Survey on variants of heuristic algorithms for scheduling workflow of tasks

Raj, R. Joshua Samuel; Prasad, Sandeep

doi:10.1109/iccpct.2016.7530288

Cited by 4 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of its techniques are Min-Min, Max-Min, improved Max-Min [34], and Min-Min based on priorities. These techniques generate better results than traditional approaches [35]; 2.…”

Section: Non-preemptive Schedulingmentioning

confidence: 99%

A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems

Fadlallah

Rebaïne

Mcheick

2021

IoT

View full text Add to dashboard Cite

Smart, pervasive devices have recently experienced accelerated technological development in the fields of hardware, software, and wireless connections. The promotion of various kinds of collaborative mobile computing requires an upgrade in network connectivity with wireless technologies, as well as enhanced peer-to-peer communication. Mobile computing also requires appropriate scheduling methods to speed up the implementation and processing of various computing applications by better managing network resources. Scheduling techniques are relevant to the modern architectural models that support the IoT paradigm, particularly smart collaborative mobile computing architectures at the network periphery. In this regard, load-balancing techniques have also become necessary to exploit all the available capabilities and thus the speed of implementation. However, since the problem of scheduling and load-balancing, which we addressed in this study, is known to be NP-hard, the heuristic approach is well justified. We thus designed and validated a greedy scheduling and load-balancing algorithm to improve the utilization of resources. We conducted a comparison study with the longest cloudlet fact processing (LCFP), shortest cloudlet fact processing (SCFP), and Min-Min heuristic algorithms. The choice of those three algorithms is based on the efficiency and simplicity of their mechanisms, as reported in the literature, for allocating tasks to devices. The simulation we conducted showed the superiority of our approach over those algorithms with respect to the overall completion time criterion.

show abstract

“…Some of its techniques are Min-Min, Max-Min, improved Max-Min [34], and Min-Min based on priorities. These techniques generate better results than traditional approaches [35]; 2.…”

Section: Non-preemptive Schedulingmentioning

confidence: 99%

A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems

Fadlallah

Rebaïne

Mcheick

2021

IoT

View full text Add to dashboard Cite

show abstract

“…[72]. These techniques give better results as compared to the traditional techniques, but do not guarantee to score high ranking in the cloud scheduling [73].…”

Section: Heuristic Techniquesmentioning

confidence: 99%

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Al-Arasi¹,

Saif²

2020

EAI Endorsed Transactions on Cloud Systems

View full text Add to dashboard Cite

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria.

show abstract

“…The most significant challenge in the CC domain is resource scheduling (RS). While carrying out RS, at least fulfilling levels of QoS must be preserved with the help of appropriate hardware algorithms and infrastructure [4]. The components of cloud framework, generally denoted as the broker in recent survey, is accountable to map requested end users task to the accessible virtualized hardware i.e., generally executed by means of virtual machine (VM).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Saraereh¹,

Ali²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Internet of Things (IoT) is transforming the technical setting of conventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to the IoT enabled models are resource-limited and necessitate crisp responses, low latencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentioned challenges. But the intrinsic high latency of CC makes it nonviable. The longer latency degrades the outcome of IoT based smart systems. CC is an emergent dispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes the energy utilization of the cloud infrastructure and rises the income of service providers by the minimization of the processing time of the user job. With this motivation, this paper presents an intelligent Chaotic Artificial Immune Optimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabled cloud environment. The proposed CAIOA-RS algorithm solves the issue of resource allocation in the IoT enabled cloud environment. It also satisfies the makespan by carrying out the optimum task scheduling process with the distinct strategies of incoming tasks. The design of CAIOA-RS technique incorporates the concept of chaotic maps into the conventional AIOA to enhance its performance. A series of experiments were carried out on the CloudSim platform. The simulation results demonstrate that the CAIOA-RS technique indicates that the proposed model outperforms the original version, as well as other heuristics and metaheuristics.

show abstract

Survey on variants of heuristic algorithms for scheduling workflow of tasks

Cited by 4 publications

References 12 publications

A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems

A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems

Task scheduling in cloud computing based on metaheuristic techniques: A review paper

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Contact Info

Product

Resources

About