An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

Thekkepuryil, Jabir Kakkottakath Valappil; Suseelan, David Peter; Keerikkattil, Preetha Mathew

doi:10.1007/s10586-021-03269-5

Cited by 36 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [30], a hybrid ACO-PSO algorithm is proposed to optimize a cloud-specific workflow schedule. Additionally, in this scheduling, the authors also focused on security by using the Data Encryption Standard (DES) algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

Anka

2024

Preprint

View full text Add to dashboard Cite

Real-time processing demands are also increasing significantly with the proliferation of Internet of Things (IoT)-based smart systems. At the same time, the volume of data generated, and computational workload are also increasing significantly. In this regard, fog-cloud architectures are proposed to alleviate the excessive load on cloud servers. However, since the resources of fog nodes and the virtual machines on them are limited, efficient management mechanisms are required. As the volume and amount of data increases, computational and processing costs increase, and delays are inevitable. However, these requirements need to be resolved to increase QoS and customer satisfaction. One of the most important criteria to achieve this goal is accurate and effective task allocation and scheduling. Since the scheduling is a type of NP-hard problem, the metaheuristic approach is used. In this study, an Enhanced variant of the Sand Cat Swarm Optimization algorithm (ESCSO) is proposed to efficient scheduling according to tasks priorities and a suitable fitness function based on completion (makespan) time, energy consumption and execution cost parameters is defined. In the proposed algorithm, global search ability and population diversity of the SCSO is improved based on the chaotic map. Also, its exploration and exploitation mechanisms are enhanced based on Brownian and Levy motion strategies. Besides, the control mechanism of the phases transition is redefined to increase balancing performance. The proposed method is compared with SCSO, Improved Artificial Hummingbird Algorithm (IAHA), Discrete Moth Flame Optimization (DMFO), Enhanced Particle Swarm Optimization (EPSO), Chaotic-Whale Optimization Algorithm (CWOA), Modified Harris-Hawks Optimization (MHHO), and Hybrid Artificial Ecosystem Optimization and Salp Swarm Algorithm (AEOSSA) algorithms and analyzed on the three parameters in two different scenarios. The obtained results present that the ESCSO algorithm outperform others in all cases.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

Anka

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, [12] also uses a hybrid approach and combines PSO for local search and BSO to further optimize the search, reducing the cloud center's overall cost in the simulated environment. Additionally, there are many hybrid approaches, like the GA-PSO hybrid approach [16], and the PSO-Cuckoo hybrid approach [23] to improve the QoS parameters of cloud centers. The main limitation of these hybrid approaches is that they fail to explain why they are better than other combinations.…”

Section: Meta-heuristic Techniquementioning

confidence: 99%

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Goyal

Awasthi

2023

Preprint

View full text Add to dashboard Cite

Cloud data centers remove the hardware barriers for the world with enormous storage and computing capabilities and provide access from anywhere over the internet with the concept of virtual machines (VM) and physical machines (PM). These cloud centers raise the problem of lack of resource utilisation and energy waste. These drawbacks have led many researchers to propose VM placement techniques that improve resource utilization and energy consumption. The research proposes VM migration/consolidation and host selection techniques to improve the resource utilisation of cloud centers. This research suggests hybrid black widow optimization (HBWO) and a combined machine learning model to mitigate the problem of cloud centers. HBWO optimizes the VM placement sub-process using an evolutionary approach and a combined machine-learning-based model to predict the over and under-utilised hosts. The effectiveness of the proposed approach was analysed and compared with a Power-Aware Best-Fit Decrease (PABFD) approach and a Particle Swarm Optimization (PSO) approach. The proposed approach improves energy utilisation by 30-40\% over PABFD and 20-15\% over PSO. Moreover, the combined approach of HBWO and machine learning improves the overall performance by 40-50\% compared to PABFD and 20-30\% compared to PSO.

show abstract

“…In cloud computing, workflow scheduling is a practice of allocating tasks to the cloud resources in order to fulfill the user requirements as per the service level agreement (SLA). 1,2,3,4,5 . It has many business and scientific uses, such as in astronomy, weather predicting, medicine, and bio-informatics.…”

Section: Introductionmentioning

confidence: 99%

Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach

Jaiprakash,

Arya,

Gupta

et al. 2023

Preprint

View full text Add to dashboard Cite

The energy consumption of cloud data centers is a critical concern that could affect both the environment and the availability of energy resources. For this, the global community and industries are taking measures to address this issue that is caused by the high electricity consumption of servers, Heating, Ventilation, and Air Conditioning (HVAC) in the data centers. With this context, this paper presents a novel approach for scheduling energy-efficient workflows (EEWS) in cloud computing using the MaxUtil model. The proposed approach incorporates the flower pollination algorithm (FPA), a popular meta-heuristic algorithm inspired by nature. The primary objectives of the proposed scheduling scheme are to minimize energy consumption and workflow processing time (makespan). The proposed algorithm involves two key phases: (i) assigning tasks to available virtual machines (VMs) and (ii) scheduling the tasks based on optimal criteria. As per our knowledge, this is the first study that focuses on optimizing energy consumption and makespan in cloud computing workflow scheduling using FPA. The proposed approch employs an effective representation of pollen and dynamic fitness function with multi-objective. The advantage of FPA lies in its speed of convergence and providing feasible solutions. Extensive studies have been conducted across five different scientific workflows from various fields. The proposed algorithm outperforms traditional workflow scheduling algorithms based on particle swarm optimization (PSO), gravitational search algorithms (GSA) and genetic algorithm (GA). The proposed algorithm outperforms GA, PSO, and GSA in the majority of cases, according to simulation findings. In addition, a well-known statistical test known as variance analysis (ANOVA) is used to validate the experimental results of the suggested algorithm. Based on the result’s of ANOVA test, the article claims that the suggested algorithm is superior to existing methods.

show abstract

An effective meta-heuristic based multi-objective hybrid optimization method for workflow scheduling in cloud computing environment

Cited by 36 publications

References 33 publications

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

HBWO-CML: An energy-efficient Hybrid Black Widow Optimisation and a Combined Machine Learning approach for VM Placement

Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach

Contact Info

Product

Resources

About