Work | Highlights | MK | DL | CT | DT | DY | RL | SC | EN | HM | WL |
---|---|---|---|---|---|---|---|---|---|---|---|

[105] | Dynamic level scheduling (DLS) | x | . | . | o | x | . | . | . | . | x |

[126] | Wide-area scheduling with dynamic load balancing | . | . | . | x | x | . | . | . | o | . |

[57] | Dynamic Critical Path (DCP) | o | . | . | . | x | . | . | . | . | x |

[99] | Integration to conventional schedulers. | . | . | . | . | . | . | . | . | o | . |

[2] | ELISA, decentralized dynamic algorithm | . | . | . | . | x | . | . | . | o | . |

[51] | Hierarchical scheduling | o | . | . | . | . | . | . | . | . | o |

[27] | Federation of resource traders | . | . | . | . | . | . | . | . | o | . |

[117] | HEFT (Heterogeneous Earliest Finish Time) | x | . | . | . | . | . | . | . | . | . |

[113] | Redundantly distribute job to multiple sites to increase backfilling | . | . | . | . | . | . | . | . | o | . |

[30] | Performance and reliability optimization | x | . | x | . | x | x | . | . | . | x |

[16] | Reduce maximum job waiting time in the queue | x | . | . | . | . | . | . | . | o | . |

[3] | Community of peers for brokering | . | . | . | . | o | . | . | . | o | . |

[49] | Fault-tolerant scheduling | . | . | . | . | . | x | . | . | . | x |

[79] | Dynamic, deadline, energy | . | x | . | . | x | . | . | x | . | x |

[97] | Rescheduling policies | x | . | o | . | x | . | . | . | . | x |

[58] | Auction-based scheduling. | . | . | . | . | . | . | . | . | o | . |

[138] | Deadline partitioning | . | x | o | . | . | . | . | . | . | x |

[121] | Dynamic voltage scaling | . | x | o | . | o | . | . | x | . | x |

[137] | Genetic algorithm to optimize cost with deadline constraint | . | x | x | . | . | . | . | . | . | x |

[149] | Merge multiple DAGs | x | . | . | . | . | . | . | . | . | x |

[104] | Makespan and robustness | x | . | . | . | . | x | . | . | . | x |

[102] | Load balancing on arrival | . | . | . | . | o | . | . | . | o | . |

[98] | LOSS and GAIN approaches | x | . | x | . | . | . | . | . | . | x |

[43] | Performance and reliability optimization | x | . | . | . | . | x | . | . | . | x |

[31] | Reliable HEFT | x | . | . | . | . | x | . | . | . | x |

[139] | Minimize execution time and cost | x | x | x | . | . | . | . | . | . | x |

[71] | Dynamic scheduling | . | . | . | . | x | o | . | . | . | x |

[90] | Dynamic storage mgmt. | o | . | . | x | . | . | . | . | . | x |

[55] | Energy and deadline | . | x | . | . | o | . | . | x | . | x |

[145] | Forecast prototype and SLA compensation | . | . | x | . | . | . | . | . | . | . |

[146] | Historical information, forecasting | . | . | x | . | . | . | . | . | . | . |

[47] | Delegated matchmaking, local vs remote usage | . | o | . | . | o | . | . | . | o | . |

[29] | Improve average response time | o | . | . | . | . | . | . | . | o | . |

[142] | Float time amortization | . | x | o | . | . | . | . | . | . | x |

[142] | Based on HEFT | x | . | . | . | . | . | . | . | . | x |

[83] | Bandwidth speedup, data-intensive | o | . | . | x | . | . | . | . | . | x |

[89] | Makespan and energy | x | . | . | . | . | . | . | x | . | x |

[133] | MQMW (Multiple QoS scheduling of Multi-Workflows) | x | . | x | . | x | . | . | . | . | x |

[84] | RASA (Resource-Aware Scheduling Algorithm | x | . | . | . | . | . | . | . | . | . |

[59] | Decentralized model that improves makespan | x | . | . | . | . | . | . | . | o | . |

[35] | Fuzzy approach for decentralized grids | o | . | . | . | . | . | . | . | o | . |

[93] | Backfilling strategy based on dynamic information | x | . | . | . | x | . | . | . | o | . |

[23] | Ant Colony Optimization | x | x | x | . | . | x | . | . | . | x |

[140] | Path-based deadline partition | . | x | . | . | . | . | . | . | . | x |

[141] | Greedy time-cost distribution | . | . | x | . | . | . | . | . | . | x |

[61] | Optimize makespan and resource utilization | x | . | . | . | x | . | . | . | . | x |

[114] | Similar to YU et al., 2007 | x | x | x | . | . | . | . | . | . | x |

[13] | Data staging | x | o | . | x | . | . | . | . | . | x |

[96] | QoS-aware, cost and execution time | x | . | x | . | . | . | . | . | . | . |

[153] | Based on genetic algorithm; increase resource utilization | . | . | . | . | x | . | . | . | . | . |

[100] | Cost-based | . | . | x | . | . | . | . | . | . | . |

[67] | Time-cost-based, instance-intensive workflows | x | x | x | . | . | . | . | . | . | x |

[82] | Particle swarm optimization heuristic; | . | . | . | x | x | . | . | . | . | x |

[94] | Brokering for multiple grids. | . | . | . | . | . | . | . | . | o | . |

[122] | Bidding system for resource selection | o | . | . | . | . | . | . | . | . | . |

