Resources In Cloud Data Centers Research Articles

A Novel Cloud Data Center Resource Scheduling Optimization Model based on KPCA and Markov Chain

A Novel Cloud Data Center Resource Scheduling Optimization Model based on KPCA and Markov Chain

Optimized Energy Efficient Virtual Machine Placement Algorithm and Techniques for Cloud Data Centers

Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that's why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. Hence there is need for green computing and for energy efficient management of cloud data center resources besides meeting the QoS constraints. Within a data center, maximum energy is consumed by cooling systems and ICT Infrastructure, particularly, the servers. So it is of utmost importance to optimize energy utilization in datac enter servers. In order to achieve this, we can leverage the power of virtualization which is intrinsic in Cloud Computing. Virtualization opens the doors for VM consolidation by allowing dynamic migration of virtual machines across physical machines. In this study, the problem of VM consolidation has been formulated as a mathematical optimization problem.

P-Aware: a proportional multi-resource scheduling strategy in cloud data center

Concentrating on a single resource cannot efficiently cope with the overall high utilization of resources in cloud data centers. Nowadays multiple resource scheduling problem is more attractive to researchers. Some studies achieve progresses in multi-resource scenarios. However, these previous heuristics have obvious limitations in complex software defined cloud environment. Focusing on energy conservation and load balancing, we propose a preciousness model for multiple resource scheduling in this paper. We give the formulation of the problem and propose an innovative strategy (P-Aware). In P-Aware, a special algorithm PMDBP (Proportional Multi-dimensional Bin Packing) is applied in the multi-dimensional bin packing approach. In this algorithm, multiple resources are consumed in a proportional way. Structure and details of PMDBP are discussed in this paper. Extensive experiments demonstrate that our strategy outperforms others both in efficiency and load balancing. Now P-Aware has been implemented in the resource management system in our cooperative company to cut energy consumption and reduce resource contention.

Directory-based incentive management services for ad-hoc mobile clouds

Mobile cloud computing is envisioned as a promising approach to augment the computational capabilities of mobile devices for emerging resource-intensive mobile applications. This augmentation is generally achieved through the capabilities of stationary resources in cloud data centers. However, these resources are mostly not free and sometimes not available. Mobile devices are becoming powerful day by day and can form a self-organizing mobile ad-hoc network of nearby devices and offer their resources as on-demand services to available nodes in the network. In the ad-hoc mobile cloud, devices can move after consuming or providing services to one another. During this process, the problem of incentives arises for a node to provide service to another device (or other devices) in the network, which ultimately decreases the motivation of the mobile device to form an ad-hoc mobile cloud. To solve this problem, we propose a directory-based architecture that keeps track of the retribution and reward valuations (in terms of energy saved and consumed) for devices even after they move from one ad-hoc environment to another. From simulation results, we infer that this framework increases the motivation for mobile devices to form a self-organizing proximate mobile cloud network and to share their resources in the network.

A Classification-Based Virtual Machine Placement Algorithm in Mobile Cloud Computing

In recent years, cloud computing services based on smart phones and other mobile terminals have been a rapid development. Cloud computing has the advantages of mass storage capacity and high-speed computing power, and it can meet the needs of different types of users, and under the background, mobile cloud computing (MCC) is now booming. In this paper, we have put forward a new classification-based virtual machine placement (CBVMP) algorithm for MCC, and it aims at improving the efficiency of virtual machine (VM) allocation and the disequilibrium utilization of underlying physical resources in large cloud data center. By simulation experiments based on CloudSim cloud platform, the experimental results show that the new algorithm can improve the efficiency of the VM placement and the utilization rate of underlying physical resources.

Virtual Machine Customization and Task Mapping Architecture for Efficient Allocation of Cloud Data Center Resources

Energy usage of large-scale data centers has become a major concern for cloud providers. There has been an active effort in techniques for the minimization of the energy consumed in the data centers. However, most approaches lack the analysis and application of real cloud backend traces. In existing approaches, the variation of cloud workloads and its effect on the performance of the solutions are not investigated. Furthermore, the focus of existing approaches is on virtual machine migration and placement algorithms, with little regard to tailoring virtualmachine configuration to workload characteristics, which can further reduce the energy consumption and resource wastage in a typical data center. To address these weaknesses and challenges, we propose a new architecture for cloud resource allocation that maps groups of tasks to customized virtual machine types. This mapping is based on the task usage patterns obtained from the analysis of the historical data extracted from utilization traces. In our work, the energy consumption is decreased via efficient resource allocation based on the actual resource usage of tasks. Experimental results show that, when resources are allocated based on the discovered usage patterns, significant energy saving can be achieved.

