Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Singh, Harvinder; Tyagi, Sanjay; Kumar, Pardeep; Gill, Sukhpal Singh; Buyya, Rajkumar

doi:10.1016/j.simpat.2021.102353

Cited by 67 publications

(27 citation statements)

References 110 publications

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“…Cloud computing is the centralized computation of the computing services within a single environment, allocating the necessary portion of that environment as per service demand in one of three types of service: Software As a Service (SAAS), Platform As a Service (PAAS), or Infrastructure As a Service (IAAS) [28]. e services provided by the cloud are purely dependent upon what services have been demanded by the users.…”

Section: Cloud Computingmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.

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Section: Cloud Computingmentioning

confidence: 99%

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Maray

Shuja

2022

Mobile Information Systems

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“…We need a suitable task scheduling algorithm to find a trade-off between user goals (such as reducing makespan) and service provider goals (such as reducing energy consumption and cost). To solve such a problem, a large number of researchers focused their research work on heuristic, meta-heuristic, and hybrid scheduling algorithms [20,21]. Currently, swarm intelligence algorithms are widely used to solve these types of problems.…”

Section: Task Schedulingmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based T ask Scheduling (SOAT S) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost -based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy -GA (FUGE). In the heavily loaded environment, the SOAT S algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

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“…• Resource Monitoring: When many users operate a fog node in fog frameworks, monitoring is useful. Detailed research can be done in the direction of fog-resource monitoring that assist multi operator access [189]. • Secure Fog Offloading: Offloading chores contain some security as well as privacy concerns.…”

Section: B Platform and Performance Metricsmentioning

confidence: 99%

Fog computing: A taxonomy, systematic review, current trends and research challenges

Singh

Gill

2021

Journal of Parallel and Distributed Computing

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104

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Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Cited by 67 publications

References 110 publications

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

Fog computing: A taxonomy, systematic review, current trends and research challenges

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