Cost-aware DAG scheduling algorithms for minimizing execution cost on cloud resources

Convolbo, Moïse W.; Chou, Jerry

doi:10.1007/s11227-016-1637-7

Cited by 48 publications

(14 citation statements)

References 31 publications

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“…For example, a simple case of optimally placing M tasks to R resources has a brute‐force computational complexity of

scriptO false(R^{M} false)

. This is intractable for practical applications with 10 − 100 ′ s of tasks and resources …”

Section: The Taxonomymentioning

confidence: 99%

“…Deng et al decomposed the overall problem of distributing a workload among fog and cloud resources into subproblems using an approximate and solve these subproblems individually using different optimization techniques. Alternatively, the optimal solution, while impractical, offers a theoretical baseline against which to empirically compare their proposed approximate solution …”

Section: The Taxonomymentioning

confidence: 99%

“…This is intractable for practical applications with 10 − 100 ′ s of tasks and resources. 143,144 In some cases, one can stop the brute-force search once a "good enough" solution is found and thus bound the cost of the algorithm, while not getting an optimal solution. There are also approaches that use dynamic programming (DP), wherein the original scheduling problem is decomposed into a set of overlapping subproblems, each of which can be solved optimally in tractable time.…”

Section: Scheduling Techniquesmentioning

confidence: 99%

“…Alternatively, the optimal solution, while impractical, offers a theoretical baseline against which to empirically compare their proposed approximate solution. 88,143 Consequently, scheduling problems are often solved using suboptimal algorithms that resort to heuristics or metaheuristics that, in practice, come close to optimal solutions but within a reasonable time. Greedy algorithms select the most promising option from the solution space at any given stage.…”

Section: Scheduling Techniquesmentioning

confidence: 99%

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Characterizing application scheduling on edge, fog, and cloud computing resources

Varshney

Simmhan

2019

Softw Pract Exp

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Summary Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, edge and fog computing resources have emerged on the wide‐area network as part of Internet of things (IoT) deployments. These three resource abstraction layers are complementary, and offer distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and data flow models offering a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still maturing, and can benefit from learnings on cloud resources. At the same time, there is also value in using these resources cohesively for application execution. In this article, we offer a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, fog, and cloud computing resources. We first characterize the resource capabilities and limitations of these infrastructure and offer a taxonomy of application models, quality‐of‐service constraints and goals, and scheduling techniques, based on a literature review. We also tabulate key research prototypes and papers using this taxonomy. This survey benefits developers and researchers on these distributed resources in designing and categorizing their applications, selecting the relevant computing abstraction(s), and developing or selecting the appropriate scheduling algorithm. It also highlights gaps in literature where open problems remain.

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“…For example, a simple case of optimally placing M tasks to R resources has a brute‐force computational complexity of

scriptO false(R^{M} false)

. This is intractable for practical applications with 10 − 100 ′ s of tasks and resources …”

Section: The Taxonomymentioning

confidence: 99%

Section: The Taxonomymentioning

confidence: 99%

Section: Scheduling Techniquesmentioning

confidence: 99%

Section: Scheduling Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

Characterizing application scheduling on edge, fog, and cloud computing resources

Varshney

Simmhan

2019

Softw Pract Exp

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“…Task scheduling can be divided into uniprocessor scheduling and multiprocessor scheduling. At present, the problem of multiprocessor scheduling for a DAG task is approaching, and the research on different target resources, scheduling objectives and scheduling methods is approaching maturity [6][7]. With the rapid development of grid computing and cloud computing applications, in a heterogeneous distributed computing environment on how to improve the multiple task scheduling performance aspects put forward the new requirements, also caused the wide attention of scholars both at home and abroad [8][9].…”

Section: Introductionmentioning

confidence: 99%

A novel HEFTS-L algorithm for scheduling large number of DAG tasks

Liu¹,

Xu²,

Cheng³

et al. 2017

Proceedings of the 3rd Workshop on Advanced Research and Technology in Industry (WARTIA 2017)

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Abstract:In recent years, along with the development of technologies for distributed computing such as big data and clouds workflow systems, efficiency of workflow scheduling has become very impotent. Hence scheduling of multiple DAGs sharing on heterogeneous distributed resources has attracted intensive attention recently. This paper issues on scheduling of multiple DAGs with Deadline constraints surrounding scheduling of multiple DAGs sharing on heterogeneous distributed resources on traditional DAG-based task. It brings forward two parameter association methods to balance DAG Deadline constraint priority including relative strictness and laxity, which are used to balance priority of multiple DAGs. An improved HEFT algorithm based on priority was put forward to schedule multiple DAGs with deadline constraint. Through experiments, the phenomenon of "overload" can be detected due to the high degree of emergency of DAG scheduling. Once a task was detected, it can be selective discarded with corresponding measures, so as to maximize DAG before deadline.

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Multi‐objective GA to schedule task graphs on heterogeneous voltage frequency islands

Sanchit,

Singh,

Singh

2024

Concurrency and Computation

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SummaryEnergy consumption of multiprocessor's system is increasing day by day. The capability of multiprocessor systems and high compute‐intensive tasks play a major role in increasing energy consumption. Voltage frequency island (VFI) architecture partitioned the cores into groups for which voltage/frequency can be controlled by a single switch. VFI plays a major role in optimizing the energy consumption. We have generated the initial population by using the slot technique to VFI architecture. The genetic algorithm studied by many researchers to solve scheduling problems. So we combined the genetic algorithm with the VFI‐enabled architecture and slot approach called VFIGen. Then apply the VFIGen algorithm to optimize the energy consumption. When comparing the results of the proposed one with the existing state‐of‐art we achieved the performance gain by to .

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Cost-aware DAG scheduling algorithms for minimizing execution cost on cloud resources

Cited by 48 publications

References 31 publications

Characterizing application scheduling on edge, fog, and cloud computing resources

Characterizing application scheduling on edge, fog, and cloud computing resources

A novel HEFTS-L algorithm for scheduling large number of DAG tasks

Multi‐objective GA to schedule task graphs on heterogeneous voltage frequency islands

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