Analysis of the Energy-Response Time Tradeoff for Mobile Cloud Offloading Using Combined Metrics

The mobile devices capabilities are found to be greater than before by utilizing the cloud services. There are various of service rendered by the cloud paradigm and the mobile devices usually allows the execution of the resource-intensive applications on the resource- constrained mobile device to be offloaded to the cloudlets that are resource rich thus enhancing the its processing capabilities. But accessing the cloud services within the minimum response time and energy consumption still remains as a serious research problem. So the proposed method put forth in the paper scopes in developing a frame work to choose the optimal cloud service provider. The frame work proposed is categorized into two stages where the initial stage engages the classifier to segregate the mobile device according to the fuzzy K-nearest neighbor and cultivates an improved computational offloading employing the Hidden Markov Model and ACO- ant colony optimization. The algorithm proffered is implemented in the MATLAB version 9.1 and the performance is evinced on the basis of the response time, energy consumption and the processing cost. The results obtained through the proposed method proves to provide an 89% better response time, 95 % better energy consumption and 50% enhanced processing cost compared to the few existing computational offloading methods put forth for the mobile cloud computing.

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Energy Efficient Real-Time Distributed Communication Architectures for Military Tactical Communication Systems

Advances in Wireless Technologies and Telecommunication - Enabling Real-Time Mobile Cloud Computing through Emerging Technologies ◽

10.4018/978-1-4666-8662-5.ch002 ◽

2015 ◽

pp. 35-82

Author(s):

Bora Karaoglu ◽

Tolga Numanoglu ◽

Bulent Tavli ◽

Wendi Heinzelman

Keyword(s):

Cloud Computing ◽

Response Time ◽

Real Time ◽

Energy Efficient ◽

Communication Systems ◽

Mobile Cloud Computing ◽

Mobile Cloud ◽

Communication Architectures ◽

Distributed Communication ◽

Communication Modes

For military communication systems, it is important to achieve robust and energy efficient real-time communication among a group of mobile users without the support of a pre-existing infrastructure. Furthermore, these communication systems must support multiple communication modes, such as unicast, multicast, and network-wide broadcast, to serve the varied needs in military communication systems. One use for these military communication systems is in support of real-time mobile cloud computing, where the response time is of utmost importance; therefore, satisfying real-time communication requirements is crucial. In this chapter, we present a brief overview of military tactical communications and networking (MTCAN). As an important example of MTCAN, we present the evolution of the TRACE family of protocols, describing the design of the TRACE protocols according to the tactical communications and networking requirements. We conclude the chapter by identifying how the TRACE protocols can enable mobile cloud computing within military communication systems.

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Response Time Analysis Considering Sensing Data Synchronization in Mobile Cloud Applications

The Journal of The Institute of Internet Broadcasting and Communication ◽

10.7236/jiibc.2015.15.3.137 ◽

2015 ◽

Vol 15 (3) ◽

pp. 137-141 ◽

Cited By ~ 1

Author(s):

Hong Min ◽

Junyoung Heo

Keyword(s):

Response Time ◽

Data Synchronization ◽

Mobile Cloud ◽

Response Time Analysis ◽

Time Analysis ◽

Sensing Data ◽

Cloud Applications

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A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.20210701.oa5 ◽

2021 ◽

Vol 15 (3) ◽

pp. 58-73

Author(s):

Somula Ramasubbareddy ◽

Evakattu Swetha ◽

Ashish Kumar Luhach ◽

T. Aditya Sai Srinivas

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Load Balancing ◽

Response Time ◽

Execution Time ◽

Mobile Cloud Computing ◽

Mobile Cloud ◽

Heterogeneous Environments ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

Mobile cloud computing is an emerging technology in recent years. This technology reduces battery consumption and execution time by executing mobile applications in remote cloud server. The virtual machine (VM) load balancing among cloudlets in MCC improves the performance of application in terms of response time. Genetic algorithm (GA) is popular for providing optimal solution for load balancing problems. GA can perform well in both homogeneous and heterogeneous environments. In this paper, the authors consider multi-objective genetic algorithm for load balancing in MCC (MOGALMCC) environment. In MOGALMCC, they consider distance, bandwidth, memory, and cloudlet server load to find optimal cloudlet before scheduling VM in another cloudlet. The framework MOGALMCC aims to improve response time as well as minimizes VM failure rate. The experiment result shows that proposed model performed well by reducing execution time and task waiting time at server.

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Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000339 ◽

2016 ◽

Vol 63 (6) ◽

pp. 333-342

Author(s):

Roberto Limongi ◽

Angélica M. Silva

Keyword(s):

Response Time ◽

Short Term Memory ◽

Estimation Error ◽

Predictive Coding ◽

Time Estimation ◽

Prediction Errors ◽

Memory Scanning ◽

Short Term ◽

Term Memory ◽

Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

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