By F. Richard Yu, Victor Leung
With contemporary advances in cellular communique applied sciences, progressively more everyone is getting access to cloud computing platforms utilizing cellular units, comparable to smartphones and pills. in contrast to conventional cellular computing platforms with constrained functions, cellular cloud computing makes use of the robust computing and garage assets on hand within the cloud to supply state of the art multimedia and knowledge providers. This booklet discusses the main learn advances in cellular cloud computing structures. Contributed chapters from major specialists during this box conceal diversified points of modeling, research, layout, optimization, and structure of cellular cloud computing systems.
Advances in cellular Cloud Computing structures
begins by means of discussing the heritage, positive factors, and to be had provider versions of cellular cloud computing. It is going directly to describe a cellular cloud computing procedure with numerous 3rd social gathering cloud cellular media (CMM) providers that gives its prone to a telecom operator. during this state of affairs, the telecom operator acts as dealer which can combine and interchange the assets provided by means of different CMM carrier prone. next contributed chapters speak about such key learn components as
- Energy-efficient job execution that reduces the strength intake in either cellular units and the cloud
- Design and structure of a Proximity Cloud that supplies low-latency, bandwidth-efficient end-user providers with a world footprint
- Virtual cellular networks in clouds that allow source sharing among a number of cellular community operators
- Software piracy keep watch over framework in cellular cloud computing structures designed to avoid cellular software piracy
- Dynamic configuration of cloud radio entry networks (C-RANs) to enhance end-to-end TCP throughput functionality in subsequent new release instant networks
The booklet comprises many aiding illustrations and tables in addition to a precious set of references on the finish of every bankruptcy. With this publication, researchers and practitioners can be well-equipped to strengthen the examine and improvement during this rising field.
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Extra resources for Advances in mobile cloud computing systems
2 Characterization of Optimal Solution . . . 3 Approximate Task Execution Algorithm . . . . . . . . . . . . . . . . . . . 4 Performance Evaluation . . . . . . . . . . . 3 Application as a General Topology . . . . . . . . . . . 1 Task Model and Problem Formulation . . . 2 Workflow Scheduling Algorithm . . . . . . 89 Policy in Task Delegation . . . . . . . . . . . . . . . . . . . . 1 Transcoding as a Service .
8) Therefore, we can obtain the optimal power allocation strategy pk∗ as follows: pk∗ ⎤+ ⎡ ⎢⎢ W (1 − μk xr Brk ) σ2k ⎥⎥ = ⎢⎢⎣ − 2 ⎥⎥⎦ . 9) Mobile Cloud Computing with Telecom Operator Cloud 23 Because the function of sk is monotonic decreasing, then we know that it will achieve the maximum when sk takes the minimum, that is, ⎛ ⎞ ∗ 2 h ⎜ ⎟⎟ p ⎜ sk∗ = W log2 ⎜⎜⎝1 + k 2 k ⎟⎟⎠ σk ⎛ 2 ⎞⎤+ ⎡ ⎜⎜ hk W (1 − μk xr Brk ) ⎟⎟⎥⎥ ⎢⎢ ⎟⎟⎥⎥ . 10) = ⎢⎢⎣W log2 ⎜⎜⎝ ⎠⎦ 2 ln 2λk ygkm σ2k When 1 − μk xr Brk < 0, the utility function of pk is a convex function, then we can obtain the minimum of the function as follows: ⎤+ ⎡ ⎢⎢ W (1 − μk xr Brk ) σ2k ⎥⎥ pk = ⎢⎢⎣ − 2 ⎥⎥⎦ .
The objective was to minimize the interaction between the mobile device and the cloud as well as the amount of exchanged data. However, it did not consider the energy consumption on the mobile device. Huang et al. in  presented a dynamic oﬀ-loading algorithm based on Lyapunov optimization, which provides a suboptimal solution to save energy on the mobile device while meeting the application deadline. Lin et al. in  derived the optimal oﬀ-loading policy by maximizing the expected sum on performance and power consumption using dynamic programming.
Advances in mobile cloud computing systems by F. Richard Yu, Victor Leung