[128] | PSO to minimize cost with deadline constraint | o | x | x | . | . | . | . | . | . | x |

[39] | Optimize makespan and cost | x | . | x | . | . | . | . | . | . | x |

[88] | Dynamic programming | x | . | x | . | . | x | . | . | . | x |

[25] | Dynamic scheduling | . | . | . | . | x | o | . | . | . | x |

[11] | Energy efficiency | . | . | . | . | o | . | . | x | . | . |

[131] | Reputation-based QoS provisioning | o | o | x | . | . | . | . | . | . | . |

[74] | Deadline, budget, auto-scaling | . | x | x | . | o | . | . | . | . | . |

[64] | SHEFT (Scalable HEFT) | x | . | . | x | x | . | . | . | . | x |

[119] | OWS (Optimal Workflow Scheduling); | x | . | . | . | . | . | . | . | . | x |

[132] | Justice-based scheduling | x | . | . | . | o | . | . | . | o | . |

[151] | Budget-constrained HEFT | . | . | x | . | x | . | . | . | . | x |

[62] | CCSH to minimize makespan and cost | x | . | x | . | . | . | . | . | . | x |

[19] | Deadline optimization based on delaying | . | x | . | . | x | . | . | . | . | x |

[73] | Multiple DAGs; deadline-based | o | x | o | . | x | . | . | . | . | x |

[15] | Hybrid clouds; iteratively resch. tasks until mksp.; deadline | x | x | o | . | x | . | . | . | x | x |

[60] | Makespan and energy | x | . | . | . | . | . | . | x | . | x |

[78] | Makespan and energy | x | . | . | . | o | . | . | x | . | x |

[116] | MapReduce on public clouds | . | x | x | . | . | . | . | . | . | . |

[50] | Multi-tier applications | o | . | o | . | . | . | . | . | . | . |

[143] | Auction-based, cloud-provider viewpoint | . | . | . | . | . | . | . | . | . | . |

[40] | Heterogeneous workloads | . | x | . | . | . | . | . | . | . | . |

[54] | SLA management, improve resource utilization | . | o | o | . | . | . | . | . | . | o |

[107] | Multi-cloud, cost optimization | . | . | x | . | . | . | . | . | x | . |

[37] | Multi-objective, cost constraints | . | . | x | . | . | . | . | . | x | . |

[144] | Backtracking and continuous cost evaluation | o | . | x | . | x | . | . | . | . | x |

[33] | Multi-objective scheduling | x | . | x | o | . | x | . | x | . | x |

[12] | Pareto-based; execution time and cost | x | . | x | . | . | . | . | . | . | x |

[118] | Combination of DAG merging techniques | x | . | . | . | . | . | . | . | . | x |

[70] | Auto-scaling of resources | o | x | x | o | . | . | . | . | . | x |

[124] | Fault-tolerant scheduling | . | x | x | . | o | x | . | . | . | x |

[120] | Deadline-driven, scientific applications, hybrid clouds | . | x | . | . | . | . | . | . | x | o |

[148] | Energy-aware, scheduling delay | . | o | . | . | o | . | . | x | . | . |

[20] | Aneka platform; QoS-driven, hybrid | . | x | . | . | o | . | . | . | x | . |

[48] | Cost minimization, deadline | . | x | x | . | . | . | . | . | . | . |

[26] | Negotiation/bargaining | . | x | x | . | . | . | . | . | . | . |

[129] | Market oriented | x | . | x | . | . | . | . | . | . | x |

[46] | Community-aware decentralized dynamic scheduling | o | . | . | . | o | . | . | . | o | . |

[1] | Partial Critical Path (PCP) | o | x | o | . | . | . | . | . | . | x |

[65] | Minimize end-to-end delay | o | . | x | . | . | . | . | . | . | x |

[152] | Monte Carlo approach | x | . | o | . | x | . | . | . | . | x |

[103] | Power aware scheduling | x | . | . | . | o | . | . | x | . | x |

[134] | Particle swarm optimization | x | . | x | . | o | . | . | x | . | x |

[130] | Data-intensive, energy-aware | x | . | . | x | o | . | . | x | . | x |

[42] | Rule-based | . | o | o | . | . | . | . | . | x | . |

[38] | Energy, deadline | . | x | o | . | . | . | . | x | . | . |

[41] | Bag of tasks, time and cost | x | . | x | . | . | . | . | . | . | . |

[106] | SLA-based cost model; power | o | . | x | . | . | . | . | o | . | . |

[136] | Cost management | . | . | o | . | . | . | . | . | . | . |

[5] | Predict Earliest Finish Time (PEFT) | x | . | . | o | . | . | . | . | . | x |

[14] | Cat Swarm Optimization | . | . | x | x | . | . | . | . | . | x |

[95] | PSO considering performance variation and VM boot time | . | x | x | . | x | . | . | . | . | x |

[4] | Aggregation-based budget distribution | . | . | x | . | . | . | . | . | . | x |

[87] | Critical-path heuristic | x | x | x | . | . | x | . | . | . | x |

[86] | Spot instances | . | x | x | . | x | x | . | . | . | x |

[10] | Fault-tolerance | . | . | x | . | o | x | . | . | . | . |

[56] | Behavioral-based estimation | . | . | o | . | x | . | . | . | . | . |

[154] | Multiple workflows, optimize time and cost | x | x | x | . | . | . | . | . | . | x |

[68] | Multi-cloud, enhanced workflow model | o | x | x | x | . | . | . | . | x | x |