Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

Yakubu, Ismail Zahraddeen; Musa, Zainab Aliyu; Muhammed, Lele; Ja’afaru, Badamasi; Shittu, Fatima; Matinja, Zakari Idris

doi:10.1016/j.procs.2020.11.039

Cited by 10 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Task scheduling is becoming a problem to achieve optimum resource utilization and minimal execution time while preserving quality of service. (4,5) Task scheduling is a vital strategy in cloud because it helps allocate jobs to the right resources for efficient resource use and overall system performance optimization.…”

Section: Introductionmentioning

confidence: 99%

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Banu Priya,

Maheswari

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

View full text Add to dashboard Cite

Introduction: The advantages and wide-ranging applications of cloud computing have made it a prominent attention for scholars these days. The dispersed structure of cloud and its whole dependence on the internet for service delivery extant security problems. Methods: Hypervisor attack detection is carried out in this study using Modified Advanced Encryption Standard (MAES) system which ensures security prominently. It finds and detects the attacks earlier for secured VM migration. The proposed system includes main phases including system framework, load balancing, resource allocation and hypervisor attack detection via MAES algorithm. Initially, Over the duration of cloud computing, consider the quantity of tasks, VM, and cloud users. In this research, the MMH system is employed for load balancing to balance the total burden across the cloud. Tasks are moved from overloaded to underloaded nodes to attain load balancing. Next, the allocation of resources is carried out utilizing Adaptive Firefly (AF) optimization system which is used to select best resources optimally. It generates the best fitness values to choose the best resources. Results: It is also focused to improve the cost metric, computational complexity, throughput and VM performance in cloud. Then, to detect hypervisor attacks, the MAES method is employed. It specializes on offering enhanced security for cloud data and is employed to identify hypervisor and VM attackers. Conclusion: The findings produced the conclusion that the suggested MAES method superior to the current approaches according to throughput, computation cost, Mean Square Error (MSE) rate, and energy use.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Banu Priya,

Maheswari

2024

Salud, Ciencia y Tecnología - Serie de Conferencias

View full text Add to dashboard Cite

show abstract

“…Cloud computing is one of the emerging, promising and pay on demand computing model [1][2][3][29][30][31]that provide configurable computing infrastructures to end users in an on-demand fashion that comes up with various benefits, such as ubiquitous and flexible access to data, resource scaling and availability [4][5][6]. Conventionally, IoT devices like smart phones, home automation devices, medical infrastructures, and other IoT devices remotely communicate with cloud for task processing.…”

Section: Introductionmentioning

confidence: 99%

A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment

et al. 2021

Self Cite

View full text Add to dashboard Cite

Cloud high latency limitation has necessitated the introduction of Fog computing paradigm that extends computing infrastructures in the cloud data centers to the edge network. Extended cloud resources provide processing, storage and network services to time sensitive request associated to the Internet of Things (IoT) services in network edge. The rapid increase in adoption of IoT devices, variations in user requirements, limited processing and storage capacity of fog resources and problem of fog resources over saturation has made provisioning and allotment of computing resources in fog environment a formidable task. Satisfying application and request deadline is the most substantial challenge compared to other dynamic variations in parameters of client requirements. To curtail these issues, the integrated fog-cloud computing environment and efficient resource selection method is highly required. This paper proposed an agent based dynamic resource allocation that employs the use of host agent to analyze the QoSrequirements of application and request and select a suitable execution layer. The host agent forwards the application request to a layer agent which is responsible for the allocation of best resource that satisfies the requirement of the application request. Host agent and layers agents maintains resource information tables for matching of task and computing resources. CloudSim toolkit functionalities were extended to simulate a realistic fog environment where the proposed method is evaluated. The experimental results proved that the proposed method performs better in terms of processing time, latency and percentage QoS delivery. HIGHLIGHTS The distance between the cloud infrastructure and the edge IoT devices makes the cloud not too competent for some IoT applications, especially the sensitive ones To minimize the latency in the cloud and ensure prompt response to user requests, Fog computing, which extends the cloud services to edge network was introduced The proliferation in adoption of IoT devices and fog resource limitations has made resource scheduling in fog computing a tedious one GRAPHICAL ABSTRACT

show abstract

Survey on Optimization of Resource Scheduling in Cloud Platforms

Kasireddi

Anitha

2022

Applications of Computational Methods in Manufacturing and Product Design

View full text Add to dashboard Cite

Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment

Cited by 10 publications

References 32 publications

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

Adaptive firefly algorithm for resource allocation and modified advanced encryption standard algorithm for hypervisor attack detection on cloud computing

A Multi Agent Based Dynamic Resource Allocation in Fog-Cloud Computing Environment

Survey on Optimization of Resource Scheduling in Cloud Platforms

Contact Info

Product

Resources

About