Virtual machine migration in cloud data centers: a review, taxonomy, and open research issues

Virtualization efficiently manages the ever-increasing demand for storage, computing, and networking resources in large-scale Cloud Data Centers. Virtualization attains multifarious resource management objectives including proactive server maintenance, load balancing, pervasive service availability, power management, and fault tolerance by virtual machine (VM) migration. VM migration is a resource-intensive operation as it constantly requires adequate CPU cycles, memory capacity, system cache, and network bandwidth. Consequently, it adversely affects the performance of running applications and cannot be entirely overlooked in contemporary data centers, particularly when user SLA and critical business goals are to be met. The unavailability of a comprehensive survey on VM migration schemes that covers various VM migration aspects such as migration patterns, sequence, application performance, bandwidth optimization, and migration granularity has motivated this review of existing schemes. This paper reviews state-of-the-art live and non-live VM migration schemes. Through an extensive literature review, a detailed thematic taxonomy is proposed for the categorization of VM migration schemes. Critical aspects and related features of current VM migration schemes are inspected through detailed qualitative investigation. We extract significant parameters from existing literature to discuss the commonalities and variances among VM migration schemes. Finally, open research issues and challenges with VM migration that require further consideration to develop optimal VM migration schemes in Cloud Data Centers are briefly addressed.

An Efficient Virtual Machine Scheduling Technique in Cloud Computing Environment

Cloud is a collection of heterogeneous resources and requirements of these resources can change dynamically. Cloud providers are always interested in maximizing the resources utilization and the associated revenues, by trimming down energy consumption and operational expenses, while on the other hand cloud users are interested in minimizing response time and optimizing overall application throughput. In cloud environment to allocate the resources with minimum overhead time along with efficient utilization of available resources is very challenging task. The resources in cloud datacenter are allocated using a virtual machine (VM) scheduling technique. So there is a need of an efficient VM scheduling technique to maximize system performance and cost saving. In this paper two dynamic virtual machine scheduling techniques i.e. Best fit and Worst fit are proposed for reducing the response time along with efficient and balanced resource utilization. The proposed algorithms removes the limitations of the previously proposed Novel Vector based algorithm and minimizes the response time complexity in order of O(logn) and O(1) using Best Fit and Worst Fit strategies respectively. Index Terms—Cloud computing, load balancing, VM scheduling, response time, resource leak.

An Efficient Virtual Machine Scheduling Technique in Cloud Computing Environment

Cloud is a collection of heterogeneous resources and requirements of these resources can change dynamically. Cloud providers are always interested in maximizing the resources utilization and the associated revenues, by trimming down energy consumption and operational expenses, while on the other hand cloud users are interested in minimizing response time and optimizing overall application throughput. In cloud environment to allocate the resources with minimum overhead time along with efficient utilization of available resources is very challenging task. The resources in cloud datacenter are allocated using a virtual machine (VM) scheduling technique. So there is a need of an efficient VM scheduling technique to maximize system performance and cost saving. In this paper two dynamic virtual machine scheduling techniques i.e. Best fit and Worst fit are proposed for reducing the response time along with efficient and balanced resource utilization. The proposed algorithms removes the limitations of the previously proposed Novel Vector based algorithm and minimizes the response time complexity in order of O(logn) and O(1) using Best Fit and Worst Fit strategies respectively. Index Terms—Cloud computing, load balancing, VM scheduling, response time, resource leak.

A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

Healthcare scientific applications, such as body area network, require of deploying hundreds of interconnected sensors to monitor the health status of a host. One of the biggest challenges is the streaming data collected by all those sensors, which needs to be processed in real time. Follow-up data analysis would normally involve moving the collected big data to a cloud data center for status reporting and record tracking purpose. Therefore, an efficient cloud platform with very elastic scaling capacity is needed to support such kind of real time streaming data applications. The current cloud platform either lacks of such a module to process streaming data, or scales in regard to coarse-grained compute nodes.In this paper, we propose a task-level adaptive MapReduce framework. This framework extends the generic MapReduce architecture by designing each Map and Reduce task as a consistent running loop daemon. The beauty of this new framework is the scaling capability being designed at the Map and Task level, rather than being scaled from the compute-node level. This strategy is capable of not only scaling up and down in real time, but also leading to effective use of compute resources in cloud data center. As a first step towards implementing this framework in real cloud, we developed a simulator that captures workload strength, and provisions the amount of Map and Reduce tasks just in need and in real time.To further enhance the framework, we applied two streaming data workload prediction methods, smoothing and Kalman filter, to estimate the unknown workload characteristics. We see 63.1% performance improvement by using the Kalman filter method to predict the workload. We also use real streaming data workload trace to test the framework. Experimental results show that this framework schedules the Map and Reduce tasks very efficiently, as the streaming data changes its arrival rate.

