2,574 research outputs found
Resource Management in Cloud Computing: Classification and Taxonomy
Cloud Computing is a new era of remote computing / Internet based computing
where one can access their personal resources easily from any computer through
Internet. Cloud delivers computing as a utility as it is available to the cloud
consumers on demand. It is a simple pay-per-use consumer-provider service
model. It contains large number of shared resources. So Resource Management is
always a major issue in cloud computing like any other computing paradigm. Due
to the availability of finite resources it is very challenging for cloud
providers to provide all the requested resources. From the cloud providers
perspective cloud resources must be allocated in a fair and efficient manner.
Research Survey is not available from the perspective of resource management as
a process in cloud computing. So this research paper provides a detailed
sequential view / steps on resource management in cloud computing. Firstly this
research paper classifies various resources in cloud computing. It also gives
taxonomy on resource management in cloud computing through which one can do
further research. Lastly comparisons on various resource management algorithms
has been presented
Algorithms for advance bandwidth reservation in media production networks
Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results
GPU PaaS Computation Model in Aneka Cloud Computing Environment
Due to the surge in the volume of data generated and rapid advancement in
Artificial Intelligence (AI) techniques like machine learning and deep
learning, the existing traditional computing models have become inadequate to
process an enormous volume of data and the complex application logic for
extracting intrinsic information. Computing accelerators such as Graphics
processing units (GPUs) have become de facto SIMD computing system for many big
data and machine learning applications. On the other hand, the traditional
computing model has gradually switched from conventional ownership-based
computing to subscription-based cloud computing model. However, the lack of
programming models and frameworks to develop cloud-native applications in a
seamless manner to utilize both CPU and GPU resources in the cloud has become a
bottleneck for rapid application development. To support this application
demand for simultaneous heterogeneous resource usage, programming models and
new frameworks are needed to manage the underlying resources effectively. Aneka
is emerged as a popular PaaS computing model for the development of Cloud
applications using multiple programming models like Thread, Task, and MapReduce
in a single container .NET platform. Since, Aneka addresses MIMD application
development that uses CPU based resources and GPU programming like CUDA is
designed for SIMD application development, here, the chapter discusses GPU PaaS
computing model for Aneka Clouds for rapid cloud application development for
.NET platforms. The popular opensource GPU libraries are utilized and
integrated it into the existing Aneka task programming model. The scheduling
policies are extended that automatically identify GPU machines and schedule
respective tasks accordingly. A case study on image processing is discussed to
demonstrate the system, which has been built using PaaS Aneka SDKs and CUDA
library.Comment: Submitted as book chapter, under processing, 32 page
Fog Computing: A Taxonomy, Survey and Future Directions
In recent years, the number of Internet of Things (IoT) devices/sensors has
increased to a great extent. To support the computational demand of real-time
latency-sensitive applications of largely geo-distributed IoT devices/sensors,
a new computing paradigm named "Fog computing" has been introduced. Generally,
Fog computing resides closer to the IoT devices/sensors and extends the
Cloud-based computing, storage and networking facilities. In this chapter, we
comprehensively analyse the challenges in Fogs acting as an intermediate layer
between IoT devices/ sensors and Cloud datacentres and review the current
developments in this field. We present a taxonomy of Fog computing according to
the identified challenges and its key features.We also map the existing works
to the taxonomy in order to identify current research gaps in the area of Fog
computing. Moreover, based on the observations, we propose future directions
for research
Machine-to-Machine (M2M) Communications in Virtualized Cellular Networks with MEC
As an important part of the Internet-of-Things (IoT), machine-to-machine
(M2M) communications have attracted great attention. In this paper, we
introduce mobile edge computing (MEC) into virtualized cellular networks with
M2M communications, to decrease the energy consumption and optimize the
computing resource allocation as well as improve computing capability.
