Tasks scheduling technique using league championship algorithm for makespan minimization in IaaS cloud

Makespan minimization in tasks scheduling of infrastructure as a service (IaaS) cloud is an NP-hard problem. A number of techniques had been used in the past to optimize the makespan time of scheduled tasks in IaaS cloud, which is propotional to the execution cost billed to customers. In this paper, we proposed a League Championship Algorithm (LCA) based makespan time minimization scheduling technique in IaaS cloud. The LCA is a sports-inspired population based algorithmic framework for global optimization over a continuous search space. Three other existing algorithms that is, First Come First Served (FCFS), Last Job First (LJF) and Best Effort First (BEF) were used to evaluate the performance of the proposed algorithm. All algorithms under consideration assumed to be non-preemptive. The results obtained shows that, the LCA scheduling technique perform moderately better than the other algorithms in minimizing the makespan time of scheduled tasks in IaaS cloud

Energy Aware Routing Protocol for Energy Constrained Mobile Ad-hoc Networks

Dynamic topology change and decentralized makes routing a challenging task in mobile ad hoc network. Energy efficient routing is the most challenging task in MANET due to limited energy of mobile nodes. Limited power of batteries typically use in MANET, and this is not easy to change or replace while running communication. Network disorder can occur for many factors but in middle of these factors deficiency of energy is the most significant one for causing broken links and early partition of the network. Evenly distribution of power between nodes could enhance the lifetime of the network, which leads to improving overall network transmission and minimizes the connection request. To discourse this issue, we propose an Energy Aware Routing Protocol (EARP) which considers node energy in route searching process and chooses nodes with higher energy levels. The EARP aim is to establish the shortest route from source to destination that contains energy efficient nodes. The performance of EARP is evaluated in terms of packet delivery ratio, network lifetime, end-to-end delay and throughput. Results of simulation done by using NS2 network simulator shows that EARP can achieve both high throughput and delivery ratio, whereas increase network lifetime and decreases end-to-end delay

Recent advances in mobile touch screen security authentication methods: a systematic literature review

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user’s stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquir

Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments

Whale optimization algorithm (WOA) is a recently developed swarm-based meta-heuristic algorithm that is based on the bubble-net hunting maneuver technique—of humpback whales—for solving the complex optimization problems. It has been widely accepted swarm intelligence technique in various engineering fields due to its simple structure, less required operator, fast convergence speed and better balancing capability between exploration and exploitation phases. Owing to its optimal performance and efficiency, the applications of the algorithm have extensively been utilized in multidisciplinary fields in the recent past. This paper investigates further into WOA of its applications, modifications, and hybridizations across various fields of engineering. The description of the strengths, weaknesses and opportunities to support future research are also explored. The Systematic Literature Review is opted as a method to disseminate the findings and gap from the existing literature. The authors select eighty-two (82) articles as a primary studies out of nine hundred and thirty-nine (939) articles between 2016 and 2020. As per our result, WOA-based techniques are applied in 5 fields and 17 subfields of various engineering domains. 61% work has been found on modification, 27% on hybridization and 12% on multi-objective variants of WOA techniques. The growing research trend on WOA is expected to continue into the future. The review presented in the paper has the potential to motivate expert researchers to propose more novel WOA-based algorithms, and it can serve as an initial reading material for a novice researcher

Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment

Resource scheduling is a procedure for the distribution of resources over time to perform a required task and a decision making process in cloud computing. Optimal resource scheduling is a great challenge and considered to be an NP-hard problem due to the fluctuating demand of cloud users and dynamic nature of resources. In this paper, we formulate a new hybrid gradient descent cuckoo search (HGDCS) algorithm based on gradient descent (GD) approach and cuckoo search (CS) algorithm for optimizing and resolving the problems related to resource scheduling in Infrastructure as a Service (IaaS) cloud computing. This work compares the makespan, throughput, load balancing and performance improvement rate of existing meta-heuristic algorithms with proposed HGDCS algorithm applicable for cloud computing. In comparison with existing meta-heuristic algorithms, proposed HGDCS algorithm performs well for almost in both cases (Case-I and Case-II) with all selected datasets and workload archives. HGDCS algorithm is comparatively and statistically more effective than ACO, ABC, GA, LCA, PSO, SA and original CS algorithms in term of problem solving ability in accordance with results obtained from simulation and statistical analysis

Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm

In cloud computing, resources are dynamically provisioned and delivered to users in a transparent manner automatically on-demand. Task execution failure is no longer accidental but a common characteristic of cloud computing environment. In recent times, a number of intelligent scheduling techniques have been used to address task scheduling issues in cloud without much attention to fault tolerance. In this research article, we proposed a dynamic clustering league championship algorithm (DCLCA) scheduling technique for fault tolerance awareness to address cloud task execution which would reflect on the current available resources and reduce the untimely failure of autonomous tasks. Experimental results show that our proposed technique produces remarkable fault reduction in task failure as measured in terms of failure rate. It also shows that the DCLCA outperformed the MTCT, MAXMIN, ant colony optimization and genetic algorithm-based NSGA-II by producing lower makespan with improvement of 57.8, 53.6, 24.3 and 13.4 % in the first scenario and 60.0, 38.9, 31.5 and 31.2 % in the second scenario, respectively. Considering the experimental results, DCLCA provides better quality fault tolerance aware scheduling that will help to improve the overall performance of the cloud environment

Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds

Scheduling problems in cloud computing environment are mostly influenced by multi-objective optimization but frequently deal with using single-objective algorithms. The algorithms need to resolve multi-objective problems which are significantly different from the procedure or techniques used for single-objective optimizations. For this purpose, meta-heuristic algorithms always show their strength to deal with multi-objective optimization problems. In this research article, we present an innovative Multi-objective Cuckoo Search Optimization (MOCSO) algorithm for dealing with the resource scheduling problem in cloud computing. The main objective of resource scheduling problem is to reduce the cloud user cost and enhance the performance by minimizing makespan time, which helps to increase the revenue or profit for cloud providers with maximum resource utilization. Therefore, the proposed MOCSO algorithm is a new method for solving multi-objective resource scheduling problems in IaaS cloud computing environment. Moreover, the effects of the proposed algorithm are analyzed and evaluated by comparison with state-of-the-art multi-objective resource scheduling algorithms using simulation framework. Results obtained from simulation show that the proposed MOSCO algorithm performs better than MOACO, MOGA, MOMM and MOPSO, and balance multiple objectives in terms of expected time to completion and expected cost to completion matrices for resource scheduling in IaaS cloud computing environment

Experimental Parameters Setting of Cloudsim.

<p>Experimental Parameters Setting of Cloudsim.</p

Global League Championship Algorithm (GBLCA).

<p>Global League Championship Algorithm (GBLCA).</p