1,325 research outputs found
Energy-aware Load Balancing Policies for the Cloud Ecosystem
The energy consumption of computer and communication systems does not scale
linearly with the workload. A system uses a significant amount of energy even
when idle or lightly loaded. A widely reported solution to resource management
in large data centers is to concentrate the load on a subset of servers and,
whenever possible, switch the rest of the servers to one of the possible sleep
states. We propose a reformulation of the traditional concept of load balancing
aiming to optimize the energy consumption of a large-scale system: {\it
distribute the workload evenly to the smallest set of servers operating at an
optimal energy level, while observing QoS constraints, such as the response
time.} Our model applies to clustered systems; the model also requires that the
demand for system resources to increase at a bounded rate in each reallocation
interval. In this paper we report the VM migration costs for application
scaling.Comment: 10 Page
Load Balancing and Virtual Machine Allocation in Cloud-based Data Centers
As cloud services see an exponential increase in consumers, the demand for faster processing of data and a reliable delivery of services becomes a pressing concern. This puts a lot of pressure on the cloud-based data centers, where the consumers’ data is stored, processed and serviced. The rising demand for high quality services and the constrained environment, make load balancing within the cloud data centers a vital concern. This project aims to achieve load balancing within the data centers by means of implementing a Virtual Machine allocation policy, based on consensus algorithm technique. The cloud-based data center system, consisting of Virtual Machines has been simulated on CloudSim – a Java based cloud simulator
An optimal VM Placement, Energy Efficient and SLA at Cloud Environment - A Comparative Analysis
In the cloud computing framework, computing resources can be increased or decreased in response to the users’ different application loads. The data is stored and the applications are running on the servers in the clouds. Users do not have to worry about lost or corrupt data. The clouds can distribute computing resources according to the users’ needs or preferences to provide fl exible management. Users do not have to buy expensive computing devices. They only need to pay for the computing services provided by the clouds. Cloud computing provides a platform for computational experiments with abundant computing and storage resources. The system can be considered as a whole and the control and management decisions are sent as services to agents. The challenge in the present study is to reduce energy consumption thus guarantee Service Level Agreement (SLA) at its highest level
A novel cost-based replica server placement for optimal service quality in cloud-based content delivery network
Replica server placement is one of the crucial concerns for a given geographic diversity associated with placement problems in content delivery network (CDN). After reviewing the existing literatures, it is noted that studies are more for solving placement problem in conventional CDN and not much over cloud-based CDN architectures, which some few studies are reported towards replica selection are much in its nascent stages of development. Moreover, such models are not benchmarked or practically assessed to prove its effectiveness. Hence, the proposed study introduces a novel design of computational framework associated with cloud-based CDN which can facilitate cost-effective replica server management for enhanced service delivery. Implemented using analytical research methodology, the simulated study outcome shows that proposed scheme offers reduced cost, reduced resource dependencies, reduced latency, and faster processing time in contrast to existing models of replica server placement
Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)
In this tutorial paper, we will firstly review some basic simulation concepts
and then introduce the parallel and distributed simulation techniques in view
of some new challenges of today and tomorrow. More in particular, in the last
years there has been a wide diffusion of many cores architectures and we can
expect this trend to continue. On the other hand, the success of cloud
computing is strongly promoting the everything as a service paradigm. Is
parallel and distributed simulation ready for these new challenges? The current
approaches present many limitations in terms of usability and adaptivity: there
is a strong need for new evaluation metrics and for revising the currently
implemented mechanisms. In the last part of the paper, we propose a new
approach based on multi-agent systems for the simulation of complex systems. It
is possible to implement advanced techniques such as the migration of simulated
entities in order to build mechanisms that are both adaptive and very easy to
use. Adaptive mechanisms are able to significantly reduce the communication
cost in the parallel/distributed architectures, to implement load-balance
techniques and to cope with execution environments that are both variable and
dynamic. Finally, such mechanisms will be used to build simulations on top of
unreliable cloud services.Comment: Tutorial paper published in the Proceedings of the International
Conference on High Performance Computing and Simulation (HPCS 2011). Istanbul
(Turkey), IEEE, July 2011. ISBN 978-1-61284-382-
State of The Art and Hot Aspects in Cloud Data Storage Security
Along with the evolution of cloud computing and cloud storage towards matu-
rity, researchers have analyzed an increasing range of cloud computing security
aspects, data security being an important topic in this area. In this paper, we
examine the state of the art in cloud storage security through an overview of
selected peer reviewed publications. We address the question of defining cloud
storage security and its different aspects, as well as enumerate the main vec-
tors of attack on cloud storage. The reviewed papers present techniques for key
management and controlled disclosure of encrypted data in cloud storage, while
novel ideas regarding secure operations on encrypted data and methods for pro-
tection of data in fully virtualized environments provide a glimpse of the toolbox
available for securing cloud storage. Finally, new challenges such as emergent
government regulation call for solutions to problems that did not receive enough
attention in earlier stages of cloud computing, such as for example geographical
location of data. The methods presented in the papers selected for this review
represent only a small fraction of the wide research effort within cloud storage
security. Nevertheless, they serve as an indication of the diversity of problems
that are being addressed
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