Virtualized Cloud Data Centers Research Articles

Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm

This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality.

Classification-Based and Energy-Efficient Dynamic Task Scheduling Scheme for Virtualized Cloud Data Center

The size and number of cloud data centers (CDCs) have grown rapidly with the increasing popularity of cloud computing and high-performance computing. This has the unintended consequences of creating new challenges due to inefficient use of resources and high energy consumption. Hence, this necessitates the need to maximize resource utilization and ensure energy efficiency in CDCs. One viable approach to achieve energy efficiency and resource utilization in CDC is task scheduling. While several task scheduling approaches have been proposed in the literature, there appears to be a lack of classification-based merging concept for real-time tasks in these existing approaches. Thus, an energy-efficient dynamic scheduling scheme (EDS) of real-time tasks for virtualized CDC is presented in this paper. In the scheduling scheme, the heterogeneous tasks and virtual machines are first classified based on a historical scheduling record. Then, similar type of tasks are merged and scheduled to maximally utilize an operational state of the host. In addition, energy efficiencies and optimal operating frequencies of heterogeneous physical hosts are employed to attain energy preservation while creating and deleting the virtual machines. Experimental results show that, in comparison with existing techniques, EDS significantly improves overall scheduling performance, achieves a higher CDC resource utilization, increases task guarantee ratio, minimizes the mean response time, and reduces energy consumption.

Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

Speeding Up VM Startup by Cooperative VM Image Caching

Virtual machine (VM) management is at the core of virtualized cloud data centers. Among others, how to reduce the startup delay of VMs is a key issue for improving user experience and resource utility. In this paper, we study this issue by jointly considering VM placement and VM image caching. We formulate the joint placement problem and design several joint algorithms, including both online and offline algorithms, to speed up VM startup. In our design, we adopt the cooperative caching approach, where image cache copies are shared among physical machines (PMs) so as to reduce image retrieval time. The key point of our algorithms lies in how to appropriately place VM image cache among PMs so as to speed up VM startup as much as possible. The proposed algorithms are evaluated by extensive simulations via SimGrid. The results show that our algorithms can achieve shorter startup delay in most cases, compared with existing ones.

QoS-aware simulation job scheduling algorithm in virtualized cloud environment

Cloud computing is attracting an increasing number of simulation applications running in the virtualized cloud data center. These applications are submitted to the cloud in the form of simulation jobs. Meanwhile, the management and scheduling of simulation jobs are playing an essential role to offer efficient and high productivity computational service. In this paper, we design a management and scheduling service framework for simulation jobs in two-tier virtualization-based private cloud data center, named simulation execution as a service (SimEaaS). It aims at releasing users from complex simulation running settings, while guaranteeing the QoS requirements adaptively. Furthermore, a novel job scheduling algorithm named adaptive deadline-aware job size adjustment (ADaSA) algorithm is designed to realize high job responsiveness under QoS requirement for SimEaaS. ADaSA tries to make full use of the idle fragmentation resources by tuning the number of requested processes of submitted jobs in the queue adaptively, while guaranteeing that jobs’ deadline requirements are not violated. Extensive experiments with trace-driven simulation are conducted to evaluate the performance of our ADaSA. The results show that ADaSA outperforms both cloud-based job scheduling algorithm KCEASY and traditional EASY in terms of response time (up to 90%) and bounded slow down (up to 95%), while obtains approximately equivalent deadline-missed rate. ADaSA also outperforms two representative moldable scheduling algorithms in terms of deadline-missed rate (up to 60%).

An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.

Energy and quality of service-aware virtual machine consolidation in a cloud data center

The large-scale virtualized Cloud data centers consume huge amount of electrical energy leading to high operational costs and emission of greenhouse gases. Virtual machine (VM) consolidation has been found to be a promising approach to improve resource utilization and reduce energy consumption of the data center. However, aggressive consolidation of VMs tends to increase the number of VM migrations and leads to over-utilization of hosts. This in turn affects the quality of service (QoS) of the applications running in the VMs. Thus, reduction in energy consumption and at the same time ensuring proper QoS to the Cloud users are one of the major challenges among the researchers. In this paper, we have proposed an energy efficient and QoS-aware VM consolidation technique in order to address this problem. We have used Markov chain-based prediction approach to identify the over-utilized and under-utilized hosts in the data center. We have also proposed an efficient VM selection and placement policy based on linear weighted sum approach to migrate the VMs from over-utilized and under-utilized hosts considering both energy and QoS. Extensive simulations using real-world traces and comparison with state-of-art strategies show that our VM consolidation approach substantially reduces energy consumption within a data center while delivering suitable QoS.

