An Energy-Efficient Scheduling Framework for Cloud Using Learning Automata

Sahoo, Sampa; Sahoo, Bibhudatta; Turuk, Ashok Kumar

doi:10.1109/icccnt.2018.8493692

Cited by 9 publications

(8 citation statements)

References 15 publications

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“…In experiment A, static task scheduling is simulated, and the dynamic adjustment switch of ADATSA is closed. The impact of the number of tasks on static scheduling performance of ADATSA with that of LAEAS [31], PSOS [18] and K8S is compared. The motivation of choosing these comparison algorithms is that LAEAS is a representative scheduling algorithm using learning automata, PSOS is an excellent heuristic scheduling algorithm using non-automata based technique, and Kubernetes (K8S) platform has a strong ability of task scheduling.…”

Section: B Experimenttal Resultsmentioning

confidence: 99%

“…In Ref. [31], a new cloud task scheduling algorithm based on learning automata is proposed to solve dual objective minimization problem of energy consumption and task completion time. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…However, the existing task scheduling algorithms based on learning automata using RP (rewardpenalty) probabilistic updating strategy (such as Ref. [31]) lack of difference in the probability reward granularity of different scheduling actions, which leads to the problems of large randomness and weak directivity when searching for the optimal solution in the solution space, so there is still a large room for improvement.…”

Section: Related Workmentioning

confidence: 99%

“…[39] first proposed an implementation method of RP strategy for energy consumption optimization of sensor scheduling in the field of wireless sensor networks, And Ref. [31] used it to solve the task scheduling problem in container cloud. In this strategy, Formula (1) is used to update the probability of favorable scheduling actions, otherwise Formula (2) is used.…”

Section: B Task Scheduling Oriented Learning Automaton Modelmentioning

confidence: 99%

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A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

Zhu

Huang

et al. 2021

IEEE Access

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With the rapid development of cloud computing and container technology, more and more applications are deployed to the cloud, and the scale of cloud platform is expanding. Due to the large number of container instances running in the platform, complex dependency relationship, fast version iteration and other characteristics, the update of business can often cause the change of the whole cloud resource environment, which triggers the repetitive scheduling problem of related tasks and affects stability of the business. In this paper, we propose a self-adapting task scheduling algorithm (ADATSA) using learning automata to solve these problems. Firstly, we design a learning automata model and objective function for the system on task scheduling problem. Then, we realize an effective reward-penalty mechanism for scheduling actions in combination with the idle state of resources and the running state of tasks in the current environment. Meanwhile, the environment is modeled by cluster, node and task, and the probability of action selected is optimized by scheduling execution, thus enhancing the adaptability to the cloud environment of the scheduling and accelerating convergence. Finally, we construct a framework of task load monitoring with buffer queue to achieve dynamic scheduling based on priority. The experimental part verifies the effectiveness of proposed algorithm with different angles such as resource imbalance degree, resource residual degree and QoS. Compared with other learning automata scheduling models such as LAEAS, non-automata technology based algorithms such as PSOS and K8S scheduling engine, ADATSA shows the better performance of environment adaptability, resource optimization efficiency and QoS in dynamic scheduling. The theoretical analysis was consistent with the experimental results.

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Section: B Experimenttal Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Task Scheduling Oriented Learning Automaton Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

Zhu

Huang

et al. 2021

IEEE Access

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“…Moreover, the paper in particular displayed the improvement of the IoT, not often mentioned in the studies. Elliptic curve cryptography (ECC) is a relatively recent kind of public key encrypting technique that offers greater security per bit than previous kinds of encryption now in use [8]. Encryption and decryption of text using ECC are explained with the mapping technique.…”

Section: Fig 3: Secure Systems In Cloud Infrastructure[6]mentioning

confidence: 99%

Compositional Defence of Application Privacy in Resistant to Physical and Software Attacks in Untrusted Cloud Environment

Chanodiya*¹,

Potey²

2021

IJEAT

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Cloud computing facilities have become an increasing rise in demand for presenting computing sources. Various programs like monetary transactions, fitness care systems, video streaming, IoT programs want on-call for provisioning of cloud sources to guarantee timeliness and excessive availability. A shared culture for data interchange has evolved as a result of the fast progress of networking technology. The data security has become a difficult issue as a result of the vast volume of data that is exchanged via the internet. Thus there is a requirement for security to protect data that is transmitted across an insecure connection. In our proposed work, privacy, confidentiality, availability, integrity, and accountability that contribute to the security of information are ensured by cryptographic technique. This paper also describes an exhaustive study on the usage of hybrid algorithms i.e. the combination of Advanced Encryption Standards (AES) and Elliptical Curve Cryptography (ECC) to protect data leakage and protect the privacy of end-users and SHA256 algorithm to ensure data integrity. This type of hybrid encryption will help not only in concentrating software as well as physical attacks but also provide the model to deal with and prevent the application from these types of attacks; thus making the cloud application system more secure. The implemented web application can be used as a defence system for privacy and security in organizations as well as institutions where members of the organization can share the file or confidential data to other team members or colleagues without data leakage thus providing data privacy and security.

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Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment

et al. 2021

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An Energy-Efficient Scheduling Framework for Cloud Using Learning Automata

Cited by 9 publications

References 15 publications

A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

A Self-Adapting Task Scheduling Algorithm for Container Cloud Using Learning Automata

Compositional Defence of Application Privacy in Resistant to Physical and Software Attacks in Untrusted Cloud Environment

Energy and Makespan Aware Scheduling of Deadline Sensitive Tasks in the Cloud Environment

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