An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

Cloud infrastructures are suitable environments for processing large scientific workflows. Nowadays, new challenges are emerging in the field of optimizing workflows such that it can meet user’s service quality requirements. The key to workflow optimization is the scheduling of workflow tasks, which is a famous NP-hard problem. Although several methods have been proposed based on the genetic algorithm for task scheduling in clouds, our proposed method is more efficient than other proposed methods due to the use of new genetic operators as well as modified genetic operators and the use of load balancing routine. Moreover, a solution obtained from a heuristic used as one of the initial population chromosomes and an efficient routine also used for generating the rest of the primary population chromosomes. An adaptive fitness function is used that takes into account both cost and makespan. The algorithm introduced in this paper utilizes a load balancing routine to maximize resources’ efficiency at execution time. The performance of the proposed algorithm is evaluated by comparing the results with state of the art algorithms of this field, and the results indicate that the proposed algorithm has remarkable superiority in comparison to other algorithms and performs task scheduling with the least makespan and cost.

This paper proposes to solve the task scheduling problem in cloud computing by using a load balance aware genetic algorithm LAGA with Min-min and Max-min methods. Task scheduling problems are of great importance in cloud computing, and become especially challenging when taking load balance into account. Our proposed LAGA algorithm has several advantages when solving this kind of problems. Firstly, by introducing the time load balance TLB model to help establish the fitness function with makespan, the algorithm benefits from the ability to find the solution that performs best on load balance among a set of solutions with the same makespan. More importantly, the interaction between makespan and TLB helps the algorithm to minimize makespan in the same time. Secondly, Min-min and Max-min methods are used to produce promising individuals at the beginning of evolution, leading to noticeable improvement of evolution efficiency. We evaluated LAGA on several task scheduling problems and compared with a Min-min, Max-min improved version of genetic algorithm MMGA, which does not use the TLB strategy. The results show that LAGA can obtain very competitive results with good load balancing properties, and outperform MMGA in both makespan and TLB objectives.

This paper discusses a Unified Modelling Language (UML) based formal verification methodology for early error detection in the model-based software development cycle. Our approach proposes a UML-based formal verification process utilising functional and behavioural modelling artifacts of UML. It reinforces these artifacts with formal model transition and property verification. The main contribution is a UML to Labelled Transition System (LTS) Translator application that automatically converts UML Statecharts to formal models. Property specifications are derived from system requirements and corresponding Computational Tree Logic (CTL)/Linear Temporal Logic (LTL) model checking procedure verifies property entailment in LTS. With its ability to verify CTL and LTL specifications, the methodology becomes generic for verifying all types of embedded system behaviours. The steep learning curve associated with formal methods is avoided through the automatic formal model generation and thus reduces the reluctance of using formal methods in software development projects. A case study of an embedded controller used in military applications validates the methodology. It establishes how the methodology finds its use in verifying the correctness and consistency of UML models before implementation.

In this paper we develop an intermittent communication framework for teams of mobile robots. Robots move along the edges of a mobility graph and communicate only when they meet at the vertices of this graph, giving rise to a dynamic communication network. We design distributed controllers for the robots that determine meeting times at the nodes of the mobility graph so that connectivity of the communication network is ensured over time, infinitely often. We show that this requirement can be captured by a global Linear Temporal Logic (LTL) formula that forces robots to meet infinitely often at the meeting points. To generate motion plans that satisfy the LTL expression, we propose a novel technique that approximately decomposes the global LTL formula into local LTL formulas and assigns them to the robots. Since the approximate decomposition of the LTL formula can result in conflicting robot behaviors, we develop a distributed conflict resolution scheme that generates conflict-free motion plans that satisfy the global LTL expression. By appropriately introducing delays in the execution of the generated motion plans we show that the proposed controllers can be executed asynchronously.

In this study, the author presents a new efficient algorithm for translating linear temporal logic (LTL) formulas to Buchi automata, which are used by LTL model checkers. The general idea of this algorithm is to generate Buchi automata from LTL formulas, using the principle of alternating automata and keeping only the positive transitions without generating the intermediate generalised automata. The LTL translation is the heart of any LTL model checker, which affects its performance. The translation performance is measured in addition to its speed and the size of the produced Buchi automaton (number of states and number of transitions), by correctness of produced Buchi automaton and its level of determinism. The author will show that this method is different from the others and it is very competitive with the most efficient translators to date.

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

Ordinary users can build their smart home automation system easily nowadays, but such user-customized systems could be error-prone. Using formal verification to prove the correctness of such systems is necessary. However, to conduct formal proof, formal specifications such as Linear Temporal Logic (LTL) formulas have to be provided, but ordinary users cannot author LTL formulas but only natural language.To address this problem, this paper presents a novel approach that can automatically generate formal LTL specifications from natural language requirements based on domain knowledge and our proposed ambiguity refining techniques. Experimental results show that our approach can achieve a high correctness rate of 95.4% in converting natural language sentences into LTL formulas from 481 requirements of real examples.

