Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

A Multimedia Cloud Computing Model for Combinatorial Virtual Machine Placement

Cloud computing, which allows users to access subscription-based services on a pay-as-you-go basis, has recently transformed IT departments. Today, a variety of media services are offered through the Internet owing to the development of multimedia cloud computing, which is based on cloud computing. However, as multimedia cloud computing spreads, it has a negative influence on greenhouse gas emissions due to its high energy consumption and raises expenses for cloud users. Therefore, while still providing consumers with the resources they require and maintaining a high level of service, multimedia cloud service providers should make every effort to consume as little energy as possible. This proposal proposes residual usage-aware (RUA) and performance-aware (PA) methods for virtual machine placement. To save energy, find a suitable host to switch off. These two techniques were merged and applied to cloud data centers in order to complete the VM consolidation process. The outcomes of the simulation demonstrate a trade-off between energy consumption and SLA violations. Additionally, during VM deployment, it can manage shifting workloads to prevent host overload, dramatically lowering SLA breaches

Cloud-based Recommendation Systems: Applications and Solutions

Recommender systems have become extremely common in recent years, and are applied in a variety of applications. They help businesses increase their sales and customer satisfaction. More and more computing applications including recommender systems, are being deployed as cloud computing services. This papers presents some of the most common recommendation applications and solutions which follow SaaS, PaaS or other cloud computing service models. They are provided both from academia and business domain and use recent data mining, machine learning and artificial intelligence techniques. The tendency of these kind of applications is towards SaaS service model which seems the most appropriate especially for businesses

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how Azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies

Next Generation Cloud Computing: New Trends and Research Directions

The landscape of cloud computing has significantly changed over the last decade. Not only have more providers and service offerings crowded the space, but also cloud infrastructure that was traditionally limited to single provider data centers is now evolving. In this paper, we firstly discuss the changing cloud infrastructure and consider the use of infrastructure from multiple providers and the benefit of decentralising computing away from data centers. These trends have resulted in the need for a variety of new computing architectures that will be offered by future cloud infrastructure. These architectures are anticipated to impact areas, such as connecting people and devices, data-intensive computing, the service space and self-learning systems. Finally, we lay out a roadmap of challenges that will need to be addressed for realising the potential of next generation cloud systems.Comment: Accepted to Future Generation Computer Systems, 07 September 201

Effective Management of Hybrid Workloads in Public and Private Cloud Platforms.

As organizations increasingly adopt hybrid cloud architectures to meet their diverse computing needs, managing workloads across on-premises and on multiple cloud environments has become a critical challenge. This thesis explores the concept of hybrid workload management through the implementation of Azure Arc, a cutting-edge solution offered by Microsoft Azure. The primary objective of this study is to investigate how azure Arc enables efficient resource utilization and scalability for hybrid workloads. The research methodology involves a comprehensive analysis of the key features and functionalities of Azure Arc, coupled with practical experimentation in a simulated hybrid environment. The thesis begins by examining the fundamental principles of hybrid cloud computing and the associated workload management challenges. It then introduces Azure Arc as a novel approach that extends Azure control to on-premises and multi-cloud systems. The architecture, components, and integration mechanisms of Azure Arc are presented in detail, highlighting its ability to centralize management, enforce governance policies, and streamline operational tasks. This thesis contributes to the understanding of hybrid workload management by exploring the capabilities of Azure Arc. It provides valuable insights into the benefits of adopting this technology for organizations seeking to optimize resource utilization, streamline operations, and scale their workloads efficiently across on-premises and multi-cloud environments. The research findings serve as a foundation for further advancements in hybrid cloud computing and workload management strategies