In recent years, the datacenters have observed unprecedented growth both in terms of size as well as magnitude. Virtualization solutions are widely implemented to solve various problems of modern day datacenters which include: hardware underutilization, data center space usage and high system administration and maintenance cost. The majorchallenges faced by these large server farms are the lack of high system reliability and the high operational cost due to large electricity consumption. Thus, energy-aware VM deployments and scheduling is an urgent necessity to achieve both of these goals. Job scheduling has been studied by researches for several years now, but the development of virtualized clusters and cloud environments has opened door for new approaches to job scheduling.^The major components of job scheduling in virtualized environment consist of: VM placement among the available physical resources and the dynamic workload balancing with the help of job migrations across datacenter cluster nodes. There are several ongoing research efforts towards the development of an integrated cloud management system to provide comprehensive online monitoring of resources utilization along with the implementation of power-aware policies to reduce the total energy consumption. However, most of these techniques provide online power monitoring based on the power consumption of a physical node running one or moreVirtual Machines (VM). They lack a fine-grained mechanism to profile the power of an individual hosted VM. In this work we propose a novel power modelling technique, VMeter, based on online monitoring of system-resources having high correlation with the total power consumption. The monitored system sub-components include: CPU, cache, disk, and DRAM.^The proposed model predicts instantaneous power consumption of an individual VM hosted on a physical node besides the full system power consumption. Our model is validated using computationally diverse and industry standard benchmark programs. Our evaluation results show that our model is able to predict instantaneous power with an average mean and median accuracy of 93% and 94%, respectively, against the actual measured power using an externally attached power meter. With a fine grained monitoring infrastructure in place, we then propose two energy-aware VM placement policies namely: Performance Aware and Load-Factor Aware deployments. Both these policies analyze the power state information of each physical node within the cloud todetermine the optimal set of physical nodes to be used to deploy new VMs. The simulation results show that both of our proposed algorithms lead to substantial reduction in the total energy consumption compared to the first-fit VM placement strategy.^The load-factor aware algorithm performs better than the performance-aware algorithm for most of the test scenarios. Moreover, our simulation results provide a comprehensiveanalysis of the datacenter total energy consumption by varying job parameters including: job length (large, medium and small jobs), job nodes requirement, and job priority etc.

This book discusses harnessing the real power of cloud computing in optimization problems, presenting state-of-the-art computing paradigms, advances in applications, and challenges concerning both the theories and applications of cloud computing in optimization with a focus on diverse fields like the Internet of Things, fog-assisted cloud computing, and big data. In real life, many problems – ranging from social science to engineering sciences – can be identified as complex optimization problems. Very often these are intractable, and as a result researchers from industry as well as the academic community are concentrating their efforts on developing methods of addressing them. Further, the cloud computing paradigm plays a vital role in many areas of interest, like resource allocation, scheduling, energy management, virtualization, and security, and these areas are intertwined with many optimization problems. Using illustrations and figures, this book offers students and researchers a clear overview of the concepts and practices of cloud computing and its use in numerous complex optimization problems.

Networking and machine virtualization play critical roles in the success of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past, including the impact from network traffic. When it comes to virtual machines (VMs) in cloud data centers, it remains unexplored how the highly dynamic traffic affects the energy consumption in virtualized environments. In this thesis, we first present an empirical study on the interplay between energy consumption and network transactions in virtualized environments. Through the real-world measurement on both Xen- and KVM-based platforms, we show that these state-of-the-art designs bring significant overhead on virtualizing network devices and noticeably increase the demand of CPU resources when handling network traffic. Furthermore, the energy consumption varies significantly with traffic allocation strategies and virtual CPU affinity conditions, which was not seen in conventional physical machines. Next, we study the performance and energy efficiency issues when CPU intensive tasks and I/O intensive tasks are co-located inside a VM. A combined effect from device virtualization overhead and VM scheduling latency can cause severe interference in the presence of such hybrid workloads. To this end, we propose Hylics, a novel solution that enables an efficient data traverse path for both I/O and computation operations, and decouples the costly interference. Several important design issues are pinpointed and addressed during our implementation, including efficient intermediate data sharing, network service offloading, and QoS-aware memory usage management. Based on our real-world deployment in KVM, Hylics can improve computation and networking performance with a moderate amount of memory usage. Moreover, this design also sheds new light on optimizing the energy efficiency for virtualized systems.

This book focuses on the current trends in research and analysis of virtual machine placement in a cloud data center. It discusses the integration of machine learning models and metaheuristic approaches for placement techniques. Taking into consideration the challenges of energy-efficient resource management in cloud data centers, it emphasizes upon computing resources being suitably utilised to serve application workloads in order to reduce energy utilisation, while maintaining apt performance. This book provides information on fault-tolerant mechanisms in the cloud and provides an outlook on task scheduling techniques. Focuses on virtual machine placement and migration techniques for cloud data centers Presents the role of machine learning and metaheuristic approaches for optimisation in cloud computing services Includes application of placement techniques for quality of service, performance, and reliability improvement Explores data center resource management, load balancing and orchestration using machine learning techniques Analyses dynamic and scalable resource scheduling with a focus on resource management The text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology.

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Seminar paper from the year 2013 in the subject Computer Science - Commercial Information Technology, grade: 1.0, Otto-von-Guericke-University Magdeburg (Faculty of Computer Science), course: Recent Topics in Business Informatics, language: English, abstract: Energy efficiency has a rising importance throughout society. With the growth of large data centers, the energy consumption becomes centralized and nowadays takes a significant amount of the overall electricity consumption of a country. Load balancing algorithms are able to make an existing infrastructure more efficient without major drawbacks. This structured literature research presents the state of the art technology regarding the load balancing approach to make data centers more en-ergy efficient. The state of the art approaches are reviewed for techniques, im-provements and consideration of performance effects.