An Adaptive and Fuzzy Resource Management Approach in Cloud Computing

Haratian, Parinaz; Safi-Esfahani, Faramarz; Salimian, Leili; Nabiollahi, Akbar

doi:10.1109/tcc.2017.2735406

Cited by 34 publications

(17 citation statements)

References 30 publications

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“…Fuzzy modeling provides the best alternative for fuzziness and obscurities. FL has been applied to address issues in CC and EC, for instance, job scheduling [99]- [101], resource allocation [100], [102], and task offloading [103].…”

Section: Fsmentioning

confidence: 99%

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A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Section: Fsmentioning

confidence: 99%

“…2) FS for Resource Allocation: Haratian et al [100] proposed an adaptive and fuzzy resource management approach called AFRM for allocating resources in CC. In AFRM, the last resource values of each VM are collected through the environment sensors and sent to a fuzzy controller.…”

Section: Fs In CC and Ecmentioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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“…However, such dynamic and unpredictable workloads can lead to performance degradation of applications. To meet the Quality of Service (QoS) requirements based on Service Level Agreements (SLA), appropriate resource management strategies should be taken into account . A VM consolidation algorithm needs to be designed in such a way that there will be minimum energy consumption, minimum violation of SLA, efficient VM migration, and a minimum number of active hosts in a given time.…”

Section: Related Workmentioning

confidence: 99%

“…Haratian et al proposed an adaptive and fuzzy resource management approach in cloud computing. The goal is to reduce the number of SLA violations based on the optimization of resources applying an autonomous control cycle and a fuzzy knowledge management system.…”

Section: Related Workmentioning

confidence: 99%

An efficient method for allocating resources in a cloud computing environment with a load balancing approach

Pourghaffari

Barari

Kashi

2019

Concurrency and Computation

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Cloud computing technology forms a computational ensemble of large computing services and systems. Recently, it has been the focus of research on resource management, task scheduling, and effective resource sharing among users. Given the computational and resource management challenges in cloud computing, an improved method is required to approach the optimal allocation of resources. The current research combines evolutionary algorithms, fuzzy logic and task scheduling techniques to improve computational cloud resource allocation with the aim of maintaining load balance in cloud providers. Simulation of the proposed model reveals that the response time, task execution time, and energy consumption of the proposed method are better than for those of other methods. KEYWORDS cloud computing, fuzzy logic, load balancing, resource allocation INTRODUCTIONUsers demand access to computer services according to their needs, no matter where these services are located or how they are delivered. In recent years, an increasing number of business-to-consumer and enterprise applications have been run in heterogeneous clouds. 1 The explosive growth in multimedia content is shifting the global infrastructure towards a cloud-based paradigm facilitated by enormous storage capacity and high end computational resources. 2 Cloud computing is one of the best computing frameworks that can offer such services to users.Cloud computing is a model for enabling ubiquitous, on-demand network-based access to a shared pool of configurable computing resources (eg, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing, as a model for distributed computing, provides network-based access to a shared collection of reconfigurable computing resources, which are provided and released with minimal management and service provider interaction. Nowadays, IT companies are moving from the traditional CAPEX model (buy the dedicated hardware and depreciate it over a period of time) to the OPEX model (use a shared cloud infrastructure and pay as one uses it). 3 Cloud computing providers designate their resources as different examples of virtual machine (VM), which are provided to a consumer for a specified period. 4 Storage, memory, processing, and bandwidth are often more efficiently provided through cloud computing. The growth of clouds has been driven to some degree by the use of virtualization. Distributed computing suppliers provide applications through the Internet, which are accessed from a web program, while the application programs and data are secured on servers at a remote location. 5Clouds can be thought of as a service model consisting of three layers, ie, software service provider (SaaS), platform service provider (PaaS), and infrastructure service provider (IaaS). 6 The base layer of any cloud computing system is IaaS. The IaaS layer offers physical servers, virtual machines, storage, and connectivity resources to run enter...

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“…(6) Haratian et al [8] address the issue of how to reduce the number of SLA violations based on the optimization of resources allocated to users, applying an autonomous control cycle and a fuzzy knowledge management system. A framework called Adaptive and Fuzzy Resource Management (AFRM) is proposed in which the last values of resources of each VM are gathered through the sensors of the environment and sent to a fuzzy controller.…”

Section: Related Workmentioning

confidence: 99%

Type-2 Fuzzy Logic Approach for Overloaded Hosts in Consolidation of Virtual Machines in Cloud Computing

Moura¹,

Schneider²,

Yamin³

et al. 2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing. Determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system. This paper presents a new proposal with a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Results in a simulated cloud computing environment show improvements in energy efficiency of 15.64% in the Median Absolute Deviation (MAD), 15.43% in the Inter Quartile Range (IQR), and 9.19% in the Local Regression (LR).The following research questions stand out: (i) do load balancers consider the uncertainties associated with factors such as CP, CC, and RAM? (ii) are the load balancers equipped with consistent techniques for the treatment of the uncertainties related to cloud computing environments? and (iii) do the users have the necessary knowledge about the computational and communication demands of their applications?This proposal extends the approaches in [16,15], modeling the uncertain variables generating CP, CC, and RAM in Load Balancing (LB) considering Type-2 Fuzzy Logic (T2FL) which can preserve the balance between energy efficiency and manage resources while ensuring QoS through SLAs for such cloud-based services.The paper is structured as follows. Related works are presented in section 2. Section 3 introduces basic concepts of Type-2 Fuzzy Logic. In section 4, details of the Int-FLBCC component and its conception are discussed, including database, fuzzification, rule base, inference, and defuzzification. Section 5 describes the experimental evaluation. Finally, section 6 presents conclusions and future work.

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An Adaptive and Fuzzy Resource Management Approach in Cloud Computing

Cited by 34 publications

References 30 publications

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

An efficient method for allocating resources in a cloud computing environment with a load balancing approach

Type-2 Fuzzy Logic Approach for Overloaded Hosts in Consolidation of Virtual Machines in Cloud Computing

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