Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment

Tarafdar, Anurina; Debnath, Mukta; Khatua, Sunirmal; Das, Rajib

doi:10.1007/s10723-021-09548-0

Cited by 31 publications

(10 citation statements)

References 34 publications

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“…The authors used normalised adaptive league championship algorithm (NALCA) for minimisation of makespan of tasks. Tarafdar et al [37] proposed two scheduling approaches for task in cloud environment to minimise energy and makespan: Greedy Heuristic and Ant Colony Optimisation (ACO). These scheduling techniques aim to balance between the energy consumption in cloud infrastructure and the average makespan of the tasks.…”

Section: Related Workmentioning

confidence: 99%

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

Freire

Frantz

Basto‐Fernandes

et al. 2023

Preprint

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Contemporaneous environments involve a large number of information which is received from many interconnected devices and integrated by many platforms. The Internet of Things and Cloud computing offer services to deal with such big data, so that enterprises can play in their business core by outsourcing the development, the configuration, and the maintenance of those services and their computational resources. Many enterprises use integration Platform as a Service to maintain their applications and data working in a synchronous way. However, current task scheduling algorithms of integration platforms are facing some hardships to tackle with high volumes of data. In this article, we propose a scheduling algorithm based in the round-robin heuristic through multiple task queue, which presents better performance than the traditional First-in-first-out heuristic used by current platforms. We experimented our algorithm in the execution of an integration process, and we validated the results with statistical techniques.

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Section: Related Workmentioning

confidence: 99%

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

Freire

Frantz

Basto‐Fernandes

et al. 2023

Preprint

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“…In [26] a bio-inspired approach was proposed to solve the bi-objective optimization problem for the system makespan and the energy consumption objectives. In [27], the authors employed linear weighted sum techniques to minimize both energy and makespan. In [3], [28], the probabilistic approach is used to determine the probability of on-time completion of tasks on available computing resources.…”

Section: Related Workmentioning

confidence: 99%

FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

Mokhtari¹,

Hossen²,

Jamshidi³

et al. 2022

Preprint

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Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGA) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multiobjective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

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“…The problem of real‐time task scheduling under energy‐awareness has been extensively studied 15‐19 . Xiao et al 15 proposed an algorithm called MSLECC.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, the goal of the work in this article is to reduce the energy consumption of the system. Tarafdar et al 16 proposed an algorithm based on a combination of linear weighting and greedy strategy as a way to reduce the system energy consumption when executing deadline sensitive tasks in cloud computing systems. The algorithm uses task execution time and energy consumption to calculate the weight of each node, and finally selects the VM with the highest weight according to the greedy strategy.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of real-time task scheduling under energy-awareness has been extensively studied. [15][16][17][18][19] Xiao et al 15 proposed an algorithm called MSLECC. The main feature of this approach is to reduce the execution time of the system by preassigning, for each unassigned task, the minimum energy consumption that satisfies the constraint.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Time‐constrained and reliability aware energy minimization scheduling algorithm for heterogeneous multiprocessor environments

Wang

Fan

2022

Concurrency and Computation

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Heterogeneous multiprocessor systems are now widely used in industry by providing high performance and high concurrency. However, with the increasing number of computational nodes, it leads to a dramatic increase in energy consumption of heterogeneous multiprocessor systems. Most of the current research has been conducted to reduce the energy consumption by reducing the processor frequency and extending the task execution time, but these measures often lead to a significant decrease in system reliability. This article addresses the problem of energy-aware task scheduling in the case of distributed computing systems with deadline and reliability constraints.First, a time and reliability allocation model is built to assign the deadline and reliability constraints to each task, which ensures fairness among tasks. Then, a three-stage scheduling algorithm is proposed to minimize the system energy consumption. The static energy consumption is minimized by shutting down the inefficient processors.The dynamic energy consumption of the system is reduced by distributing tasks as evenly as possible on each processor through a task redistribution strategy. Finally, experimental results on real scientific workflows and randomly generated graphs show that the proposed algorithm outperforms other algorithms in terms of energy reduction.

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Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment

Cited by 31 publications

References 34 publications

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

A Multi-queue Round Robin Algorithm for Task Scheduling in Enterprise Application Integration Platforms in the Cloud

FELARE: Fair Scheduling of Machine Learning Tasks on Heterogeneous Edge Systems

Time‐constrained and reliability aware energy minimization scheduling algorithm for heterogeneous multiprocessor environments

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