52,582 research outputs found
An Auction Mechanism for Resource Allocation in Mobile Cloud Computing Systems
A mobile cloud computing system is composed of heterogeneous services and
resources to be allocated by the cloud service provider to mobile cloud users.
On one hand, some of these resources are substitutable (e.g., users can use
storage from different places) that they have similar functions to the users.
On the other hand, some resources are complementary that the user will need
them as a bundle (e.g., users need both wireless connection and storage for
online photo posting). In this paper, we first model the resource allocation
process of a mobile cloud computing system as an auction mechanism with premium
and discount factors. The premium and discount factors indicate complementary
and substitutable relations among cloud resources provided by the service
provider. Then, we analyze the individual rationality and incentive
compatibility (truthfulness) properties of the users in the proposed auction
mechanism. The optimal solutions of the resource allocation and cost charging
schemes in the auction mechanism is discussed afterwards
Stability of Service under Time-of-Use Pricing
We consider "time-of-use" pricing as a technique for matching supply and
demand of temporal resources with the goal of maximizing social welfare.
Relevant examples include energy, computing resources on a cloud computing
platform, and charging stations for electric vehicles, among many others. A
client/job in this setting has a window of time during which he needs service,
and a particular value for obtaining it. We assume a stochastic model for
demand, where each job materializes with some probability via an independent
Bernoulli trial. Given a per-time-unit pricing of resources, any realized job
will first try to get served by the cheapest available resource in its window
and, failing that, will try to find service at the next cheapest available
resource, and so on. Thus, the natural stochastic fluctuations in demand have
the potential to lead to cascading overload events. Our main result shows that
setting prices so as to optimally handle the {\em expected} demand works well:
with high probability, when the actual demand is instantiated, the system is
stable and the expected value of the jobs served is very close to that of the
optimal offline algorithm.Comment: To appear in STOC'1
A pricing proposal for a QoS enabled UMTS network
ArticleThird generation networks e.g. the Universal
Mobile Telecommunications System (UMTS) provide higher data
transfer rates which enables the transport of real-time
multimedia traffic e.g. streaming video. The cost of Internet
access over mobile networks remains high yet user demand for
mobile services is increasing rapidly. In order for mobile
computing to become viable, the deployment of charging schemes
that would see the cost of communication reflect the utilization of
resources on the network is necessary. A dynamic charging
scheme is an attractive solution. When prices change, users need
to indicate their willingness to continue using the service
especially when a price increase is beyond the level they
anticipated. In this paper we propose a charging scheme that
relies on the congestion at the RNC of the UMTS to calculate
pricing coefficients, which are in turn used in determining the
charge incurred for using the network. The use of user profiles
and network agents in the management of the charging scheme is
also explored.Third generation networks e.g. the Universal
Mobile Telecommunications System (UMTS) provide higher data
transfer rates which enables the transport of real-time
multimedia traffic e.g. streaming video. The cost of Internet
access over mobile networks remains high yet user demand for
mobile services is increasing rapidly. In order for mobile
computing to become viable, the deployment of charging schemes
that would see the cost of communication reflect the utilization of
resources on the network is necessary. A dynamic charging
scheme is an attractive solution. When prices change, users need
to indicate their willingness to continue using the service
especially when a price increase is beyond the level they
anticipated. In this paper we propose a charging scheme that
relies on the congestion at the RNC of the UMTS to calculate
pricing coefficients, which are in turn used in determining the
charge incurred for using the network. The use of user profiles
and network agents in the management of the charging scheme is
also explored
Supporting Big Data at the Vehicular Edge
Vehicular networks are commonplace, and many applications have been developed to utilize their sensor and computing resources. This is a great utilization of these resources as long as they are mobile. The question to ask is whether these resources could be put to use when the vehicle is not mobile. If the vehicle is parked, the resources are simply dormant and waiting for use. If the vehicle has a connection to a larger computing infrastructure, then it can put its resources towards that infrastructure. With enough vehicles interconnected, there exists a computing environment that could handle many cloud-based application services. If these vehicles were electric, then they could in return receive electrical charging services.
This Thesis will develop a simple vehicle datacenter solution based upon Smart Vehicles in a parking lot. While previous work has developed similar models based upon the idea of migration of jobs due to residency of the vehicles, this model will assume that residency times cannot be predicted and therefore no migration is utilized. In order to offset the migration of jobs, a divide-and-conquer approach is created. This uses a MapReduce process to divide the job into numerous sub-jobs and process the subtask in parallel. Finally, a checkpoint will be used between the Map and Reduce phase to avoid loss of intermediate data. This will serve as a means to test the practicality of the model and create a baseline for comparison with future research
A dynamical model of a GRID market
We discuss potential market mechanisms for the GRID. A complete dynamical
model of a GRID market is defined with three types of agents. Providers,
middlemen and users exchange universal GRID computing units (GCUs) at varying
prices. Providers and middlemen have strategies aimed at maximizing profit
while users are 'satisficing' agents, and only change their behavior if the
service they receive is sufficiently poor or overpriced. Preliminary results
from a multi-agent numerical simulation of the market model shows that the
distribution of price changes has a power law tail.Comment: 4 pages, 3 figure
Cloud Configuration Modelling: a Literature Review from an Application Integration Deployment Perspective
Enterprise Application Integration has played an important role in providing methodologies, techniques and tools to develop
integration solutions, aiming at reusing current applications and supporting the new demands that arise from the evolution of
business processes in companies. Cloud-computing is part of a new reality in which companies have at their disposal a high capacity IT infrastructure at a low-cost, in which integration solutions can be deployed and run. The charging model adopted by
cloud-computing providers is based on the amount of computing resources consumed by clients. Such demand of resources can
be computed either from the implemented integration solution, or from the conceptual model that describes it. It is desirable that
cloud-computing providers supply detailed conceptual models describing the variability of services and restrictions between
them. However, this is not the case and providers do not supply the conceptual models of their services. The conceptual model of
services is the basis to develop a process and provide supporting tools for the decision-making on the deployment of integration
solutions to the cloud. In this paper, we review the literature on cloud configuration modelling, and compare current proposals
based on a comparison framework that we have developed
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