87 research outputs found
Mobile Cloud Computing: Offloading Mobile Processing to the Cloud
The current proliferation of mobile systems, such as smart phones, PDA and tablets, has led to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device. However, this convergence penalizes the mobile system in computational resources such as processor speed, memory consumption, disk capacity, as well as in weight, size, ergonomics and the user’s most important component, battery life. Therefore, this current trend aims towards the efficient and effective use of its hardware and software components. Hence, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems.
Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for off-loading computation and data processing from mobile devices restricted by reduced resources. In cloud computing, virtualization enables a logical abstraction of physical components in a scalable manner that can overcome the physical constraint of resources. This optimizes IT infrastructure and makes cloud computing a worthy cost effective solution.
The intent of this thesis is to determine the types of applications that are better suited to be off-loaded to the cloud from mobile devices. To this end, this thesis quantitatively and
qualitatively compares the performance of executing two different kinds of workloads locally on two different mobile devices and remotely on two different cloud computing providers. The results of this thesis are expected to provide valuable insight to developers and architects of mobile applications by providing information on the applications that can be performed remotely in order to save energy and get better response times while remaining transparent to users
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
Tripod of Requirements in Horizontal Heterogeneous Mobile Cloud Computing
Recent trend of mobile computing is emerging toward executing
resource-intensive applications in mobile devices regardless of underlying
resource restrictions (e.g. limited processor and energy) that necessitate
imminent technologies. Prosperity of cloud computing in stationary computers
breeds Mobile Cloud Computing (MCC) technology that aims to augment computing
and storage capabilities of mobile devices besides conserving energy. However,
MCC is more heterogeneous and unreliable (due to wireless connectivity) compare
to cloud computing. Problems like variations in OS, data fragmentation, and
security and privacy discourage and decelerate implementation and pervasiveness
of MCC. In this paper, we describe MCC as a horizontal heterogeneous ecosystem
and identify thirteen critical metrics and approaches that influence on
mobile-cloud solutions and success of MCC. We divide them into three major
classes, namely ubiquity, trust, and energy efficiency and devise a tripod of
requirements in MCC. Our proposed tripod shows that success of MCC is
achievable by reducing mobility challenges (e.g. seamless connectivity,
fragmentation), increasing trust, and enhancing energy efficiency
A trajectory-based recruitment strategy of social sensors for participatory sensing
Participatory sensing, a promising sensing paradigm, enables people to collect and share sensor data on phenomena of interest using mobile devices across many applications, such as smart transportation and air quality monitoring. This article presents a framework of participatory sensing and then focuses on a key technical challenge: developing a trajectory-based recruitment strategy of social sensors in order to enable service providers to identify well suited participants for data sensing based on temporal availability, trust, and energy. To devise a basic recruitment strategy, the Dynamic Tensor Analysis algorithm is initially adopted to learn the time-series tensor of trajectory so that the users' trajectory can be predicted. To guarantee reliable sensing data collection and communication, the trust and energy factors are taken into account jointly in our multi-objective recruitment strategy. In particular, friend-like social sensors are also defined to deal with an emergency during participatory sensing. An illustrative example and experiment are conducted on a university campus to evaluate and demonstrate the feasibility and extensibility of the proposed recruitment strategy
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