Energy and Reliability-Aware Task Scheduling for Cost Optimization of DVFS-Enabled Cloud Workflows

Cao, E; Musa, Saira; Chen, Mingsong; Wei, Tongquan; Wei, Xian; Fu, Xin; Qiu, Meikang

doi:10.1109/tcc.2022.3188672

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…Further, its exploitation phase uses a non‐dominated and crowding distance sorting to find the best solution, and its exploration phase uses a single‐point crossover method to generate new chromosomes and two mutation operators to replace some gene values with new values to get a proper mix of possible solutions. Finally, in Reference 79, an initial population of the algorithm consists of chromosomes generated using a random method and HEFT algorithm 43 . Further, in its exploitation phase, the algorithm uses a fitness function that calculates the fitness of a chromosome by evaluating its overall cost.…”

Section: Analytical Discussionmentioning

confidence: 99%

“…The authors in Reference 79 have proposed a reliable, cost‐effective, and efficient meta‐heuristic task scheduling algorithm for workflow applications. Unlike existing scheduling algorithms based on GA that take random initial population, the proposed algorithm takes into consideration three factors: (i) current task to VM mapping, (ii) VM to a specific configuration of the VM, and (iii) task to CPU frequency mapping for the initial population of schedules.…”

Section: Taxonomy Of Energy‐efficient Workflow Scheduling Approachesmentioning

confidence: 99%

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A survey on energy‐efficient workflow scheduling algorithms in cloud computing

Verma,

Maurya,

Yadav

2023

Softw Pract Exp

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The advancements in computing and storage capabilities of machines and their fusion with new technologies like the Internet of Thing (IoT), 5G networks, and artificial intelligence, to name a few, has resulted in a paradigm shift in the way computing is done in a cloud environment. In addition, the ever‐increasing user demand for cloud services and resources has resulted in cloud service providers (CSPs) expanding the scale of their data center facilities. This has increased energy consumption leading to more carbon dioxide emission levels. Hence, it becomes all the more important to design scheduling algorithms that optimize the use of cloud resources with minimum energy consumption. This paper surveys state‐of‐the‐art algorithms for scheduling workflow tasks to cloud resources with a focus on reducing energy consumption. For this, we categorize different workflow scheduling algorithms based on the scheduling approaches used and provide an analytical discussion of the algorithms covered in the paper. Further, we provide a detailed classification of different energy‐efficient strategies used by CSPs for energy saving in data centers. Finally, we describe some of the popular real‐world workflow applications as well as highlight important emerging trends and open issues in cloud computing for future research directions.

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Section: Analytical Discussionmentioning

confidence: 99%

Section: Taxonomy Of Energy‐efficient Workflow Scheduling Approachesmentioning

confidence: 99%

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

Verma,

Maurya,

Yadav

2023

Softw Pract Exp

View full text Add to dashboard Cite

show abstract

“…In Reference 17, a multi‐objective evolutionary list scheduling algorithm was proposed to optimize both cost and execution time. Cao et al 18 investigated cost optimization for energy and reliability‐aware workflow scheduling in clouds where the dynamic voltage and frequency scaling technique is enabled.…”

Section: Background and Related Workmentioning

confidence: 99%

A probabilistic modeling and evolutionary optimization approach for serverless workflow configuration

Wang,

Wu,

Zhang

et al. 2023

Softw Pract Exp

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Serverless computing has nowadays become a mainstream paradigm to develop cloud‐native applications owing to its high scalability, ease of usage and cost‐effectiveness. Nevertheless, because of its poor infrastructure transparency, two main challenges emerge when users migrate their applications to a serverless platform: the lack of an effective analytical model for performance and billing, and the trade‐off problem between them. In this paper, we formally define a serverless workflow and introduce the concept of execution instances. Based on them, a probabilistic performance and cost evaluation model is built to obtain their expected values for an input serverless workflow. Then, we design a tailored evolutionary optimization algorithm called EASW to tackle budget‐constrained performance optimization and performance‐constrained cost optimization problems. Extensive experiments were carried out to test the proposed model and optimization algorithm on AWS Lambda. Results reveal that our model can achieve an accuracy over 98% and EASW can yield a better memory configuration solution than existing methods for constrained optimization.

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“…T HE CLOUD computing paradigm (CCP) has radically altered the computing sector by relieving users/consumers of the burden of operating their own, sometimes costly, information technology infrastructure [1], [2]. CCP's infrastructure has elastic, reliable, and cost-effective computing resources to accommodate millions of physical machines, storage devices, network equipment, and cooling facilities [3]. These resources are shared across global Industrial-Internet-of-Things (IIoT) users to execute a range of heterogeneous deadline-constrained applications, such as scientific computing, high-performance simulation, big data analysis, and e-commerce [4].…”

Section: Introductionmentioning

confidence: 99%

Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

Gad

Houssein

Zhou

et al. 2024

IEEE Internet Things J.

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In recent years, ongoing advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is one of the difficulties of cloud computing, which is typically referred to as a Task-Scheduling-in-Cloud-Computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this paper introduces an Opposition-based Simulated Annealing Particle Swarm Optimizer (OSAPSO) to address PSO's premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, the swarm is formed at random by using OBL to guarantee swarm diversity with a light computational burden. A multi-way tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion-scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good trade-off between solution diversity and convergence speed of the proposed method. Due to the non-convex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multi-scale IIoT heterogeneous task groups (realistic and synthetic). In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO has a winning performance and is statistically more This paragraph of the first footnote will contain the date on which you submitted your paper for review. It will also contain support information, including sponsor and financial support acknowledgment.

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Energy and Reliability-Aware Task Scheduling for Cost Optimization of DVFS-Enabled Cloud Workflows

Cited by 9 publications

References 34 publications

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

A survey on energy‐efficient workflow scheduling algorithms in cloud computing

A probabilistic modeling and evolutionary optimization approach for serverless workflow configuration

Damping-Assisted Evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

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