Moreover, based on different functions and quality of service (QoS)
requirements, the physical network can be virtualized into several virtual
networks, and then each MTCD selects the corresponding virtual network to
access. Meanwhile, the random access process of MTCDs is formulated as a
partially observable Markov decision process (POMDP) to minimize the system
cost, which consists of both the energy consumption and execution time of
computing tasks. Furthermore, to facilitate the network architecture
integration, software-defined networking (SDN) is introduced to deal with the
diverse protocols and standards in the networks. Extensive simulation results
with different system parameters reveal that the proposed scheme could
significantly improve the system performance compared to the existing schemes
VIRTUALIZED BASEBAND UNITS CONSOLIDATION IN ADVANCED LTE NETWORKS USING MOBILITY- AND POWER-AWARE ALGORITHMS
Virtualization of baseband units in Advanced Long-Term Evolution networks and a rapid performance growth of general purpose processors naturally raise the interest in resource multiplexing. The concept of resource sharing and management between virtualized instances is not new and extensively used in data centers. We adopt some of the resource management techniques to organize virtualized baseband units on a pool of hosts and investigate the behavior of the system in order to identify features which are particularly relevant to mobile environment. Subsequently, we introduce our own resource management algorithm specifically targeted to address some of the peculiarities identified by experimental results
VUPIC: Virtual Machine Usage Based Placement in IaaS Cloud
Efficient resource allocation is one of the critical performance challenges
in an Infrastructure as a Service (IaaS) cloud. Virtual machine (VM) placement
and migration decision making methods are integral parts of these resource
allocation mechanisms. We present a novel virtual machine placement algorithm
which takes performance isolation amongst VMs and their continuous resource
usage into account while taking placement decisions. Performance isolation is a
form of resource contention between virtual machines interested in basic low
level hardware resources (CPU, memory, storage, and networks bandwidth).
Resource contention amongst multiple co-hosted neighbouring VMs form the basis
of the presented novel approach. Experiments are conducted to show the various
categories of applications and effect of performance isolation and resource
contention amongst them. A per-VM 3-dimensional Resource Utilization Vector
(RUV) has been continuously calculated and used for placement decisions while
taking conflicting resource interests of VMs into account. Experiments using
the novel placement algorithm: VUPIC, show effective improvements in VM
performance as well as overall resource utilization of the cloud.Comment: 9 Pages, 7 figure
Dynamic resource management in Cloud datacenters for Server consolidation
Cloud resource management has been a key factor for the cloud datacenters
development. Many cloud datacenters have problems in understanding and
implementing the techniques to manage, allocate and migrate the resources in
their premises. The consequences of improper resource management may result
into underutilized and wastage of resources which may also result into poor
service delivery in these datacenters. Resources like, CPU, memory, Hard disk
and servers need to be well identified and managed. In this Paper, Dynamic
Resource Management Algorithm(DRMA) shall limit itself in the management of CPU
and memory as the resources in cloud datacenters. The target is to save those
resources which may be underutilized at a particular period of time. It can be
achieved through Implementation of suitable algorithms. Here, Bin packing
algorithm can be used whereby the best fit algorithm is deployed to obtain
results and compared to select suitable algorithm for efficient use of
resources.Comment: 8 pages, 4 figure
Recent Developments in Cloud Based Systems: State of Art
Cloud computing is the new buzzword in the head of the techies round the
clock these days. The importance and the different applications of cloud
computing are overwhelming and thus, it is a topic of huge significance. It
provides several astounding features like Multitenancy, on demand service, pay
per use etc. This manuscript presents an exhaustive survey on cloud computing
technology and potential research issues in cloud computing that needs to be
addressed
Software-Defined and Virtualized Future Mobile and Wireless Networks: A Survey
With the proliferation of mobile demands and increasingly multifarious
services and applications, mobile Internet has been an irreversible trend.
Unfortunately, the current mobile and wireless network (MWN) faces a series of
pressing challenges caused by the inherent design. In this paper, we extend two
latest and promising innovations of Internet, software-defined networking and
network virtualization, to mobile and wireless scenarios. We first describe the
challenges and expectations of MWN, and analyze the opportunities provided by
the software-defined wireless network (SDWN) and wireless network
virtualization (WNV). Then, this paper focuses on SDWN and WNV by presenting
the main ideas, advantages, ongoing researches and key technologies, and open
issues respectively. Moreover, we interpret that these two technologies highly
complement each other, and further investigate efficient joint design between
them. This paper confirms that SDWN and WNV may efficiently address the crucial
challenges of MWN and significantly benefit the future mobile and wireless
network.Comment: 12 pages, 3 figures, submitted to "Mobile Networks and Applications"
(MONET
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