A lightweight active service migration framework for computational offloading in mobile cloud computing

Cloud computing enables access to the widespread services and resources in cloud datacenters for mitigating resource limitations in low-potential client devices. Computational cloud is an attractive platform for computational offloading due to the attributes of scalability and availability of resources. Therefore, mobile cloud computing (MCC) leverages the application processing services of computational clouds for enabling computational-intensive and ubiquitous mobile applications on smart mobile devices (SMDs). Computational offloading frameworks focus on offloading intensive mobile applications at different granularity levels which involve resource-intensive mechanism of application profiling and partitioning at runtime. As a result, the energy consumption cost (ECC) and turnaround time of the application is increased. This paper proposes an active service migration (ASM) framework for computational offloading to cloud datacenters, which employs lightweight procedure for the deployment of runtime distributed platform. The proposed framework employs coarse granularity level and simple developmental and deployment procedures for computational offloading in MCC. ASM is evaluated by benchmarking prototype application on the Android devices in the real MCC environment. It is found that the turnaround time of the application reduces up to 45 % and ECC of the application reduces up to 33 % in ASM-based computational offloading as compared to traditional offloading techniques which shows the lightweight nature of the proposed framework for computational offloading.

A Systematic Approach to Adaptive Dimensioning of Data Centers

Cloud data centers (CDCs) provide key infrastructure for Cloud computing. Accurately allocating computing resources is very important for the CDCs to function efficiently. Current allocation of resources in CDCs is mostly dedicated and static. However, workloads for Cloud applications are highly variable which cause poor application performance, poor resource utilization or both. In this paper, considering both blocking and delay probability by applying queuing theories to model different scenarios, systematic adaptive dimensioning methods for CDCs are developed so that right amount of computing resources are allocated for variable workloads to meet quality of service requirements in different situations. Both single CDC and multiple CDCs with and without delay are considered. The proposed methods can be applied to dimension data centers efficiently and dynamically.

Energy Efficient Virtual Machine Allocation in Cloud

Cloud data centers hosting cloud applications consume huge amounts of electrical energy, contributing to high operational costs and carbon footprints to the environment. When virtualized resources of cloud data centers are allocated, the energy consumption is a problem to be considered necessarily. In this paper, three energy efficient virtual machine allocation algorithms are carried on experimental analysis, including MAX, DVFS and dynamic deployment algorithm (DDA). We have evaluated these algorithms by conducting a performance evaluation study using the CloudSim toolkit. The experimental results show that the dynamic virtual machine allocation algorithm like as DDA is more energy-efficient and the virtual machine migration is an efficient saving energy method.

Using Model Checking to Evaluate Live Migrations

Server virtualization technologies and their live migration function support more efficient use of computing resources in cloud datacenters. However, many management operations for virtual machines can be evoked simultaneously in large-scale systems. What happens if many live migrations are executed simultaneously?

Enterprise-Ready Virtual Cloud Pools: Vision, Opportunities and Challenges

Cloud computing platforms such as Amazon EC2 provide customers with flexible, on-demand resources at low cost. However, while existing offerings are useful for providing basic computation and storage resources, they have not provided the transparency, security and network controls that many enterprise customers would like. While cloud computing has a great potential to change how enterprises run and manage their IT systems, a more comprehensive control over network resources and security needs to be provided for such users. Toward this goal, we propose a virtual cloud pool (VCP) abstraction to logically unify cloud and enterprise data center resources, and present the vision behind CloudNet, a cloud platform architecture that utilizes virtual private networks to securely and seamlessly link cloud and enterprise sites. It also enables the pooling of resources across data centers to provide enterprises the capability of having cloud resources that are dynamic and adaptive to their needs. We describe several usage scenarios for VCPs and discuss the benefits of using this abstraction in enterprise settings.

Tools for Analyzing Computing Resource Management Strategies and Algorithms for SDR Clouds

AbstractSoftware defined radio (SDR) clouds centralize the computing resources of base stations. The computing resource pool is shared between radio operators and dynamically loads and unloads digital signal processing chains for providing wireless communications services on demand. Each new user session request particularly requires the allocation of computing resources for executing the corresponding SDR transceivers. The huge amount of computing resources of SDR cloud data centers and the numerous session requests at certain hours of a day require an efficient computing resource management. We propose a hierarchical approach, where the data center is divided in clusters that are managed in a distributed way. This paper presents a set of computing resource management tools for analyzing computing resource management strategies and algorithms for SDR clouds. We use the tools for evaluating a different strategies and algorithms. The results show that more sophisticated algorithms can achieve higher resource occupations and that a tradeoff exists between cluster size and algorithm complexity.

Bounds on Multiprocessing Timing Anomalies

Bounds on Multiprocessing Timing Anomalies