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

SummaryCloud computing introduced a new paradigm in IT industry by providing on‐demand, elastic, ubiquitous computing resources for users. In a virtualized cloud data center, there are a large number of physical machines (PMs) hosting different types of virtual machines (VMs). Unfortunately, the cloud data centers do not fully utilize their computing resources and cause a considerable amount of energy waste that has a great operational cost and dramatic impact on the environment. Server consolidation is one of the techniques that provide efficient use of physical resources by reducing the number of active servers. Since VM placement plays an important role in server consolidation, one of the main challenges in cloud data centers is an efficient mapping of VMs to PMs. Multiobjective VM placement is generating considerable interest among researchers and academia. This paper aims to represent a detailed review of the recent state‐of‐the‐art multiobjective VM placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments. Also, it gives special attention to the parameters and approaches used for placing VMs into PMs. In the end, we will discuss and explore further works that can be done in this area of research.

Straggler Root-Cause and Impact Analysis for Massive-scale Virtualized Cloud Datacenters

Increased complexity and scale of virtualized distributed systems has resulted in the manifestation of emergent phenomena substantially affecting overall system performance. This phenomena is known as “Long Tail”, whereby a small proportion of task stragglers significantly impede job completion time. While work focuses on straggler detection and mitigation, there is limited work that empirically studies straggler root-cause and quantifies its impact upon system operation. Such analysis is critical to ascertain in-depth knowledge of straggler occurrence for focusing developmental and research efforts towards solving the Long Tail challenge. This paper provides an empirical analysis of straggler root-cause within virtualized Cloud datacenters; we analyze two large-scale production systems to quantify the frequency and impact stragglers impose, and propose a method for conducting root-cause analysis. Results demonstrate approximately 5 percent of task stragglers impact 50 percent of total jobs for batch processes, and 53 percent of stragglers occur due to high server resource utilization. We leverage these findings to propose a method for extreme straggler detection through a combination of offline execution patterns modeling and online analytic agents to monitor tasks at runtime. Experiments show the approach is capable of detecting stragglers less than 11 percent into their execution lifecycle with 95 percent accuracy for short duration jobs.

Online assignment of non-SDN virtual network nodes to a physical SDN

Network virtualization is a promising solution to increase utilization of communication resources in data center networks. On the other side, software defined networks (SDNs) gather great attention from industry and academia to be used as the networking solution for data centers. Virtualizing SDNs and providing suitable virtual network embedding (VNE) techniques, paves the path for designing highly configurable virtualized cloud data centers. Considering the challenges which are arisen by using the SDNs as the substrate network for hosting non-SDN virtual networks, it is desired to develop an online VNE technique with a bounded competitive ratio as well as polynomial execution time in terms of problem size. To achieve this goal, this work is a primary step which solves the embedding problem with releasing the capacity constraints of physical network links. So, to the best of our knowledge, the problem of online virtual node assignment to physical nodes of SDNs with limited capacity of in-node and on-controller processing powers is presented for the first time in this work. Furthermore an online mapping algorithm is provided for the mentioned problem with poly-logarithmic competitive ratio. This can be considered as an online algorithm for general node assignment problem with batch arrivals of the requests and also can be used as a solution for online virtual machine allocation problem with batch arrivals of the requests.

Application-Aware Dynamic Fine-Grained Resource Provisioning in a Virtualized Cloud Data Center

A key factor of win–win cloud economy is how to trade off between the application performance from customers and the profit of cloud providers. Current researches on cloud resource allocation do not sufficiently address the issues of minimizing energy cost and maximizing revenue for various applications running in virtualized cloud data centers (VCDCs). This paper presents a new approach to optimize the profit of VCDC based on the service-level agreements (SLAs) between service providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines at different service classes. An analytic probabilistic model is then developed for non-steady VCDC states. In addition, a smart controller is developed for fine-grained resource provisioning and sharing among multiple applications. Furthermore, a novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. The advantage of the proposed approach is validated with trace-driven simulations.

