Mutation and dynamic objective-based farmland fertility algorithm for workflow scheduling in the cloud

scientific workflows are suitable models in many applications for recognition and execution of tasks in parallel, especially in the Cloud. Different aspects of resource provisioning and the resource pricing model in the cloud environment cause the scheduling problem of workflow very complex. One of the main aims of the scheduling algorithms is to satisfy the users’ different quality of service requirements. Communication delay between tasks is an important affecting factor in optimal scheduling of workflows. It can also highly increase the cost of workflow scheduling if proper actions are not taken. To solve this problem, we propose a tasks duplication-based list scheduling algorithm called Communication-Critical Task Duplication (CCTD). We first define the concept of communication critical task (CCT) for a workflow. Then, by presenting a ranking-based approach, we identify communication critical tasks in a workflow as well as duplicating candidates. Task duplication in idle time slots of leased virtual machines which their children tasks are mapped to. This idea, while eliminating the cost and time of data transfer between parent-child tasks, reduces the time of execution of tasks and effectively uses leased time intervals of resources. According to the proposed scheduling algorithm, a new heuristic method has been proposed for the budget distribution. This method distribute overall budget to tasks in proportional to the workload and duplication rank of each task. The proposed algorithm was evaluated and verified using four well-known scientific workflows. The simulation results show that the CCTD algorithm, while respecting the user budget constraint, reduces the workflow overall completion time.

This paper presents a shuffled frog leaping algorithm (SFLA) based solution to solve the View Selection Problem (VSP) subject to dual constraints, which is often used to accelerate data warehouse queries. Since VSP is both discrete and constrained, a greedy-repaired strategy under dual constraints is proposed to handle unfeasible solutions. This proposed solution also profits from a mutation strategy in order to improve the quality of solutions, particularly to avoid being trapped in local optima. Experimental results show that under different constraints combinations, SFLA is able to find a near-optimal feasible solution, with maximum error less than 1%. Comparisons with GA and PSO show that SFLA has better solution quality and faster convergence rate, and also scales with the problem size.

Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. BA has been mostly used to resolve diverse kind of optimization problems and one of major issue faced by BA is frequently captured in local optima meanwhile handling the complex real world problems. Many authors improved the standard BA with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This paper aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of BA. It has been tried to provide guidelines for the researchers who are active in the area of BA and its mutation strategies. The objective of this study is divided in two sections: primarily to display the improvement of BA with mutation strategies that may enhance the performance of standard BA up to great extent and secondly, to motivate the researchers and developers for using BA to solve the complex real world problems. This study presents a comprehensive survey of the various BA algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the BA algorithm in detail for the researcher.

The Particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. With flexibility for numerical experimentation, the PSO algorithm has been mostly used to resolve diverse kind of optimization problems. The PSO algorithm is frequently captured in local optima meanwhile handling the complex real-world problems. Many authors improved the standard PSO algorithm with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This article aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of the PSO algorithm. It has tried to provide guidelines for the researchers who are active in the area of the PSO algorithm and its mutation strategies. The objective of this study is divided into two sections: primarily to display the improvement of the PSO algorithm with mutation strategies that may enhance the performance of the standard PSO algorithm to great extent and secondly, to motivate researchers and developers to use the PSO algorithm to solve the complex real-world problems. This study presents a comprehensive survey of the various PSO algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the PSO algorithm in detail for researchers.

This paper proposes a hybrid chaotic biogeography-based optimization (HCBBO) for solving the sequence dependent setup times flowshop scheduling problem with the objective of minimizing the total weighted tardiness. First of all, a largest-order-value rule is employed to transform continuous vectors into discrete job permutations. Second, the chaotic theory and the problem-specific Nawaz-Enscore-Ham heuristic are applied to compose the initial population with the property of intensification and diversification. Third, an improved biogeography-based optimization is introduced to improve the global search ability by designing new migration and mutation schemes. Meanwhile, a further local search is proposed and embedded in HCBBO to enhance the quality of the elite habitats. In addition, an effective perturbation is applied to avoid the solutions getting trapped in the local optima. Computation comparison experiments of seven benchmark algorithms on the Taillard benchmark problems are provided to verify the efficiency of the proposed algorithm. From the experiment results, it can conclude that HCBBO beats other compared algorithms effectively with higher quality and robustness solutions.

