https://dc.tsinghuajournals.com/cgi/viewcontent.cgi?article=1562&context=tsinghua-science-and-technology

Distributed Scheduling Problems in Intelligent Manufacturing Systems

A survey study on virtual machine migration and server consolidation techniques in DVFS-enabled cloud datacenter: Taxonomy and challenges

A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems

Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters

Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.

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Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

Cloud providers deliver heterogeneous virtual machines to run complicated jobs submitted by users. The task scheduling issue is formulated to a discrete optimization problem which is well-known NP-Hard. This paper presents a hybrid meta-heuristic algorithm based on genetic and thermodynamic simulated annealing algorithms to solve this problem. In the proposed algorithm, the genetic and simulated annealing algorithms have respective global and local search inclinations covering each other's shortcomings. A novel theorem is presented and applied to produce a semi-conducted initial population. In a used genetic algorithm with a global trend, the crossover operator is performed to explore search space. The thermodynamic simulated annealing algorithm is utilized to improve the efficiency, which considers entropy and energy difference concepts in the cooling schedule process. After obtaining a suitable solution, one of the three novel neighbor operators is randomly called to enhance the given solution potentially. In this way, the efficient balance between exploration and exploitation in the search space is achieved. Simulation results prove that the proposed hybrid algorithm has 10.17%, 9.31%, 7.76%, and 8.21% dominance in terms of makespan, schedule length ratio, speedup, and efficiency against other comparative algorithms.

A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments

Edge computing extends cloud computing capabilities to the edge of the network, allowing for instance Internet-of-Things (IoT) applications to process computation more locally and thus more efficiently. We aim to minimize latency and delay in edge architectures. We focus on an advanced architectural setting that takes communication and processing delays into account in addition to an actual request execution time in a performance engineering scenario. Our architecture is based on multi-cluster edge layer with local independent edge node clusters. We argue that particle swarm optimisation as a bio-inspired optimisation approach is an ideal candidate for distributed load processing in semi-autonomous edge clusters for IoT management. By designing a controller and using a particle swarm optimization algorithm, we can demonstrate that processing and propagation delay and the end-to-end latency (i.e., total response time) can be optimized.

Particle Swarm Optimization for Performance Management in Multi-cluster IoT Edge Architectures.

By advent of multi cloud computing, abundant web services are published by several providers to their world-wide users. Web service composition technology attracted a lot of attention for the sake of reduction in software development cost. In multi cloud environment (MCE), each atomic web service published by any cloud provider with same functionality has different price and quality of service (QoS). Each cybersecurity attack on security tenets can make business financial loss or even failure. Literature review in this ambit indicates that the current techniques seldom solve a mission-critical business process since majority of them pay attention only on QoS and network parameters and do not take security tenets into consideration. A bi-objective genetic optimization algorithm is presented in which it solves web service composition problem in MCE by cost and risk viewpoints. The result of implementations show that the proposed bi-objective genetic algorithm is sustainable against a single objective algorithm which minimizes only service costs and disregards security risks.

Web Service Composition in multi-cloud environment: A bi-objective genetic optimization algorithm

Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reach the goal, a system framework is presented which has different modules. The main concentration of the paper is on virtual machine (VM) consolidation module which launches users requested VMs on the minimum number of active servers to reduce datacenter total power consumption (TPC). In this paper, the VMs consolidation is abstracted to two-dimensional bin-packing problem and also is formulated to an integer linear programming. Since the papers in the literature scarcely are aware of skewness in resources of requested VMs and for discrete nature of search space, this paper presents the resource skewness-aware VMs consolidation algorithm based on improved thermodynamic simulated annealing approach because resource skewness potentially compels the algorithm to activate additional servers. The proposed SA-based algorithm is validated in extensive scenarios with different resource skewness in comparison with two heuristics and two meta-heuristics. The average results reported from different scenarios proves superiority of proposed algorithm in comparison with other approaches in terms of the number of used servers, TPC, and total resource wastage of datacenter.

An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters

Mirsaeid Hosseini Shirvani

Papers

Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

A hybrid meta-heuristic task scheduling algorithm based on genetic and thermodynamic simulated annealing algorithms in cloud computing environments

Particle Swarm Optimization for Performance Management in Multi-cluster IoT Edge Architectures.

Web Service Composition in multi-cloud environment: A bi-objective genetic optimization algorithm

An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters