7,138 research outputs found
Profitable Task Allocation in Mobile Cloud Computing
We propose a game theoretic framework for task allocation in mobile cloud
computing that corresponds to offloading of compute tasks to a group of nearby
mobile devices. Specifically, in our framework, a distributor node holds a
multidimensional auction for allocating the tasks of a job among nearby mobile
nodes based on their computational capabilities and also the cost of
computation at these nodes, with the goal of reducing the overall job
completion time. Our proposed auction also has the desired incentive
compatibility property that ensures that mobile devices truthfully reveal their
capabilities and costs and that those devices benefit from the task allocation.
To deal with node mobility, we perform multiple auctions over adaptive time
intervals. We develop a heuristic approach to dynamically find the best time
intervals between auctions to minimize unnecessary auctions and the
accompanying overheads. We evaluate our framework and methods using both real
world and synthetic mobility traces. Our evaluation results show that our game
theoretic framework improves the job completion time by a factor of 2-5 in
comparison to the time taken for executing the job locally, while minimizing
the number of auctions and the accompanying overheads. Our approach is also
profitable for the nearby nodes that execute the distributor's tasks with these
nodes receiving a compensation higher than their actual costs
SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing
Mobile Cloud Computing (MCC) is the state-ofthe- art mobile computing
technology aims to alleviate resource poverty of mobile devices. Recently,
several approaches and techniques have been proposed to augment mobile devices
by leveraging cloud computing. However, long-WAN latency and trust are still
two major issues in MCC that hinder its vision. In this paper, we analyze MCC
and discuss its issues. We leverage Service Oriented Architecture (SOA) to
propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our
architecture consists of three major layers, namely SOA, arbitrator, and
infrastructure. The main strength of this architecture is in its multi-tier
infrastructure layer which leverages infrastructures from three main sources of
Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top
of the infrastructure layer, an arbitrator layer is designed to classify
Services and allocate them the suitable resources based on several metrics such
as resource requirement, latency and security. Utilizing SAMI facilitate
development and deployment of service-based platform-neutral mobile
applications.Comment: 6 full pages, accepted for publication in IEEE MobiCC'12 conference,
MobiCC 2012:IEEE Workshop on Mobile Cloud Computing, Beijing, Chin
Efficient Three-stage Auction Schemes for Cloudlets Deployment in Wireless Access Network
Cloudlet deployment and resource allocation for mobile users (MUs) have been
extensively studied in existing works for computation resource scarcity.
However, most of them failed to jointly consider the two techniques together,
and the selfishness of cloudlet and access point (AP) are ignored. Inspired by
the group-buying mechanism, this paper proposes three-stage auction schemes by
combining cloudlet placement and resource assignment, to improve the social
welfare subject to the economic properties. We first divide all MUs into some
small groups according to the associated APs. Then the MUs in same group can
trade with cloudlets in a group-buying way through the APs. Finally, the MUs
pay for the cloudlets if they are the winners in the auction scheme. We prove
that our auction schemes can work in polynomial time. We also provide the
proofs for economic properties in theory. For the purpose of performance
comparison, we compare the proposed schemes with HAF, which is a centralized
cloudlet placement scheme without auction. Numerical results confirm the
correctness and efficiency of the proposed schemes.Comment: 22 pages,12 figures, Accepted by Wireless Network
On the Economics of Cloud Markets
Cloud computing is a paradigm that has the potential to transform and
revolutionalize the next generation IT industry by making software available to
end-users as a service. A cloud, also commonly known as a cloud network,
typically comprises of hardware (network of servers) and a collection of
softwares that is made available to end-users in a pay-as-you-go manner.
Multiple public cloud providers (ex., Amazon) co-existing in a cloud computing
market provide similar services (software as a service) to its clients, both in
terms of the nature of an application, as well as in quality of service (QoS)
provision. The decision of whether a cloud hosts (or finds it profitable to
host) a service in the long-term would depend jointly on the price it sets, the
QoS guarantees it provides to its customers, and the satisfaction of the
advertised guarantees. In this paper, we devise and analyze three
inter-organizational economic models relevant to cloud networks. We formulate
our problems as non co-operative price and QoS games between multiple cloud
providers existing in a cloud market. We prove that a unique pure strategy Nash
equilibrium (NE) exists in two of the three models. Our analysis paves the path
for each cloud provider to 1) know what prices and QoS level to set for
end-users of a given service type, such that the provider could exist in the
cloud market, and 2) practically and dynamically provision appropriate capacity
for satisfying advertised QoS guarantees.Comment: 7 pages, 2 figure
Hierarchical video surveillance architecture: a chassis for video big data analytics and exploration
There is increasing reliance on video surveillance systems for systematic derivation, analysis and interpretation of the data needed for predicting, planning, evaluating and implementing public safety. This is evident from the massive number of surveillance cameras deployed across public locations. For example, in July 2013, the British Security Industry Association (BSIA) reported that over 4 million CCTV cameras had been installed in Britain alone. The BSIA also reveal that only 1.5% of these are state owned. In this paper, we propose a framework that allows access to data from privately owned cameras, with the aim of increasing the efficiency and accuracy of public safety planning, security activities, and decision support systems that are based on video integrated surveillance systems. The accuracy of results obtained from government-owned public safety infrastructure would improve greatly if privately owned surveillance systems ‘expose’ relevant video-generated metadata events, such as triggered alerts and also permit query of a metadata repository. Subsequently, a police officer, for example, with an appropriate level of system permission can query unified video systems across a large geographical area such as a city or a country to predict the location of an interesting entity, such as a pedestrian or a vehicle. This becomes possible with our proposed novel hierarchical architecture, the Fused Video Surveillance Architecture (FVSA). At the high level, FVSA comprises of a hardware framework that is supported by a multi-layer abstraction software interface. It presents video surveillance systems as an adapted computational grid of intelligent services, which is integration-enabled to communicate with other compatible systems in the Internet of Things (IoT)
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