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In recent years, cloud computing has emerged as a promising paradigm providing various resources in the Cloud Data Center (CDC), including computations, storage, and even platform as a service. The virtualization technology plays a pivotal role in CDC resources management and provides an enchanting feature of virtual machine (VM) migration which provides several benefits in terms of VM scheduling, fault tolerance, load balancing, energy efficiency, power management, and security. To achieve efficient VM migration, a plethora of VM migration schemes have been proposed in the literature aiming to serve the quality of service-driven user requirements. This paper surveys the most recent and state-of-the-art Artificial Intelligence (AI) based and conventional load balancing, energy-aware, SLA aware, and network-aware live VM migration schemes. Through an extensive literature review, this paper investigates the most critical aspects of conventional and AI-driven live VM migration schemes. Finally, a few open research challenges that require further consideration from the research community are highlighted. [Display omitted] • Overview of cloud computing, data centers, virtualization, and VM migration. • Conventional and AI-based Live VM migration schemes categorization. • Live VM migration survey of load, energy, SLA, and network schemes. • VM migration analysis based on migration cost and AI benefits and drawbacks. • Live VM migration research challenges and future directions. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as the most revolutionary technology in the field of computing. The cloud service providers (CSPs) have high computational facilities called data centers (DCs) at their disposal. CSPs provide services to the users through virtual machines (VMs). VM placement is the mapping of VMs onto physical machine called hosts. In this paper, we propose a two-tier virtual machine placement algorithm. Firstly, we propose a queueing structure to manage and schedule a large set of VMs. Secondly, a multi-objective VM placement algorithm called crow search based VM placement (CSAVMP) is proposed to reduce the resources wastage and power consumption at the data centers. VM migration is an indispensable part of any cloud platform for activities like maintenance, load balancing, fault tolerance etc. Three different migration strategies namely serial, parallel, improved serial have been tested and a comparative result has been produced. [ABSTRACT FROM AUTHOR]

Advancements in cloud computing has transformed it into the most promising computing paradigm for business organizations. In a dynamic cloud environment, Virtual Machine (VM) migration is a critical step in resource management, especially in large scale datacenters. Most of the VM migration and load balancing policies are based on power consumption and response time, with little attention on the interferences caused due to VM migrations. Such interferences degrade the overall performance of the system, and consequently violates the service level agreement (SLA) between the customer and the provider. This paper introduces an interference aware prediction mechanism for VM migration, with auto scaling. The automatic scaling policies help to handle sudden load changes with precise prediction and minimum VM migration. The experimental results and comparative analysis show that the proposed model is capable of predicting the interference more accurately. It also provides an optimum threshold range for VM migration. [ABSTRACT FROM AUTHOR]

The increasing use of workflows in various fields of science has led to the development of the Workflow as a Service (WaaS) platform, which provides workflow execution as a service to the users. The WaaS platform must use appropriate resource provisioning, sharing, and scheduling, to properly allocate the workflow tasks to the Cloud resources and fulfill the scheduling objectives. In this paper, we have proposed a novel workflow scheduling approach for the WaaS platform that reduces the average makespan of the workflows, improves energy efficiency, and reduces the resource renting cost of the Cloud resources. Our scheduling model consists of containers, virtual machines (VMs), and hosts. We have also proposed a suitable scaling policy for the Cloud resources to improve resource utilization. Extensive simulations with real-world workflows and comparison with some state-of-the-art algorithms show the efficacy of our approach in improving performance and energy efficiency while reducing the monetary cost. [Display omitted] • Multi-criteria scheduling approach for the Workflow as a Service platform. • Optimal MIPS for tasks decides sharing of virtual machine resources with containers. • Resource scaling policy to improve the schedulability of the workflows. • Results show improvement in performance and energy-efficiency while reducing cost. [ABSTRACT FROM AUTHOR]

Operation on demand flexibility in cloud computing services has resulted in great popularity and wide adoption. These services integrate thousands of computers, storage and communication networks, which implies a high consumption of electrical energy. Therefore, renewable energy-based cloud data centers are replacing traditional energy power grids. In this regard, the workload could be transferred to different nodes among different cloud data centers geographically distributed regarding renewable energy availability. In this sense, this paper presents a framework based on Cloudsim for virtual machine migrations among cloud data centers regarding sustainability optimization. Moreover, an approach for migrations among datacenters based on an expert system has been tested in several scenarios with renewable energy dynamically available. Experimental results show the improvements of the proposed framework and how expert systems can take advantage of renewable energy availability in terms of sustainability while preserving the QoS in terms of execution time. [Display omitted] [ABSTRACT FROM AUTHOR]

Proliferation of IT services provided by cloud service delivery model as well as diverse range of cloud users have led to the establishment of huge energy hungry data centers all around the world. Therefore, cloud providers are confronted with great pressures to reduce their energy consumption as well as their CO 2 emissions. In this direction, consolidation is proposed as an effective method of energy saving in cloud data centers. This paper proposes a new holistic cloud resource management procedure as well as novel heuristics based on multi-criteria decision making method for both determination of underloaded hosts and placement of the migrating VMs. The results of simulations using Cloudsim simulator validates the applicability of the proposed policies which shows up to 46%, 99%, and 95% reductions in energy consumption, SLA violation, and number of VM migrations, respectively in comparison with state of the arts. [ABSTRACT FROM AUTHOR]