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This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [ABSTRACT FROM AUTHOR]

The performance growth in processors has been continuing toward increasing the number of processing cores on the chip and scaling the feature size of transistors. However, in the nanoera, side effects of the scaling, such as induced heterogeneities in the performance, power, and soft error rate of identically designed cores, prevent the potential performance from being fully utilized. In this paper, we harness the mentioned side effects in shared-memory multicore processors with unknown workloads by a dynamic heuristic scheduling algorithm called HDSAP. HDSAP aims to maximize performance, i.e., the average response time, under power and reliability constraints in presence of induced heterogeneities. In this regard, we use a mathematical model to quantify task to core assignments based on performance variation. We also consider the variation in power to change selected cores when the power constraint is missed. To meet the reliability constraint, we use N-modular redundancy while being aware of the variation in the soft error rate of cores to prevent under/over reliability estimation. To evaluate HDSAP, we run SPLASH benchmark suite on Sniper and MACPat simulators. As a result, the response time of HDSAP reduces by 6%, 8%, and 25% in comparison with similar algorithms under the same power and reliability constraints. [ABSTRACT FROM AUTHOR]

The two-dimension strip packing problem (2DSPP) is an NP-hard combinatorial optimization problem, and it has a wide range of applications. The order in which the rectangles are placed in the strip is the key to solving the 2DSPP. In this paper, the corner increment multi-level scoring strategy and the two-step selection strategy are proposed to make the selection of rectangles more reasonable. The hierarchical construction strategy is used to improve the constructive heuristic algorithm to expand the search space of the rectangle selection. Based on this, this paper proposes a multi-strategy rectangle selection algorithm TSMSA for solving 2DSPP. Comparing experiments with 7 excellent algorithms for solving 2DSPP on 737 instances of the benchmark dataset shows that the instance number of the optimal solution obtained by TSMSA is 1.05~2.9 times that of the other 7 algorithms. [ABSTRACT FROM AUTHOR]

The variety of pricing models offered by cloud service providers and the availability of a wide diversity of computing resources has increased the popularity of this paradigm for scientific applications. Such a scalable platform can be an ideal option for the execution of loosely coupled parallel applications, such as scientific workflows. Scientific workflows are regarded as one of the most important elements in different scientific fields in which a complex application may be divided into several dependent tasks. Given that the cost of leasing multicore VMs on the cloud will rise with an increase in the number of processing cores, an efficient scheduling algorithm focusing on utilizing multicore resources can significantly reduce execution costs. As an extension of its authors' previous research, the current paper proposes a heuristic scheduling algorithm, the Cluster Dividing Algorithm that concentrates on expanding the utilization of multicore resources to reduce execution costs while also meeting the user-defined deadline. To increase resource utilization, the proposed scheduling employs different techniques, such as task clustering, directed graph leveling, and task duplicating. The experimental results reveal that the presented algorithm leads to lower execution costs while complying with the deadline. [ABSTRACT FROM AUTHOR]

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization, which has fast convergence, a simple structure, and strong search ability. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this problem, the paper proposes a hybrid approach to improving its local search mechanism. The local search strategy of curve decreasing and speed weight is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. The performance of the improved bat algorithm has better global optimization ability and higher convergence accuracy than the standard bat algorithm. [ABSTRACT FROM AUTHOR]

Multilevel thresholding image segmentation has always been a popular issue and attracted the attention of many researchers in recent years since it has many applications. However, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To address this problem, in this paper, the optimal thresholds for multilevel thresholding image segmentation are obtained by maximizing the Rényi entropy using the Lévy flight firefly algorithm (FAFA), in which an adaptive parameter strategy based on Lévy flight is used to improve the performance of the standard firefly algorithm (FA). The performance of the proposed algorithm has been evaluated using several benchmark images and has been compared with five different heuristic algorithms. The experimental results showed that the proposed algorithm prevailed over the other five competitive algorithms in terms of the objective function value, image quality measures, and computational efficiency. [ABSTRACT FROM AUTHOR]

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement's performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications "E-PRWA" in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing. [ABSTRACT FROM AUTHOR]

Travelling salesman problem (TSP) is a graph problem that has been widely used in many applications, especially for transportation and logistics. Because TSP is a NP hard problem, minimizing the complexity of TSP algorithms is an important problem. Many heuristic algorithms have been proposed to compute the TSP tours of a given static graph. But limited studies have been done on time-evolving graph (TEG) where the graph can change over time due to update events, such as weight changes on edges or vertices. It is a more challenging problem because the speed of TSP computations must be high enough to catch up the graph update frequency. In this paper, we make the very first attempt to minimize the computation time of solving TSP on time-evolving graphs. By exploring parallel computing power and reusing previous computing results, we proposed a distributed and incremental TSP algorithm which can be implemented on vertex-centric parallel graph computing frameworks to efficiently find TSP tours on large changing graphs. Our incremental algorithm can maintain shortest TSP tour with minimum amount of recomputation. Through our experimental evaluation, we have shown incremental TSP algorithm is 98% faster than distributed algorithm. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation has always been a popular issue and has attracted much attention. Traditional exhaustive search methods take considerable time to solve multilevel thresholding problems. However, heuristic search algorithms have potential advantages in terms of solving such multilevel thresholding problems. Based on this idea, in this paper, a novel adaptive gravitational search algorithm (AGSA) is proposed to solve the optimal multilevel image thresholding problem; this algorithm is more efficient than the traditional exhaustive search method for grayscale image segmentation. In the AGSA, an adaptive parameter optimization strategy is used to tune the gravitational constant and the inertia weight. To verify the performance of the proposed algorithm, a series of classic test images are used to perform several experiments. In addition, the standard GSA and some optimization algorithms are compared with the proposed algorithm. The experimental results show that the proposed algorithm is obviously better than the other six algorithms. These promising results suggest that the AGSA is more suitable than existing methods for multilevel image thresholding. [ABSTRACT FROM AUTHOR]

Multilevel thresholding image segmentation is an important technique, which has attracted much attention in recent years. The conventional exhaustive search method for image segmentation is efficient for bilevel thresholding. However, they are time expensive when dealing with multilevel thresholding image segmentation. To better tackle this problem, an improved cuckoo search algorithm (ICS) is proposed to search for the optimal multilevel thresholding in this paper, and Otsu is considered as its objective function. In the ICS, two modifications are used to improve the standard cuckoo search algorithm. First, a parameter adaptation strategy is utilized to improve exploration performance. Second, a dynamic weighted random-walk method is adopted to enhance the local search efficiency. A total of six benchmark test images are used to perform the experiments, and seven state-of-the-art metaheuristic algorithms are introduced to compare with the ICS. A series of measure indexes such as objective function value and standard deviation, PSNR, FSIM, and SSIM as well as the Wilcoxon rank sum and convergence performance are performed in the experiments; the experimental results show that the proposed algorithm is superior to other seven well-known heuristic algorithms. [ABSTRACT FROM AUTHOR]