In heterogeneous environments, dynamic scheduling algorithms are a powerful tool towards performance improvement of scientific applications via load balancing. However, these scheduling techniques employ heuristics that require prior knowledge about workload via profiling resulting in higher overhead as problem sizes and number of processors increase. In addition, load imbalance may appear only at run-time, making profiling work tedious and sometimes even obsolete. Recently, the integration of dynamic loop scheduling algorithms into a number of scientific applications has been proven effective. This paper reports on performance improvements obtained by integrating the Adaptive Weighted Factoring, a recently proposed dynamic loop scheduling technique that addresses these concerns, into two scientific applications: computational field simulation on unstructured grids, and N-Body simulations. Reported experimental results confirm the benefits of using this methodology, and emphasize its high potential for future integration into other scientific applications that exhibit substantial performance degradation due to load imbalance.

A real-time application is specified as a set of nonpreemptive tasks with precedence and deadline constraints. An offline algorithm for multiprocessor scheduling of nonpreemptive tasks with precedence and deadline constraints is proposed. Since this problem is known to be NP-hard, it has been traditionally solved by using list-based scheduling heuristics such as the minimum-laxity-first algorithm. In contrast, the authors propose a genetic algorithm based approach for solving this problem. Unlike the traditional scheduling algorithms, which do not perform well when there is considerable communication between the tasks, the proposed approach performs better as the communication requirements become more stringent. Results of an experimental evaluation comparing the proposed approach with the minimum-laxity-first heuristic are also presented. >

Cloud computing, a novel and promising methodology in the distributed computing domain, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. These workflow applications have a constraint that each of them must completed within the limited time (deadline constraint). Therefore, scheduling a workflow with deadline constraints is increasingly becoming a crucial research issue. However, many analytical reviews on scheduling problems reveal that existing solutions fail to provide cost-effective solutions and they do not consider the parameters like CPU performance variation, delay in acquisition and termination of Virtual Machines (VMs). This paper presents a Cost-Effective Firefly based Algorithm (CEFA) to solve workflow scheduling problems that can occur in an Infrastructure as a Service (IaaS) platform. The proposed CEFA uses a novel method for problem encoding, population initialization and fitness evaluation with an objective to provide cost-effective and optimized workflow execution within the time limit. The performance of the proposed CEFA is compared with the state-of-the-art algorithms such as IaaS Cloud-Partial Critical Path (IC-PCP), Particle Swarm Optimization (PSO), Robustness-Cost-Time (RCT), Robustness-Time-Cost (RTC), and Regressive Whale Optimization (RWO). Our experimental results demonstrate that the proposed CEFA outperforms current state-of-the-art heuristics with the criteria of achieving the deadline constraint and minimizing the cost of execution.

As a type of distributed computing, volunteer computing (VC) has provided unlimited computing capacity at a low cost in recent decades. The architecture of most volunteer computing platforms (VCPs) is a master–worker model, which defines a master–slave relationship. Therefore, VCPs can be considered asymmetric multiprocessing systems (AMSs). As AMSs, VCPs are very promising for providing computing services for users. Users can submit tasks with deadline constraints to the VCPs. If the tasks are completed within their deadlines, VCPs will obtain the benefits. For this application scenario, this paper proposes a new task assignment problem with the maximum benefits in VCPs for the first time. To address the problem, we first proposed a list-based task assignment (LTA) strategy, and we proved that the LTA strategy could complete the task with a deadline constraint as soon as possible. Then, based on the LTA strategy, we proposed a maximum benefit scheduling (MBS) algorithm, which aimed at maximizing the benefits of VCPs. The MBS algorithm determined the acceptable tasks using a pruning strategy. Finally, the experiment results show that our proposed algorithm is more effective than current algorithms in the aspects of benefits, task acceptance rate and task completion rate.

