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Performance evaluation of shared hosting security methods
Mirheidari, S. A

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Performance evaluation of shared hosting security methods
Author :   Mirheidari, S. A
Publisher :  
Pub. Year  :   2012
Subjects :   LAMP Apache Security ITK MPM Peruser Shared Hosting Suexec Suphp Electric lamps ...
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  • Title Page
  • Authorization Page
  • Signature Page
  • Acknowledgments (5)
  • Table of Contents (7)
  • List of Figures (11)
  • List of Tables (13)
  • Abstract (13)
  • Chapter 1 Introduction (19)
    • Prerequisites (26)
      • Introduction to Stochastic Optimisation (26)
      • General Formulation of Stochastic Optimisation Problems (27)
      • Single-Timescale Stochastic Optimisation (28)
      • Mixed-Timescale Stochastic Optimisation (29)
  • Chapter 2 Efficient Energy-Aware Routing with Redundancy Elimination (33)
    • Introduction (33)
    • System Model (37)
    • Problem Formulation (38)
      • The Proposed Mixed Integer Linear Programming (39)
    • Lagrangian Relaxation and Optimal Solution to the obtained MILP (41)
      • Total Unimodularity Property of the Coefficient Matrix (43)
      • Updating the Lagrangian Multipliers of the Links (46)
      • Complexity of the Proposed Solution (47)
      • On the Feasibility of the Solution of the Sub-gradient Method for Integer Programming (47)
      • The Generic Energy-Aware Routing Problem Without Redundancy Elimination (48)
    • An Illustrative Example (48)
    • Numerical Results and Comparison (50)
    • Conclusion and Future Work (56)
  • Chapter 3 Optimal Hierarchical Radio Resource Management for HetNets with Flexible Backhaul (57)
    • Introduction (57)
    • System Model (62)
      • Heterogeneous Network Topology (62)
      • Two-Timescale Hierarchical Radio Resource Control Variables (65)
    • Two-Timescale Hierarchical RRM Problem Formulation (72)
      • Coupling of the Long-Term and Short-Term Control Variables (75)
    • Problem Transformation and Decomposition (80)
    • Solution to Problem P2 (82)
      • Hidden Convexity and Global Optimality Condition of P2 (82)
      • Globally Optimal Solution of P2 (83)
    • Implementation Considerations (87)
      • Signalling Flow (87)
      • Signalling Overhead and Computational Complexity (89)
    • Simulation Results (90)
      • Performance Evaluation and Comparison (92)
      • The Average Computational Time and Signalling Overhead (95)
      • Trade-off between Cost Saving and Performance Loss under Flexible Backhaul (98)
      • Convergence of the Proposed Algorithm (99)
    • Discussion of Analytical and Practical Implications (100)
    • Proofs of the Theoretical Results (101)
      • Proof of Lemma 3.5.1 (101)
      • Proof of Proposition 1 (101)
      • Proof of Theorem 3.5.1 (103)
      • Proof of Theorem 3.5.2 (104)
    • Conclusion (106)
  • Chapter 4 Two-Timescale QoS-Aware Cross-Layer Radio Resource Management for HetNets with Flexible Backhaul (107)
    • Introduction (108)
    • System Model (109)
      • Heterogeneous Network Topology (109)
      • Two-Timescale Hierarchical Radio Resource Control Variables (110)
    • QoS-Aware Cross-Layer RRM Problem Formulation (112)
    • QoS-Aware Two-Timescale RRM Solution (113)
      • Problem Transformation and Decomposition (113)
      • Solution to the Long-term Control Problem Pout (115)
      • Signalling Flow (117)
    • Performance Evaluation and Comparison (118)
    • QSI-Aware RRM Solution (122)
      • Why Dynamic QSI Captures the Data Flow Urgency (123)
      • Proposed Cross-Layer QoS-Aware RRM Algorithm (123)
      • Performance Evaluation and Comparison (126)
    • Conclusion (129)
  • Chapter 5 Parallel Stochastic Optimisation Framework (130)
    • Introduction (130)
      • Stochastic Gradient-Based Algorithms (133)
      • Stochastic Majorization-Minimisation (134)
      • Stochastic Parallel Decomposition Method (136)
    • Problem Formulation (138)
    • Proposed Parallel Stochastic Optimisation Algorithm (139)
    • Convergence of the Proposed Algorithm (143)
      • Proof of Theorem 5.3.1 (147)
      • Proof of Theorem 5.3.2 (Convex case) (148)
      • Proof of Theorem 5.3.3 (Non-convex case) (151)
    • Conclusion (155)
  • Chapter 6 Applications of the Proposed Stochastic Method in Large-Scale Machine Learning (156)
    • Introduction to Support Vector Machines (156)
    • SVM Problem Formulation (158)
    • Adopting a Stochastic Setting for Solving Large-Scale SVMs (160)
    • Simulation Results and Discussion (161)
    • Conclusion (165)
  • Chapter 7 Conclusion (166)
  • References (168)
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