Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing

Saeedi, Sahar; Khorsand, Reihaneh; Bidgoli, Somaye Ghandi; Ramezanpour, Mohammadreza

doi:10.1016/j.cie.2020.106649

Cited by 108 publications

(43 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the communication cost of the IoT application from service request t i to service request t j is defined by using server l through Equation ).

C_{italiccomm} = {italicCP}_{l} \times CT false(e_{ij}^{l} false)

where, CP l is the communication unit price of server and CP l = 0.1$ per Hour 27 . When two service request t i and t j excecuted in a same fog cell ( l ), CT (

e_{italicij}^{l}

) is set to zero, otherwise, it is set to

\frac{{cv}_{ij}}{B}

where, B is the bandwidth between two fog cell (=20 Mbps) and cv ij is the data size (in MB) from service request t i to its successor service request t j .…”

Section: Made Proposed Framework For Autonomic Service Placementmentioning

confidence: 99%

“…To assign a deadline to each service, it is necessary to define the service latency for the shortest placement P , which is obtained by placement each IoT service on a distinct fog cell with the highest rank while data transfer time is regarded as zero. Therefore, the deadline of service can be calculated through Equation ) 27

{italicDeadline of service}_{i} = λ \times P

where, λ is a variance of the service deadline.…”

Section: Performance Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

An autonomous IoT service placement methodology in fog computing

2020

Self Cite

View full text Add to dashboard Cite

With the increase in the number of Internet of Things (IoT) devices having limited resources, an extension of the cloud‐computing paradigm has emerged so‐called fog computing, where all the fog cells are located at the edge of the network and the latency can be reduced. Meanwhile, an important challenge has attracted much attention with the definition of fog computing is service placement problem that is still at its very beginning research. It allows to deployment IoT applications on computational fog resources, with the objective of optimizing quality of service (QoS) requirements of applications while taking into account maximizing the utilization of fog resources. In this paper, an autonomous IoT service placement methodology including four phases of monitoring, analysis, decision‐making, and execution is proposed called as (MADE). First, the available resources and application services' status are monitored at run time. Next, the requested services are prioritized with respect to application services' deadline. Then, the Strength Pareto Evolutionary Algorithm II is applied to take decisions about the application services placement as a multi‐objective optimization problem. Finally, the decisions made in the previous phases are executed in a fog environment. The experiment results indicate that the proposed methodology outperforms its counterparts in terms of different performance metrics.

show abstract

“…Moreover, the communication cost of the IoT application from service request t i to service request t j is defined by using server l through Equation ).

C_{italiccomm} = {italicCP}_{l} \times CT false(e_{ij}^{l} false)

where, CP l is the communication unit price of server and CP l = 0.1$ per Hour 27 . When two service request t i and t j excecuted in a same fog cell ( l ), CT (

e_{italicij}^{l}

) is set to zero, otherwise, it is set to

\frac{{cv}_{ij}}{B}

where, B is the bandwidth between two fog cell (=20 Mbps) and cv ij is the data size (in MB) from service request t i to its successor service request t j .…”

Section: Made Proposed Framework For Autonomic Service Placementmentioning

confidence: 99%

{italicDeadline of service}_{i} = λ \times P

where, λ is a variance of the service deadline.…”

Section: Performance Evaluationmentioning

confidence: 99%

An autonomous IoT service placement methodology in fog computing

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, they considered the centralized cloud environment as the underlying infrastructure and thus ignored the overhead for inter-edge-node data transmission. For a similar optimization objective, Wang et al [23] and Saeedi et al [24] employed an immune-based PSO algorithm for scheduling workflows over centralized clouds.…”

Section: Related Workmentioning

confidence: 99%

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Wang

Zheng

Chen

et al. 2020

Security and Communication Networks

View full text Add to dashboard Cite

Recently, mobile edge computing (MEC) is widely believed to be a promising and powerful paradigm for bringing enterprise applications closer to data sources such as IoT devices or local edge servers. It is capable of energizing novel mobile applications, especially the ultra-latency-sensitive ones, by providing powerful local computing capabilities and lower end-to-end delays. Nevertheless, various challenges, especially the reliability-guaranteed scheduling of multitask business processes in terms of, e.g., workflows, upon distributed edge resources and servers, are yet to be carefully addressed. In this paper, we propose a novel edge-environment-based multi-workflow scheduling method, which incorporates a reliability estimation model for edge-workflows and a coevolutionary algorithm for yielding scheduling decisions. The proposed approach aims at maximizing the reliability, in terms of success rates, of services deployed upon edge infrastructures while minimizing service invocation cost for users. We conduct simulative experimental case studies based on multiple well-known scientific workflow templates and a well-known dataset of edge resource locations as well. Simulative results clearly suggest that our proposed approach outperforms traditional ones in terms of workflow success rate and monetary cost.

show abstract

“…For complex products, of course, several non‐feasible solutions may appear, with low efficiency. Further, Ant Colony System (ACS), 8,9 Particle Swarm Optimization (PSO) 10,11 are also used for scheduling. In the case of scheduling, the quality of the optimization problem solution has to be improved and the execution time has to be reduced.…”

Section: Introductionmentioning

confidence: 99%

Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers

Mahil¹,

Jayasree²

2021

Concurrency and Computation

View full text Add to dashboard Cite

One of the foremost issues deliberated by the cloud infrastructure providers is to minimize the costs by reducing energy consumption. Though many heuristic algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and so on, have been existed for task scheduling or server consolidation to reduce energy consumption, more energy savings in data centers is required. In this paper, a combination of two distinctive heuristic algorithms, namely Particle Swarm Optimization (PSO) technique for proficient task scheduling and Ant Colony System (ACS) for effective server consolidation are proposed. In PSO, each particle in the swarm finds a task scheduling solution and updates the best solution. It is faster than many meta‐heuristic algorithms. In ACS, each ant in the ant colony tries to find the best destination PMs for migrating VMs from overloaded or underloaded PMs. The proposed PSO‐ACS algorithm is experimented with PlanetLab real workload traces available in the CloudSim toolkit. The algorithm provides reduction in energy consumption, number of VM migrations, and Energy SLA Violation (ESV) metrics while maintaining QoS. In the best‐case scenario, the proposed method reduces the ESV metric by 28%, 27%, and 18% in comparison with other algorithms like LR, ACS, and PSO‐LR.

show abstract

Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing

Cited by 108 publications

References 41 publications

An autonomous IoT service placement methodology in fog computing

An autonomous IoT service placement methodology in fog computing

A Novel Coevolutionary Approach to Reliability Guaranteed Multi-Workflow Scheduling upon Edge Computing Infrastructures

Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers

Contact Info

Product

Resources

About