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.

Evolutionarily rational mutations in structured populations

Metaheuristic optimization procedures such as evolutionary algorithms are usually driven by an objective function that rates the quality of a candidate solution. However, it is not clear in practice whether an objective function adequately rewards intermediate solutions on the path to the global optimum and it may exhibit deceptiveness, epistasis, neutrality, ruggedness, and a lack of causality. In this paper, we introduce the frequency fitness H, subject to minimization, which rates how often solutions with the same objective value have been discovered so far. The ideas behind this method are that good solutions are difficult to find and that if an algorithm gets stuck at a local optimum, the frequency of the objective values of the surrounding solutions will increase over time, which will eventually allow it to leave that region again. We substitute a frequency fitness assignment process (FFA) for the objective function into several different optimization algorithms. We conduct a comprehensive set of experiments: the synthesis of algorithms with genetic programming (GP), the solution of MAX-3SAT problems with genetic algorithms, classification with Memetic Genetic Programming, and numerical optimization with a <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$(1+1)$</tex></formula> Evolution Strategy, to verify the utility of FFA. Given that they have no access to the original objective function at all, it is surprising that for some problems (e.g., the algorithm synthesis task) the FFA-based algorithm variants perform significantly better. However, this cannot be guaranteed for all tested problems. Thus, we also analyze scenarios where algorithms using FFA do not perform better or perform even worse than with the original objective functions.

Particle swarm optimization (PSO) is a new promising evolutionary algorithm for the optimization and search problem. One problem of PSO is its tendency to trap into local optima due to its mechanism in information sharing. This paper proposes a novel hybrid PSO, namely (HPSO) technique by merging both a mutation operator and natural selection to solve the problem of premature convergence. By introducing Cauchy mutation and evolutionary selection strategy based on roulette wheel selection, HPSO could greatly reduce the probability of trapping into local optimum. HPSO is proposed to improve the performance of fragile watermarking based DCT which results in enhancing both the quality of the watermarked image and the extracted watermark. After embedding watermark to the original image in the frequency domain, the conversion of real numbers of the modified coefficients in frequency domain to integer numbers in spatial domain produces some rounding errors problem. This problem results in completely different of the extracted watermark from the embedded watermark. The new developed PSO with evolutionary operators is carried out for correcting the rounding errors by training a translation map used to modify the inverse DCT (IDCT) coefficients from real to integer numbers. The experimental results show the superiority of the proposed algorithm comparing with the standard PSO for improving the performance of DCT fragile watermarking. Besides, it has been shown that the developed PSO is faster in convergence and the obtained results proved to have higher fitness than the other algorithm.

A multi-hierarchy particle swarm optimization-based algorithm for cloud workflow scheduling

List based scheduling algorithms have been proven an optimistic strategy with a shorter response time to generate feasible solutions for the workflow scheduling problem. Data-intensive and computation-intensive workflow applications have different characteristics in terms of the ratio between data transfer time and task execution time. Workflow scheduling algorithms in a cloud-based environment should adequately consider the characteristics of the underlying cloud platform such as the on-demand resource provisioning strategy, the practically unlimited compute capacities, the booting times of virtual machines, the homogeneous network and the pay-as-you-go price model to produce an optimal scheduling solution within the deadline constraint of a given workflow. In this paper, a path based scheduling algorithm, named LPOD, is proposed to find the best workflow schedule solution with minimum monetary cost in a cloud computing environment. A series of case studies have been carefully conducted using synthetic workflows based on DATAVIEW, which is a popular open-source big data workflow management system. The experimental results show that the proposed algorithm is efficient and can generate better workflow schedules than the state-of-the-art algorithms such as IC-PCP and SGX-E2C2D.