Analyzing the tradeoffs between breakup and cloning in the context of organizational self-design

1. Analyzing the tradeoffs between breakup and cloning in the context of organizational self-design By SachinKamboj

2. Abstraction • Organizational Self-Design (OSD) • Constructing suitable organizations at runtime • Agents are responsible for their own organizational structures • Spawning a new agent • Agent is overloaded • Composing agents • When they are free • “Breaking" up a problem • For assigning one of the sub-problems to the newly spawned agent • “Cloning" the source agent • For assigning the clone agent a portion of the source’s work load

3. Goal • Analyze the tradeoffs between cloning and breakup and generate a hybrid model that uses both cloning and breakup to generate more suitable organizations than those that could be generated when using a single approach

4. Previous findings • Contingency theory • There is no best way to organize • All ways of organizing are not equally effective • The optimal organizational structure depends on the problem being solved and the environmental conditions

5. Practically • OSD is particularly suited to the problem of generating virtual organizations for grid/volunteer/cloud computing environments • Volunteer computing • type of distributed computing in which computer owners donate their computing resources • Cloud computing • the provision of dynamically scalable and often virtualised resources as a service over the Internet

6. The Model • Problem solving requests (tasks) arrive at the multi-agent system at indeterminate times • Asingle agent responsible for solving the problem in its entirety • Possibilities to handle overload: • “Breakup “ - it spawns off a new agent to handle part of its load divide the problem into smaller sub-problems • “Cloning” - The individual problems are solved in their entirety by the two agents

7. Advantages and disadvantages • Breakup • Adv • Only option if the task is too “big” for any single agent • Use less resources than cloning • Better in situations in which the agents include a learning component • Disadv • Interdependent breakup would require more coordination between the agents • Cloning • Adv • Only option if task cannot be broken up into smaller parts • Better in executing the interdependent subtasks

8. TÆMS • Computational framework for representing and reasoning about complex task environments • Tasks are represented using extended hierarchical task structures • Root represents the high-level goal that the agent is trying to achieve • Sub-nodes represent the subtasks and methods that make up the high-level task • Leaf nodes are at the lowest level of abstraction and represent executable methods - primitive actions

9. TÆMS – in more detail • Executable methods • May have multiple outcomes • Different probabilities • Different characteristics • Quality • Cost • Duration • Various mechanisms for specifying subtask variations and alternatives • Characteristic accumulation function • Describes how many or which subgoals or sets of subgoalsneed to be achieved in order

10. TÆMS – In real world • Distributed sensor networks • Information gathering • Hospital scheduling • EMS - Engine Management System • Military planning

11. Task Structure • < T; τ;M; Q;E;R; ρ;C > • T - set of tasks, the non-leaf nodes of a TÆMS task structure with quality and quality/characteristic accumulation function • τ - root of the task structure • M – set of primitive methods that can be directly executed by an agent • With the probability that the outcome will have a quality/cost/duration • Q - is the set of quality/characteristic accumulation functions • E - set of (non-local) effects • R - set of resources • ρ - mapping from an executable method and resource to the quantity of that resource needed (by an agent) to schedule/execute that method • C - mapping from a resource to the cost

12. OSD • An evaluation component • Monitoring the performance • An adaptation component • Triggered by the evaluation component when the utility falls below a threshold to modify the organizational structure

13. Local Task Structure • Obtained by rewriting the global task structure and represent the local task view of the agent vis-a-vis its role in the organization and its relationship to other agents • No single agent has a global view of the complete organization

14. organizational nodes • Two flavors • Organizational tasks • Used to aggregate other organizational nodes • Organizational methods • Represent either organizational knowledge or organizational actions that have some fixed semantics • Types • Container-Nodes • Aggregates of domain nodes and other organizational nodes • Non-Local-Nodes • Represent nodes in the global task structure that the agent knows the identity of but does not know the characteristics • Clone Selectors • Used to select amongst the clones of a node • NLE-Inheritors • Transfer the non-local effect from a non-cloned node to a cloned node or vice versa

15. Organization Structure • Breakup • Need to consider interrelationships (NLEs) • The two agents can negotiate a coordination • Non-local-nodes will be added to the root-node • Merging • Exact inverse of breakup • Should be associative • Merge order is not important • Cloning • Situation • Breakup might be infeasible • The agent would prefer to do simple load balancing • Use • Clone container to hold all the created nodes • Clone selector to enable one or more clones • Careful handle of boundaries • Preserve their original semantics • Allow the presence of clones to be transparent to the non clone nodes

16. Spawning Strategies • Breakup • Task structure is always broken up into smaller subtasks • Used the Balancing Execution Time (BET) heuristic to select the node to be allocated to the new agent • Prefer Breakup • Same as breakup but with the exception that if Breakup is infeasible, the Cloning approach is used

17. Spawning Strategies … • Cloning • The root of the task structure is always cloned and assigned to the newly spawned agent • Prefer Cloning • Similar to the Cloning approach, with the exception that if Cloning is infeasible given the current task load, the agent will Breakup according to the BET heuristic

18. Spawning Strategies … • Hybrid Model • A combination of cloning the highest level goal and breakup according to the BET heuristics • Utility value, which is the expected utility of breaking up according to the BET heuristics • If utility is greater than a constant called the breakup threshold, the agent chooses to breakup • Otherwise it clones the highest level node