Optimal Selection Techniques for Cloud Service Providers

Tricomi, Giuseppe; Merlino, Giovanni; Panarello, Alfonso; Puliafito, Antonio

doi:10.1109/access.2020.3035816

Cited by 12 publications

(5 citation statements)

References 83 publications

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“…Computing resources are made available to customers via Virtual Machines (VMs) running on physical machines in the cloud. Virtual machines resemble physical computers in terms of capability [11]. As a guest program, a VM mimics the functionality of a physical machine.…”

Section: B Problem Statementmentioning

confidence: 99%

“…𝑁𝑒𝑤 𝑋 𝑖,𝑗 = 𝑋 𝑖,𝑗 + 𝜙 𝑖,𝑗 (𝑋 𝐺𝑏𝑒𝑠𝑡,𝑗 − 𝑋 𝑘,𝑗 ) + 𝛽(𝑋 𝐺𝑏𝑒𝑠𝑡,𝑗 − 𝑋 𝑖,𝑗 ) (11) While the current generation's optimal solution information is incorporated into the neighborhood search operator, the random neighborhood search approach remains unchanged. Eq.…”

Section: Modified Abc Algorithm For Task Schedulingmentioning

confidence: 99%

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Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

LI,

WANG

2024

ijacsa

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Task scheduling in cloud computing is a complex optimization problem influenced by the ever-changing user requirements and the different architectures of cloud systems. Efficiently distributing workloads across Virtual Machines (VMs) is critical to mitigate the negative consequences of inadequate and excessive workloads, such as higher power consumption and possible machine malfunctions. This paper presents a novel method for dynamic load balancing using a Modified Artificial Bee Colony (MABC) algorithm. The ABC algorithm has exceptional competence in solving complex nonlinear optimization problems based on bee colonies' foraging behavior. Nevertheless, the traditional version of the ABC algorithm cannot effectively use resources, resulting in a rapid decline in population diversity and an ineffective spread of knowledge about the best solution between generations. To address these limitations, this study integrates a genetic model into the algorithm, enhancing population diversity through crossover and mutation operators. The developed algorithm is compared with the prevailing algorithms to confirm its effectiveness. The results of the proposed MABC algorithm for the load balancing method are compared with the current ones, and it is observed that this algorithm is more beneficial in terms of cost and energy as well as resource utilization.

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Section: B Problem Statementmentioning

confidence: 99%

Section: Modified Abc Algorithm For Task Schedulingmentioning

confidence: 99%

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

LI,

WANG

2024

ijacsa

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“…Cloud services are crucial to digital transformation because adoption of the services enables a firm to rapidly support various new technologies and provide the needed flexibility related to processing power, software deployments, and data storage at a lower cost [17,18]. Cloud services also can immediately improve a firm's infrastructure from legacy and often "on-premise" systems with tightly integrated advanced analytical, machine learning, and artificial intelligence tools and services that would be difficult, if not impossible, to replicate in an on-premise environment [4,[19][20][21]. Even though cloud services needed for digital transformation can be easier to implement and scale than on-premise systems, implementation can still be challenging.…”

Section: Cloud Services Needed For Digital Transformationmentioning

confidence: 99%

Creation of a Unified Cloud Readiness Assessment Model to Improve Digital Transformation Strategy

Smith¹

2022

IJDSA

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Digital transformation can disrupt any organization in any industry, but few organizations have successfully transformed. For an organization to transform digitally, a firm must adopt new technologies that enable it to change how it creates value. One of the most crucial new technologies organizations need to facilitate digital transformation is Cloud services. Cloud-based technologies are necessary for digital transformation because they allow a firm to cost-effectively obtain needed infrastructure capacity, processing, and developmental flexibility to support advanced analytical tools and methods. Implementing and adopting cloud services can be challenging and requires firm leaders and transformation teams to have an effective strategy that requires understanding a company's cloud readiness. Multiple organizational factors can impact cloud readiness, and depending on a firm's strengths or weaknesses, each element will support or hinder the adoption of cloud services. Understanding a firm's cloud readiness has been shown to improve an organization's adoption of cloud services, but assessing readiness can also be challenging since different assessment models determine readiness in varying ways. Readiness assessment models range from generic technology adoption models to specific cloud services readiness models, and a unified approach is needed that combines the strengths of each model to create a more comprehensive assessment model. A meta-analysis of current readiness assessment models was conducted to identify what crucial factors of an organization need to be assessed. The findings show that there seems to be significant agreement that a company's strategy, current technology, existing operations, and external factors are crucial readiness factors. More recent assessment models also identify gaps in past models, especially on human capital capabilities, system flexibility needs, and security. A more unified cloud assessment model is proposed based on the analysis showing that a firm's readiness should be based on seven crucial factors: strategy, technology, current operations, external requirements, human capital, system flexibility, and security. The new proposed assessment model provides a more comprehensive assessment of a firm's cloud readiness and enables organizations to create an improved adoptions strategy that will better support a company's digital transformation.

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“…In this research path, cooperation schemes and techniques for the selection of resource providers are important issues to be addressed. A literature review to understand the differences between brokered and decentralized Federated Cloud Service Providers is available in [ 23 ].…”

Section: Complex Cpss In a Glimpsementioning

confidence: 99%

Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies

Puliafito

Tricomi

Zafeiropoulos

et al. 2021

Sensors

Self Cite

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A smart city represents an improvement of today’s cities, both functionally and structurally, that strategically utilizes several smart factors, capitalizing on Information and Communications Technology (ICT) to increase the city’s sustainable growth and strengthen the city’s functions, while ensuring the citizens’ enhanced quality of life and health. Cities can be viewed as a microcosm of interconnected “objects” with which citizens interact daily, which represents an extremely interesting example of a cyber physical system (CPS), where the continuous monitoring of a city’s status occurs through sensors and processors applied within the real-world infrastructure. Each object in a city can be both the collector and distributor of information regarding mobility, energy consumption, air pollution as well as potentially offering cultural and tourist information. As a consequence, the cyber and real worlds are strongly linked and interdependent in a smart city. New services can be deployed when needed, and evaluation mechanisms can be set up to assess the health and success of a smart city. In particular, the objectives of creating ICT-enabled smart city environments target (but are not limited to) improved city services; optimized decision-making; the creation of smart urban infrastructures; the orchestration of cyber and physical resources; addressing challenging urban issues, such as environmental pollution, transportation management, energy usage and public health; the optimization of the use and benefits of next generation (5G and beyond) communication; the capitalization of social networks and their analysis; support for tactile internet applications; and the inspiration of urban citizens to improve their quality of life. However, the large scale deployment of cyber-physical-social systems faces a series of challenges and issues (e.g., energy efficiency requirements, architecture, protocol stack design, implementation, and security), which requires more smart sensing and computing methods as well as advanced networking and communications technologies to provide more pervasive cyber-physical-social services. In this paper, we discuss the challenges, the state-of-the-art, and the solutions to a set of currently unresolved key questions related to CPSs and smart cities.

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Optimal Selection Techniques for Cloud Service Providers

Cited by 12 publications

References 83 publications

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

Modified Artificial Bee Colony Algorithm for Load Balancing in Cloud Computing Environments

Creation of a Unified Cloud Readiness Assessment Model to Improve Digital Transformation Strategy

Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies

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