24,252 research outputs found
Run-time Energy Management for Mobiles
Due to limited energy resources, mobile computing requires an energy-efficient a rchitecture. The dynamic nature of a mobile environment demands an architecture that allows adapting to (quickly) changing conditions. The mobile has to adapt d ynamically to new circumstances in the best suitable manner. The hardware and so ftware architecture should be able to support such adaptability and minimize the energy consumption by making resource allocation decisions at run-time. To make these decisions effective, a tradeoff has to be made between computation , communication and initialization costs (both time and energy). This paper describes our approach to construct a model that supports taking such decisions
On Optimal and Fair Service Allocation in Mobile Cloud Computing
This paper studies the optimal and fair service allocation for a variety of
mobile applications (single or group and collaborative mobile applications) in
mobile cloud computing. We exploit the observation that using tiered clouds,
i.e. clouds at multiple levels (local and public) can increase the performance
and scalability of mobile applications. We proposed a novel framework to model
mobile applications as a location-time workflows (LTW) of tasks; here users
mobility patterns are translated to mobile service usage patterns. We show that
an optimal mapping of LTWs to tiered cloud resources considering multiple QoS
goals such application delay, device power consumption and user cost/price is
an NP-hard problem for both single and group-based applications. We propose an
efficient heuristic algorithm called MuSIC that is able to perform well (73% of
optimal, 30% better than simple strategies), and scale well to a large number
of users while ensuring high mobile application QoS. We evaluate MuSIC and the
2-tier mobile cloud approach via implementation (on real world clouds) and
extensive simulations using rich mobile applications like intensive signal
processing, video streaming and multimedia file sharing applications. Our
experimental and simulation results indicate that MuSIC supports scalable
operation (100+ concurrent users executing complex workflows) while improving
QoS. We observe about 25% lower delays and power (under fixed price
constraints) and about 35% decrease in price (considering fixed delay) in
comparison to only using the public cloud. Our studies also show that MuSIC
performs quite well under different mobility patterns, e.g. random waypoint and
Manhattan models
Mengenal pasti masalah pemahaman dan hubungannya dengan latar belakang matematik, gaya pembelajaran, motivasi dan minat pelajar terhadap bab pengawalan kos makanan di Sekolah Menengah Teknik (ert) Rembau: satu kajian kes.
Kajian ini dijalankan untuk mengkaji hubungan korelasi antara latar belakang Matematik, gaya pembelajaran, motivasi dan minat dengan pemahaman pelajar terhadap bab tersebut. Responden adalah seramai 30 orang iaitu terdiri daripada pelajar tingkatan lima kursus Katering, Sekolah Menengah Teknik (ERT) Rembau, Negeri Sembilan. Instrumen kajian adalah soal selidik dan semua data dianalisis menggunakan program SPSS versi 10.0 untuk mendapatkan nilai min dan nilai korelasi bagi memenuhi objektif yang telah ditetapkan. Hasil kajian ini menunjukkan bahawa hubungan korelasi antara gaya pembelajaran pelajar terhadap pemahaman pelajar adalah kuat. Manakala hubungan korelasi antara latar belakang Matematik, motivasi dan minat terhadap pemahaman pelajar adalah sederhana. Nilai tahap min bagi masalah pemahaman pelajar, latar belakang Matematik, gaya pembelajaran, motivasi dan minat terhadap bab Pengawalan Kos Makanan adalah sederhana. Kajian ini mencadangkan penghasilan satu Modul Pembelajaran Kendiri bagi bab Pengawalan Kos Makanan untuk membantu pelajar kursus Katering dalam proses pembelajaran mereka
A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
Compared to traditional distributed computing environments such as grids,
cloud computing provides a more cost-effective way to deploy scientific
workflows. Each task of a scientific workflow requires several large datasets
that are located in different datacenters from the cloud computing environment,
resulting in serious data transmission delays. Edge computing reduces the data
transmission delays and supports the fixed storing manner for scientific
workflow private datasets, but there is a bottleneck in its storage capacity.
It is a challenge to combine the advantages of both edge computing and cloud
computing to rationalize the data placement of scientific workflow, and
optimize the data transmission time across different datacenters. Traditional
data placement strategies maintain load balancing with a given number of
datacenters, which results in a large data transmission time. In this study, a
self-adaptive discrete particle swarm optimization algorithm with genetic
algorithm operators (GA-DPSO) was proposed to optimize the data transmission
time when placing data for a scientific workflow. This approach considered the
characteristics of data placement combining edge computing and cloud computing.
In addition, it considered the impact factors impacting transmission delay,
such as the band-width between datacenters, the number of edge datacenters, and
the storage capacity of edge datacenters. The crossover operator and mutation
operator of the genetic algorithm were adopted to avoid the premature
convergence of the traditional particle swarm optimization algorithm, which
enhanced the diversity of population evolution and effectively reduced the data
transmission time. The experimental results show that the data placement
strategy based on GA-DPSO can effectively reduce the data transmission time
during workflow execution combining edge computing and cloud computing
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