Physical Assembly Mapper: A Model-driven Optimization Tool for QoS-enabled Component Middleware Vanderbilt University Nashville, Tennessee Institute for Software Integrated Systems RTAS 2008, April 22, 2008 Krishnakumar Balasubramanian, Douglas C. Schmidt {kitty,schmidt}@dre.vanderbilt.e du (presented by Aniruddha Gokhale)
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Physical Assembly Mapper: A Model-driven Optimization Tool for QoS- enabled Component Middleware Vanderbilt University Nashville, Tennessee Institute for.
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Physical Assembly Mapper:A Model-driven Optimization Tool for QoS-enabled Component Middleware
Vanderbilt University Nashville, Tennessee
Institute for Software Integrated Systems
RTAS 2008, April 22, 2008
Krishnakumar Balasubramanian, Douglas C.
Schmidt{kitty,schmidt}@dre.vanderbilt.edu(presented by Aniruddha Gokhale)
Collocation Group Application AssemblyDeployment Plan
Intuition behind Deployment-time Fusion Soln• If n = no. of candidate elements
for fusion, k = no. of elements resulting from fusion, savings due to fusion will be (n – k ) / n
• Best case if k = 1, i.e., fusion creates a single element
• Given an undirected graph
G = (V,E) (fusion graph)
• V = {Candidate elements}
• E = {(u,v) | u, v are elements and CanMerge (u, v) is true}
• Finding largest set of elements that can be fused together = Finding maximum clique in G
• Well-known NP-Complete problem
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Deployment-time Fusion Approach• Enumerate all maximal cliques
• NP-Hard; O(3n/3) time complexity• Our approach
• Use modified Bron-Kerbosch (BK) algorithm to enumerate maximal cliques
• Fastest known algorithm• Use domain-specific heuristics
• Stop enumeration after first maximal clique
• Remove vertices & repeat (safe due to characteristics of BK)
• Only use elements which occur equal number of times as candidates (for component fusion only)
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Maximum Clique
Maximal Clique
Motivating Application
• US Navy Shipboard Computing System• Consists of 150 components – 10 “operational strings” with 15 components
each; deployed across 5 nodes• Sensors – Periodically sends information to the planners• System Monitors – Publish information about health of system• Planners – Process sensor & system monitor input• Effectors – Carry out planner-specified actions
component attributes• Maintains the same number of connections
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Footprint Experiments Setup• Experiments were conducted
using ISISlab• Five nodes running Windows
XP SP2• CIAO Version 0.5.10 used as
baseline for comparison• Two kinds of footprint
measurements• Static – Code & Static
Data• Dynamic – Heap Memory
used • Use vadump.exe to take a
snapshot of working set of process hosting components
• Measure number of private & shareable pages
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Footprint Results (1/2)
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Node Specific Static Footprint
Node Specific Dynamic Footprint
Total Static Footprint Total Dynamic Footprint31% reduction
49% reduction
18% reduction
45% reduction
Footprint Results (2/2)
• Increased footprint reduction with Global vs. Local component fusion due to• More opportunities for merging components• Creation of consolidated deployment plan• Applicable to more than the internal components of an assembly• Reduces the overhead due to factory objects as well as components
30Component Fusion reduces the footprint significantly
Total Footprint18% reduction
45% reduction
Concluding Remarks
34Tools can be downloaded from www.dre.vanderbilt.edu/CoSMIC/
• Our research • Describes a model-driven
approach to deployment-time optimizations
• Two algorithms• Local and Global
component fusion • Implemented via the
Physical Assembly Mapper (PAM)
• PAM’s deployment-time optimization techniques
• Resulted in a 45% decrease in footprint compared to conventional middleware technologies