Security-Aware Resource Allocation for Mobile Cloud Computing Systems

Liu, Yanchen; Lee, Myung J.

doi:10.1109/icccn.2015.7288465

Cited by 25 publications

(14 citation statements)

References 15 publications

(20 reference statements)

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“…Within the purview of MEC and MCC, there has been a few initiatives for security-and risk-centric managers to support the orchestration of the cloud resources. A optimized allocation of the virtual resources to ensure performant and secure execution stand out as the major contributions behind the works [17], [18], and [19], where the differences lie in the fact that [17] applies to a system with multiple cloud layers while the others emphasized a standalone wireless and cloud system. Additionally, the authors provided a detailed cost function that can be used by MNOs to estimate the economical benefits of providing security services.…”

Section: Security Risk-aware Edge Server Orchestration a Related mentioning

confidence: 99%

“…Content may change prior to final publication. [9] Resource exhaustion, hardware failure, and SLA violations No Experimentation [12] Resource revocation risk No Experimentation [13] Data leakage No Graph Theory and heuristic [14] Reduce the risk of co-resident attack No Simulation [15] Reduce the risk of co-resident attack No Simulation [16] Reduce the risk of co-resident attack No Simulation [17] Virtual resources to protect the task execution Yes Semi-Markov Decision Process [18] Virtual resources to protect the task execution No Semi-Markov Decision Process [19] Virtual resources to protect the task execution No Semi-Markov Decision Process [20] Virtual resources to protect the task execution No Markov reward model and simulated annealing [21] Security overhead to protect the task execution Yes Genetic Algorithm [22] Security overhead to protect the task execution Yes Deep Reinforcement Learning [23] Computation and communication uncertainties Yes Game Theory [24] Risk-neutral user, risk-averse user, risk-seeking user Yes Simulation [25] IDS at the edge of the network Yes Stochastic Differential Equation [26] Service failure No Graph Theory [27] Service and server failure No System Optimization and Heuristics Our work…”

Section: Security Risk-aware Edge Server Orchestration a Related mentioning

confidence: 99%

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When Agile Security Meets 5G

et al. 2020

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5G is a critical infrastructure that will connect the whole society and bridge other critical infrastructure systems. Thus, cybersecurity emerges as crucial tenet within the 5G pathway. In this article, we discuss the concept of agile security within a 5G infrastructure taking into account two of its major technologies: Mobile Edge Computing (MEC) and Network Functions Virtualization (NFV). In this sense, we first discuss the 5G-driven MEC deployment from a NFV perspective. Secondly, we present the concept of agile security and how it can be embedded in the daily activities of mobile network operators (MNOs). Thirdly, we discuss risk management as a key element of the agile security framework. To illustrate its application, we propose the design of an agile security risk-aware edge server mechanism for 5G driven MEC deployment, which uses multiple thresholds and a load-balancing security control to mitigate the risks of resource exhaustion and violation of the service level agreement (SLA) faced by the edge servers while taking advantage of multiple cloud layers to increase the degree of availability and dependability of the system. Numerical results show that the proposed mechanism is able to keep the risk at lower levels. INDEX TERMS 5G Security, Agile Security, Cloud Security, Security Risk Management.

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Section: Security Risk-aware Edge Server Orchestration a Related mentioning

confidence: 99%

Section: Security Risk-aware Edge Server Orchestration a Related mentioning

confidence: 99%

When Agile Security Meets 5G

et al. 2020

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“…It outcomes with several challenges and solutions with managing resource strategy which give surety scalability, manageability, versatility, efficiency, and dependability of mobile cloud computing [16,17]. The resource allocation of MCC is a great challenging to face while estimating the relative size of the infrastructures [18,19]. Few investigations have discussed these issues earlier [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Energy Consumption Based Resource Management in Mobile Cloud Computing

2019

ijrte

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This paper focused on optimal energy-efficient resource allocation management in the mobile cloud services. A resource management technique depicts the various resources reservation or blocking. Energy wastage is diminished, and revenue is amplified for mobile cloud providers. The recommended method holds two stages: a) beginning stage, the task impairment, delay time, resource utilization for every task has been individually calculated, and the enthalpy was measured, and b) the second stage, the enthalpy-based Optimal Energy Allocation Supervision (OEAS) algorithm was used to optimize the resources to the powerful resource management. In this paper, the problem of the limited and relatively small battery energy power in today’s mobile devices has been restricted functionality which can include into these platforms. Diverse mobile cloud suppliers helpfully share the resources in a pool for improving resource allocation based on the users demand and distribute revenue in mobile cloud providers. The recent upgraded research in MCC from an existing work has been examined on the issues of managing resources and vital challenges in energy consumption. The new hazing technology of mobile users and robust business interests in mobile cloud environment which escort the innovative progress in mobile cloud computing. It operates intense energy methods with a low cost. This paper exhibits the research extent and classified various issues in energy saving in mobile clouds. Later, it analyzes the presented research results and mechanisms which establish its strengths and weaknesses. Energy consumption is a major problem being faced by mobile cloud computing. This paper recognizes and explains the open issues and idea of future research. The main objective is to reduce energy consumption, increase energy efficiency in computing devices and resource allocations management as well as in their executions. Energy conservation can be the optimal solution which is minimized by using less of an energy service

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“…It can be calculated using Equation 17, , (17) where loss E is the energy loss, F is the loss factor, T is the time period, peak loss load P is the load losses at peak load, P no load loss is the no-load losses.…”

Section: Energy Lossmentioning

confidence: 99%

“…The management of the network and cloud resources in cloud computing comes with several challenges, and to address these challenges, it is necessary to develop a resource management approach which ensures the scalability, manageability, adaptability, efficiency, and dependability of cloud computing [25] [15]. The management of MCC resources is also a great challenge owing to the number of objectives that need to be met while considering the relative size (large) of the infrastructures [17] [20]. Few studies have previously addressed these issues [18] [19] [20] [26], and as a consequence, it is a salient issue for service providers to design powerful resource management schemes that will satisfy the requirements of the MCC applications in real time [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

Mohammed¹,

Țăpuș²

2017

STUD INFORM CONTROL

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Abstract:The Mobile Cloud Computing (MCC) technology is a growing technology that aids in improving the quality of mobile services. The resources in MCC are dynamically allocated to the users based on their needs. The users pay for the resources consumed by their programs, but the drawbacks of process failures and knapsack problems of resource allocation still exist in MCC. Furthermore, the scheduling of energy consumption and computational cost is very high. To solve these issues, an optimized energy efficiency resource management technique is set forth in this study. The recommended method holds two stages: a) the initial stage, when the task loss, transmission probability, delay, utilization and reputation for every single task is individually measured and the enthalpy was calculated, and b) the second stage, when the enthalpy-related Cuckoo Search Optimization (CSO) algorithm was used to optimize and prioritize the resources to the powerful resource management. The execution of the recommended method automatically reduced the knapsack issue, energy and cost. A formal analysis of the resource management framework has ensured that the provider executes resources based on the power consumption. The performance of the suggested algorithm was benchmarked against the performance of other conventional algorithms.

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Security-Aware Resource Allocation for Mobile Cloud Computing Systems

Cited by 25 publications

References 15 publications

When Agile Security Meets 5G

When Agile Security Meets 5G

An Optimal Energy Consumption Based Resource Management in Mobile Cloud Computing

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

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