Dynamic Inbound Rate Adjustment Scheme for Virtualized Cloud Data Centers

Dynamic Inbound Rate Adjustment Scheme for Virtualized Cloud Data Centers

Green cloud computing: reduced overload with a better architecture

Software systems architects had been continually facing challenges of scaling up software systems architectures. Scaling up the architectures to meet these needs certainly introduces additional energy cost. Improvement of applications, algorithms, energy efficient hardware has been done to achieve energy efficiency. Reducing the energy demands in such architectures is always challenging. The greatest environmental challenge today is e-wastes and energy crisis, bringing green computing in the limelight. Green computing requires algorithms and mechanisms to be redesigned for energy efficiency. In accordance to the state of art for distributed software architectures, are not aware on green cloud computing while the implementation needs, changing the whole infrastructure cost effectively. Software architectures don’t provide the primitives for reasoning and managing power consumption. In present work, an energy efficient resource management system for virtualized Cloud data centers that reduces operational costs and provides required Quality of Service (QoS) has been proposed and fulfilled. Our proposal towards software engineering demands to be green aware, where the software engineering and design activities should not only be judged by their technical merits, but also by their contributions to energy savings. In particular, the software system architecture seems to be adequate to address green-aware concerns but need revival. Software architectures should be green-aware, providing power management mechanisms as part of the architecture. The results of present work improvised that the proposed technique brings substantial energy savings, while ensuring reliable QoS. This justifies further investigation and development of the proposed resource management system.

Energy-aware resource management mechanism for virtualized cloud datacenter

Concerning the management of the resources in cloud,an energy model and four Virtual Machine Placement(VMP) algorithms were proposed. Firstly,the algorithms calculated the load of the servers in each rack and classified them according to the thresholds. Then the proposed methods selected appropriate virtual machines from servers to migrate using the minimization of migrations policy and accepted the new coming virtual machine allocation requests. At last,the algorithms matched each virtual machine with the servers and found the best server for placement. The experimental results show that,compared with the existing energy-aware resource management algorithm,the proposed technique improves energy efficiency of servers,network equipment,and cooling systems by 2. 4%,18. 5%,and 28. 1% respectively,resulting in a total of 14. 5%improvement on average in the entire datacenter.

Reliability‐aware server consolidation for balancing energy‐lifetime tradeoff in virtualized cloud datacenters

SUMMARYServer consolidation using virtualization technologies allow large‐scale datacenters to improve resource utilization and energy efficiency. However, most existing consolidation strategies solely focused on balancing the tradeoff between performance service‐level‐agreements (SLAs) desired by cloud applications and energy costs consumed by hosting servers. With the presence of fluctuating workloads in datacenters, the lifetime and reliability of servers under dynamic power‐aware consolidation could be adversely impacted by repeated on–off thermal cycles, ware‐and‐tear and temperature rise. In this paper, we propose a Reliability‐Aware server Consolidation stratEgy, named RACE, to address when and how to perform energy‐efficient server consolidation in a reliability‐friendly and profitable way. The focus is on the characterization and analysis of this problem as a multi‐objective optimization, by developing a utility model that unifies multiple constraints on performance SLAs, reliability factors and energy costs in a holistic manner. An improved grouping genetic algorithm is proposed to search the global optimal solution, which takes advantage of a collection of reliability‐aware resource buffering and virtual machines‐to‐servers re‐mapping heuristics for generating good initial solutions and improving the convergence rate. Extensive simulations are conducted to validate the effectiveness, scalability and overhead of RACE—in improving the overall utility of datacenters while avoiding unprofitable consolidation in the long term—compared with pMapper and PADD strategies for server consolidation. Copyright © 2013 John Wiley & Sons, Ltd.

Improving the Latency Value by Virtualizing Distributed Data Center and Automation in Cloud.

Organization today are leveraging the benefits of cloud computing to increase flexibility, agility and reduce cost however that flexibility can also pose networking challenges by moving application offsite, companies need good network connectivity between a data center site and a cloud provider so that user don't experience performance degradation. Good connectivity comes in two forms necessary bandwidth and low latency. Distributed datacenter improves services access latency and bandwidth. Virtualized cloud data center enables IT organization to share compute resources across multiple applications and user group in a much more dynamic way than is possible in traditional environment where application, middleware and infrastructure are tightly coupled and resource allocation are highly static. The goal is to enable users to reduce the cost and complexity of application provisioning and operations in virtualized data centers. Cloud environments at the same by automation liberate the operational management from the burden of manual process.