Chap.8 SCHEDULING AND RESOURCE ALLOCATION - 8.1 Introduction: Energy-Aware Scheduling 8.2 Use of Linear Programming in Energy-Aware Scheduling 8.2.1 Finding the Optimal Solution Using a Linear Program 8.2.2 Benefits and Limitations of LP 8.3 Heuristics in Large Instances 8.3.1 Energy-Aware Greedy Algorithms 8.3.2 Vector Packing 8.3.3 Improving Fast Algorithms 8.4 Comparing Allocation Heuristics for Energy-Aware Scheduling 8.4.1 Problem Formulation 8.4.2 Allocation Heuristics 8.4.3 Results 8.5 Energy-Aware Task Allocation in Mobile Environments 8.5.1 Reference Architecture 8.5.2 Task Allocation Strategy 8.5.3 Task Allocation Algorithm 8.5.4 Performance Results 8.6 An Energy-Aware Scheduling Strategy for Allocating Computational Tasks in a Fully Decentralized Way 8.6.1 Decentralized Resources in Cloud: Overview 8.6.2 Cooperative Scheduling Anti-Load Balancing Algorithm for Cloud (CSAAC) 8.6.3 Simulation Results 8.6.4 Evaluation 8.7 Cost-Aware Scheduling with Smart Grids 8.7.1 Cost-Aware Scheduling 8.7.2 Cost-Aware Scheduling Using DE 8.7.3 Comparison of DE with Other Approaches 8.8 Heterogeneity, Cooling, DVFS, and Migration 8.8.1 Lever Interactions 8.8.2 Infrastructures 8.8.3 Resource Allocation as a Whole 8.9 Conclusions - References