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.

Combining high-speed network accesses and powerful computer virtualization, cloud computing provides an elastic and cost-effective service paradigm for the development of resource-hungry new applications as well as the expansion of a broad spectrum of existing applications. Virtualization has become wide spread in modern computers and operating systems; the extensive use of virtual machines (VMs) in a networked cloud environment, however, comes with unprecedented overhead. In this thesis we study the impact of virtualization overhead on real-world systems. First, we present a study on the performance of modern virtualization solutions under DoS attacks. We experiment with the full spectrum of modern virtualization techniques, from paravirtualization, hardware virtualization, to container virtualization, with a comprehensive set of benchmarks. Our results reveal severe vulnerability of modern virtualization. Further, this thesis presents an empirical study on the power consumption of typical virtualization packages while performing network tasks. We find that both Hardware Virtualization and Paravirtualization add considerable energy overhead, affecting both sending and receiving, and a busy virtualized web-server may consume 40% more energy than its non-virtualized counterparts. Next, we focused on cloud network performance instability in the public cloud. Through measurement of real world cloud platforms, we find that network performance degradation and variation phenomena can be prevalent and significant with both TCP and UDP traffic, even within the same data center, and even with a lightly utilized network. Our in-depth measurement and detailed system analysis reveal that the performance variation and degradation are mainly due to the requirements of the CPU in both computation and network communication. Finally, we study the use of virtual machine based clouds to provide infrastructure to support cloud gaming. For each issue we further suggest solutions to resolve performance issues associated with virtualized environments, both at the hypervisor level and, inside a VM, at the operating system level. Our implementation on both large public clouds and our cloud testbed have demonstrated their practicality as well as their effectiveness in improving and stabilizing the energy consumption and performance of virtual machine based clouds.

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 constitutes the thoroughly refereed post-conference proceedings of the 8th TPC Technology Conference, on Performance Evaluation and Benchmarking, TPCTC 2017, held in conjunction with the43rd International Conference on Very Large Databases (VLDB 2017) in August/September 2017. The 12 papers presented were carefully reviewed and selected from numeroussubmissions. The TPC remains committed to developing new benchmark standards to keep pace with these rapid changes in technology.

Designing Networks and Services for the Cloud Delivering business-grade cloud applications and services A rapid, easy-to-understand approach to delivering a secure, resilient, easy-to-manage, SLA-driven cloud experience Designing Networks and Services for the Cloud helps you understand the design and architecture of networks and network services that enable the delivery of business-grade cloud services. Drawing on more than 40 years of experience in network and cloud design, validation, and deployment, the authors demonstrate how networks spanning from the Enterprise branch/HQ and the service provider Next-Generation Networks (NGN) to the data center fabric play a key role in addressing the primary inhibitors to cloud adoption–security, performance, and management complexity. The authors first review how virtualized infrastructure lays the foundation for the delivery of cloud services before delving into a primer on clouds, including the management of cloud services. Next, they explore key factors that inhibit enterprises from moving their core workloads to the cloud, and how advanced networks and network services can help businesses migrate to the cloud with confidence. You’ll find an in-depth look at data center networks, including virtualization-aware networks, virtual network services, and service overlays. The elements of security in this virtual, fluid environment are discussed, along with techniques for optimizing and accelerating the service delivery. The book dives deeply into cloud-aware service provider NGNs and their role in flexibly connecting distributed cloud resources, ensuring the security of provider and tenant resources, and enabling the optimal placement of cloud services. The role of Enterprise networks as a critical control point for securely and cost-effectively connecting to high-performance cloud services is explored in detail before various parts of the network finally come together in the definition and delivery of end-to-end cloud SLAs. At the end of the journey, you preview the exciting future of clouds and network services, along with the major upcoming trends. If you are a technical professional or manager who must design, implement, or operate cloud or NGN solutions in enterprise or service-provider environments, this guide will be an indispensable resource. * Understand how virtualized data-center infrastructure lays the groundwork for cloud-based services * Move from distributed virtualization to “IT-as-a-service” via automated self-service portals * Classify cloud services and deployment models, and understand the actors in the cloud ecosystem * Review the elements, requirements, challenges, and opportunities associated with network services in the cloud * Optimize data centers via network segmentation, virtualization-aware networks, virtual network services, and service overlays * Systematically secure cloud services * Optimize service and application performance * Plan and implement NGN infrastructure to support and accelerate cloud services * Successfully connect enterprises to the cloud * Define and deliver on end-to-end cloud SLAs * Preview the future of cloud and network services

This book features high-quality research papers presented at Second Doctoral Symposium on Computational Intelligence (DoSCI-2021), organized by Institute of Engineering and Technology (IET), AKTU, Lucknow, India, on 6 March 2021. This book discusses the topics such as computational intelligence, artificial intelligence, deep learning, evolutionary algorithms, swarm intelligence, fuzzy sets and vague sets, rough set theoretic approaches, quantum-inspired computational intelligence, hybrid computational intelligence, machine learning, computer vision, soft computing, distributed computing, parallel and grid computing, cloud computing, high-performance computing, biomedical computing, decision support and decision making.

This edited book aims to present the state of the art in research and development of the convergence of high-performance computing and parallel programming for various engineering and scientific applications. The book has consolidated algorithms, techniques, and methodologies to bridge the gap between the theoretical foundations of academia and implementation for research, which might be used in business and other real-time applications in the future.The book outlines techniques and tools used for emergent areas and domains, which include acceleration of large-scale electronic structure simulations with heterogeneous parallel computing, characterizing power and energy efficiency of a data-centric high-performance computing runtime and applications, security applications of GPUs, parallel implementation of multiprocessors on MPI using FDTD, particle-based fused rendering, design and implementation of particle systems for mesh-free methods with high performance, and evolving topics on heterogeneous computing. In the coming days the need to converge HPC, IoT, cloud-based applications will be felt and this volume tries to bridge that gap.