including one or more server nodes, wherein each server node represents a set of hardware and has an associated set of hardware capacity attribute;

means for designating a layered relationship between the server models, wherein for a first server-model layer immediately above a second server-model layer, the second server-model layer includes respective models that represent the nodes in the first server-model layer;

means for establishing a plurality of service models, each service model including one or more service nodes, wherein each service node represents one or more software components and has an associated demand attribute and an associated set of capacity attributes;

means for designating a layered relationship between the service models, wherein for a first service-model layer immediately above a second service-model layer, the second service-model layer includes respective models that represent the nodes in the first server-model layer;

means for generating an optimized mapping of service nodes in a first user-selected service model in a first layer to service nodes in a second user-selected service model in a second layer as a function of the demand attributes of service nodes of the first service model and capacity attributes of service nodes the second service model, wherein the demand attribute of a service node in the first layer includes processing demand requirements of a number of tasks per unit time and the capacity attribute of a service node in the second layer includes processing capacity of a number of tasks per unit time of a process represented by the service node in the second layer; and

means for generating an optimized mapping of service nodes in a user-selected service model to server nodes in a user-selected server model as a function of the demand and capacity attributes.