Resources In Cloud Environment Research Articles

Bridging the SNMP gap: Simple network monitoring the internet of things

Things that form Internet of Things can vary in every imaginable aspect. From simplest devices with barely any processing and memory resources, with communication handled by networking devices like switches and routers to powerful servers that provide needed back-end resources in cloud environments, all are needed for real world implementations of Internet of Things. Monitoring of the network and server parts of the infrastructure is a well known area with numerous approaches that enable efficient monitoring. Most prevalent technology used is SNMP that forms the part of the IP stack and is as such universally supported. On the other hand, ?things? domain is evolving very fast with a number of competing technologies used for communication and monitoring. When discussing small, constrained devices, the two most promising protocols are CoAP and MQTT. Combined, they cover wide area of communication needs for resource constrained devices, from simple messaging system to one that enables connecting to RESTful world. In this paper we present a possible solution to bridge the gap in monitoring by enabling SNMP access to monitoring data obtained from constrained devices that cannot feasibly support SNMP or are not intended to be used in such a manner.

Review on Scheduling of Resources in Cloud Environment

Cloud computing provides an environment, where a large pool of systems are connected in networks (i.e., private or public), to provide dynamically scalable infrastructure for storage of application, data and file. Resource allocation is a way to assign resources to the needed applications. Cloud service providers use lease manager for better resource allocation. Haizea is a lease manager that takes lease request and make scheduling decision. We describe cloud computing and its properties, different leases in Haizea and scheduling of resources in Haizea.

A Dynamic Resource Scheduling Method Based on Fuzzy Control Theory in Cloud Environment

The resources in cloud environment have features such as large-scale, diversity, and heterogeneity. Moreover, the user requirements for cloud computing resources are commonly characterized by uncertainty and imprecision. Hereby, to improve the quality of cloud computing service, not merely should the traditional standards such as cost and bandwidth be satisfied, but also particular emphasis should be laid on some extended standards such as system friendliness. This paper proposes a dynamic resource scheduling method based on fuzzy control theory. Firstly, the resource requirements prediction model is established. Then the relationships between resource availability and the resource requirements are concluded. Afterwards fuzzy control theory is adopted to realize a friendly match between user needs and resources availability. Results show that this approach improves the resources scheduling efficiency and the quality of service (QoS) of cloud computing.

Memory Virtualization Technique for Efficient Access of Data Resources in Cloud Environment

Working in cloud environment and accessing its services is fascinating task. It is one of modern technological areas to work upon it for development of nation and increase economy rate. The paper deals with improving desktop cloud computing services with the help of technique called Memory Virtualization. Since virtualization is efficient to extent scalability of typical desktop-as-a-service and make it suitable for Wide Area Network (WAN’s) environments. Desktop-as-a-Service (DaaS) in cloud computing means that user can access its own desktop services as well as services of other desktops located on remote servers. Memory virtualization not only works with virtual memory but it also maps physical memory instructions to actual machine memory. The instructions stored in machine memory are checked with incoming user client requests. Similar matched queries will result in generation of data using one of recovery techniques called Shadow Paging Technique. Data is produced and stored in remote cloud environment where user can access the data to perform their tasks. The proposed systematic model for accessing desktop services is shown in following paper by using memory virtualization technique. A comparison is also made between paravirtualization technique and memory virtualization technique that gives solution in favor of memory virtualization technique.

Cluster Energy Optimization: An Algorithmic Approach

In fact, Gartner projected global revenue for cloud computing to reach almost $150 billion by 2014. However, The 2011 market is already approx $68 billion globally. With increase in web technologies and Internet, a proportional increase in Cloud computing technologies has been cited. Cloud computing has been emerging as a flexible and powerful computational architecture to offer ubiquitous services to users. A variety of hardware and software resources are integrated together as a resource pool, the software is no longer resided in a single hardware environment, it is performed upon the schedule of the resource pool for optimized resource utilization. The optimization of energy consumption in the cloud computing environment is the question how to use various energy conservation strategies to efficiently allocate resources. The need of different resources in cloud environment is unpredictable. It is observed that load management in cloud is utmost needed in order to provide QOS. The jobs at over-loaded physical machine are shifted to under-loaded physical machine and turning the idle machine off in order to provide green cloud. For energy optimization, DVFS and Power-Nap are good strategies. As much of this energy is wasted in idle systems: in typical deployments, server utilization is below 30%, but idle servers still consume 60% of their peak power draw. In this paper, we have proposed an algorithm for energy optimization having the constraint QOS and SLA.

LTKDC: Lattice Theory Based Knowledge Discovery Cloud

In latest years, there has been an intense growth in large amount of existing computation and storage resources. Cloud Environment is a new platform for both intensive application of computation and storage, it hence become the proper solution to support the high performance data mining application service. We present the LTKDC system, which is based on lattice theory and leverages data mining application to let client to take advantage of resources in cloud environment. We describe the structure of LTKDC and especially for how it handles parallel jobs management.

An Integrated Approach to Automatic Management of Virtualized Resources in Cloud Environments

Cloud computing, as a newly emergent computing environment, promises dynamic flexible infrastructures required to host Internet applications and application service level objects (SLOs) guaranteed services in a pay-as-you-go manner to the public. However, an important problem that remains to be effectively addressed is how to offer a cloud resource management solution that saves hardware and operations and management costs while meeting various SLOs. It faces the following challenges: complex dynamic relationships between application workload and SLOs and resource utilization, and the virtual machine (VM) placement problem in cloud environments. In this paper, we present an integrated approach that employs three-layered resource controllers using different analytic techniques, including the feedback control theory, statistical machine learning and system identification etc. Compared with Xen, KVM is chosen as the VM monitor to implement the proposed approach. Our experimental results show that the integration of layered controllers can reasonably allocate multiple resources to applications which execute on different VMs in cloud environments to achieve application SLOs under fluctuating time-varying workloads and unpredictable variations of system situations. In addition, it provides application SLO differentiation.