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Journal of Cloud Computing

Advances, Systems and Applications

Table 4 Summary of identified related work classified using the consolidated taxonomy

From: Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

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

  1. MK = Makespan/Time; DL = Deadline; CT = Cost/Budget; DT = Data-Intensive; DY = Dynamic; RL = Reliability; SC = Security; EN = Energy; HM = Hybrid/Multicloud; WL = Workload/Workflow;. = Not addressed; x = Fully addressed; o = Partially addressed
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