Fog-Cloud computing has become a promising platform for executing Internet of Things (IoT) tasks with different requirements. Although the fog environment provides low latency due to its proximity to IoT devices, it suffers from resource constraints. This is vice versa for the cloud environment. Therefore, efficiently utilizing the fog-cloud resources for executing tasks offloaded from IoT devices is a fundamental issue. To cope with this, in this paper, we propose a novel scheduling algorithm in fog-cloud computing named PGA to optimize the multi-objective function that is a weighted sum of overall computation time, energy consumption, and percentage of deadline satisfied tasks (PDST). We take the different requirements of the tasks and the heterogeneous nature of the fog and cloud nodes. We propose a hybrid approach based on prioritizing tasks and a genetic algorithm to find a preferable computing node for each task. The extensive simulations evaluate our proposed algorithm to demonstrate its superiority over the state-of-the-art strategies.

A unified concurrent-composition method to state/event inference and concealment in labeled finite-state automata as discrete-event systems

Cloud computing is a new development in the world of Information Technology (IT) infrastructure and it has brought could of challenges. Task scheduling is one of the main features that allows to be efficient in cloud-computing to guarantee the effectiveness of work with the resources and make the completion time as short as possible. It should, however, be pointed out that the task scheduling in cloud computing belongs to the NP-complete optimization problems. In order to eliminate the difficulties related to task scheduling in cloud computing, a number of algorithms have been presented. One of them is also an original version of the list scheduling scheduling technique, but its implementation is specifically aimed at efficient schedules of tasks execution and load balancing in a cloud environment. This method is based on the Heterogeneous Earliest Finish Time (HEFT) strategy but with some modifications that enhance its efficiency as compared to keeping a similar level of algorithm complexity. I carried out experiments on randomly created Directed Acyclic Graphs (DAGs) with a view to testing the usefulness of the algorithm. The test done on the WorkFlowSim simulator involves testing the real and synthetic workflows. The experiments point out the difference in the effectiveness of the offered mechanism compared to the existing algorithms. As demonstrated by the experiments, the suggested would outperform the current scheduling algorithms by efficiency and use of resources. This algorithm has potential of providing improved results than any prior solutions to the task scheduling problem in computer computing although the task problem is a complex one.

Cloud computing is recently a booming area and has been emerging as a commercial reality in the information technology domain. Cloud computing represents supplement, consumption and delivery model for IT services that are based on internet on pay as per usage basis. The scheduling of the cloud services to the consumers by service providers influences the cost benefit of this computing paradigm. In such a scenario, Tasks should be scheduled efficiently such that the execution cost and time can be reduced. In this paper, we proposed a meta-heuristic based scheduling, which minimizes execution time and execution cost as well. An improved genetic algorithm is developed by merging two existing scheduling algorithms for scheduling tasks taking into consideration their computational complexity and computing capacity of processing elements. Experimental results show that, under the heavy loads, the proposed algorithm exhibits a good performance.

In distributed computing environment, task scheduling is one of the most important factors that affect the overall efficiency of the system. Task scheduling has been proved to be a NPcompleteness problem, and the only way to solve this kind of problem is the method of exhaustion. Genetic algorithm is one of the best algorithms can solve the NP-completeness problem, which has the ability to quickly approach the optimal solution. However, there are still some shortcomings of genetic algorithm, such as the problem of premature convergence. In this paper, an improved genetic algorithm called genetic algorithm with geographical isolation is proposed. It restrains the excessive growth phenomenon of individuals by dividing the population, effectively solves the problem of premature convergence in genetic algorithm. In this paper, several sets of experiments are made to compare the operating efficiency and the final allocation effect of the algorithm and other scheduling algorithms, which prove that the algorithm can improve the efficiency of the algorithm without affecting the quality of the final solution.

Enhancing Cloud Architecture’s Efficiency with an Adaptive Elitism-Based Genetic Algorithm for Task Scheduling

The cloud computing is the new technique used most today for computation and storage. Edge computing is also a new computing domain that gives computation of tasks and storage of data nearer to the data sources. Various applications of edge computing are IOT, healthcare, retail, manufacturing etc. Virtualization technologies have enabled cloud platforms to provide various services such as virtual machines to the users. Task scheduling is the major issues faced in both cloud and edge domains. Since the usage of machines is high, it is difficult to assign tasks manually. So, an efficient and optimized algorithm is required in the cloud as well as the edge environment. Here, a task scheduling is implemented based on Heuristic Optimization Technique to examine the performance measures using cloudsim. Task scheduling and optimization techniques dynamically allocate resources in the cloud environment. Similarly, in edge computing, scheduling the task is performed with different optimization techniques such as random fit, and max fit algorithms. These task scheduling methods are allocated to the VM based on the resources that are available at the VM. These algorithms are implemented, and it increased the VM utilization by 4.82%. The processing time taken is reduced by 2.67% and the percentage of failed tasks is reduced by 3.68%. The experiment result shows the importance of the scheduling in the cloud and edge environment, in terms of processing time, failed task and increase in VM utilization in edge devices.