Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

Subramanian, Murali; Manikandan, Narayanan; Bhasker, B.; Gnanavel, S.; Rahman, Habibur; Reddy, Chandra

doi:10.1155/2022/4525220

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…Equation (4) states that the candidate of the second guided search is generated based on the movement of the global best solution avoiding the corresponding agent. Equation (5) states that an agent is randomly selected among the population. Equation (6) states that the candidate of the third guided search is generated based on the movement of the corresponding agent relative to a randomly selected agent.…”

Section: Proposed Modelmentioning

confidence: 99%

“…In the minimization, the optimal global solution is the solution with the lowest value. Some experimental parameters in the minimization, such as delay [1], total order completion time [2], idle time [2], tardiness cost and maintenance [3], project duration [4], energy consumption [5], transmission losses [6], and so on. On the other hand, in maximization, the optimal global solution is the solution with the highest value.…”

Section: Introductionmentioning

confidence: 99%

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Three on Three Optimizer: A New Metaheuristic with Three Guided Searches and Three Random Searches

Kusuma¹,

Dinimaharawati²

2023

IJACSA

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This paper presents a new swarm intelligencebased metaheuristic called a three-on-three optimizer (TOTO). This name is chosen based on its novel mechanism in adopting multiple searches into a single metaheuristic. These multiple searches consist of three guided searches and three random searches. These three guided searches are searching toward the global best solution, searching for the global best solution to avoid the corresponding agent, and searching based on the interaction between the corresponding agent and a randomly selected agent. The three random searches are the local search of the corresponding agent, the local search of the global best solution, and the global search within the entire search space. TOTO is challenged to solve the classic 23 functions as a theoretical optimization problem and the portfolio optimization problem as a real-world optimization problem. There are 13 bank stocks from Kompas 100 index that should be optimized. The result indicates that TOTO performs well in solving the classic 23 functions. TOTO can find the global optimal solution of eleven functions. TOTO is superior to five new metaheuristics in solving 17 functions. These metaheuristics are grey wolf optimizer (GWO), marine predator algorithm (MPA), mixed leader-based optimizer (MLBO), golden search optimizer (GSO), and guided pelican algorithm (GPA). TOTO is better than GWO, MPA, MLBO, GSO, and GPA in solving 22, 21, 21, 19, and 15 functions, respectively. It means TOTO is powerful to solve highdimension unimodal, multimodal, and fixed-dimension multimodal problems. TOTO performs as the second-best metaheuristic in solving a portfolio optimization problem.

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Section: Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three on Three Optimizer: A New Metaheuristic with Three Guided Searches and Three Random Searches

Kusuma¹,

Dinimaharawati²

2023

IJACSA

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“…Most of the organization is meant to use the storage service that cloud service providers provide to move the data; therefore, it is necessary to secure the uploaded data to the cloud storage [25]. According to a study [27], electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters [27].…”

Section: Dong Et Al (mentioning

confidence: 99%

“…According to a study [27], electro search and the ant colony optimization algorithm are combined in the proposed method. Compared to HESGA, HPSOGA, AC-PSO, and PSO-COGENT algorithms, the created HES-ACO algorithm was simulated at CloudSim and found to optimize all parameters [27].…”

Section: Dong Et Al (mentioning

confidence: 99%

Security Performance Analysis during Side-Channel Attack Using Novel Cryptography Algorithm for VM Cloud

Gnanavel

Sathianesan

K.E³

et al. 2023

Preprint

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Cloud computing is a relatively new technology that allows multiple users to share computational resources and data over the internet. It supports multi-tenant to meet the needs of users to access the resources at the same time, it improves the profitability of the cloud service provider. The Virtual Machine (VM) operates on the same core device that is the focus of any aberrant or malicious attack. These attacks may include data leakage, privacy breaches, and unauthorized access to cloud resources. The outsider attacks may be server spoofing, stolen verifier, password guessing, impersonation, and replay attacks at the cloud’s network layer. The insider attacks may be side-channel attacks to gain access to shared resources. A Self-Adaptive Honey Encryption (SAHE) method for stronger multi-level authentication in a cloud environment the hypervisor layer monitors the running cloud memory, CUP, communication port, and network infrastructure. Introduce the classifier mode to identify the attack and VM risk level (high, medium, and low) to improve the VM performance and attack identification. A rule-based mechanism to detect a side-channel attack by monitoring cache data access. Processes running on the VM can use the cache of another virtual machine, creating a cache conflict in the system. The cache-based attack on the Physical Machine (PM) creates the cache contention to mitigate the attacker’s data. We propose a novel rule-based authentication with the swam optimization method for shifting the resource from an optimized VM into a physical machine. The migration method for optimal PM makes the VM free from cache contention, and it also mitigates the cache side-channel attack in the cloud environment.

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“…An ineffective task scheduler will reduce the quality of service of the cloud service, and increase the makespan and energy consumption, which also leads to SLA violation, affecting both cloud providers and consumers. Many authors have solved task scheduling problems in cloud computing using metaheuristic algorithms, e.g., PSO [2], GA [3], and ACO [4]. All these are metaheuristic approaches, and among these approaches, some of them work based on swarm updating, pheromone updating, and chromosome updating techniques.…”

Section: Introductionmentioning

confidence: 99%

Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization

Mangalampalli

Swain

Chakrabarti

et al. 2023

Sensors

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Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.

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Hybrid Electro Search with Ant Colony Optimization Algorithm for Task Scheduling in a Sensor Cloud Environment for Agriculture Irrigation Control System

Cited by 9 publications

References 37 publications

Three on Three Optimizer: A New Metaheuristic with Three Guided Searches and Three Random Searches

Three on Three Optimizer: A New Metaheuristic with Three Guided Searches and Three Random Searches

Security Performance Analysis during Side-Channel Attack Using Novel Cryptography Algorithm for VM Cloud

Prioritized Task-Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization

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