Cloud-Based Augmentation for Mobile Devices: Motivation, Taxonomies, and Open Challenges

Abolfazli, Saeid; Sanaei, Zohreh; Ahmed, Ejaz; Gani, Abdullah; Buyya, Rajkumar

doi:10.1109/surv.2013.070813.00285

Cited by 386 publications

(204 citation statements)

References 145 publications

(225 reference statements)

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“…of the cloud workload [3]. Also, some of the workloads patterns can be predicted as recurring jobs [33] and the deadlines for analytical jobs are more liberal by an order of magnitude than the serving jobs.…”

Section: Coordinating Green Clouds As Data-intensive Computingmentioning

confidence: 99%

“…Chapter 7 investigate emergent behavior by the constituent systems through simulations that were used to optimize of costs and resource utilization. Chapter 8 3 http://kubernetes.io furthers the research about the computing paradigm to be adopted as well as the anticipated impact on the computing hardware equipment industry (Chapter 9). We will also extend the investigation of clean energy use for cloud federation workloads (Chapter 10-12).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

2017

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CLOUD COMPUTING COST AND ENERGY OPTIMIZATION THROUGH FEDERATED CLOUD SOSThe two most significant differentiators amongst contemporary Cloud Computing service providers have increased green energy use and datacenter resource utilization. This work addresses these two issues from a system's architectural optimization viewpoint. The proposed approach herein, allows multiple cloud providers to utilize their individual computing resources in three ways by:(1) cutting the number of datacenters needed, (2) scheduling available datacenter grid energy via aggregators to reduce costs and power outages, and lastly by (3) utilizing, where appropriate, more renewable and carbon-free energy sources. Altogether our proposed approach creates an alternative paradigm for a Federated Cloud SoS approach. The proposed paradigm employs a novel control methodology that is tuned to obtain both financial and environmental advantages. It also supports dynamic expansion and contraction of computing capabilities for handling sudden variations in service demand as well as for maximizing usage of time varying green energy supplies. Herein we analyze the core SoS requirements, concept synthesis, and functional architecture with an eye on avoiding inadvertent cascading conditions. We suggest a physical architecture that diminishes unwanted outcomes while encouraging desirable results. Finally, in our approach, the constituent cloud services retain their independent ownership, objectives, funding, and sustainability means.This work analyzes the core SoS requirements, concept synthesis, and functional architecture.It suggests a physical structure that simulates the primary SoS emergent behavior to diminish unwanted outcomes while encouraging desirable results. The report will analyze optimal computing generation methods, optimal energy utilization for computing generation as well as a procedure for building optimal datacenters using a unique hardware computing system design based on the openCompute community as an illustrative collaboration platform. Finally, the research concludes with ii security features cloud federation requires to support to protect its constituents, its constituents tenants and itself from security risks.iii ACKNOWLEDGEMENTS

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Section: Coordinating Green Clouds As Data-intensive Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud Computing Cost and Energy Optimization through Federated Cloud SoS

2017

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“…Autonomic computing manage applications execution in dynamically changing and unpredictable environments without any user's manual intervention [15,102,109] by regularly checking and monitoring the changing conditions automatically. Mobile computing extends the computing facilities to the smart mobile devices in order to enhance their capabilities on demand for accommodating computational-and storage-intensive applications [1,15,59,111,118].…”

Section: Immediate Extension Of Grid Resource Managementmentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy A. Y. Zomaya University of Sydney, Sydney, Australia user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing-and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.

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“…Mobile Cloud Computing (MCC) as one of the paradigms which amalgamated Cloud Computing with Mobile Computing was defined by [2] as -the combination of cloud computing, mobile computing and wireless networks to bring rich computational resources to network operators, mobile users and cloud computing providers‖.…”

Section: Introductionmentioning

confidence: 99%