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Optimizing multi-dimensional resource utilization is a critical research area in distributed computing, particularly in cloud computing, where various heterogeneous resources are integrated to offer a wide range of services. Addressing this issue necessitates the simultaneous consideration of multiple resource bottlenecks. This paper presents a new solution, called the Multi-Population Growth Genetic Algorithm (MPGGA), which consists of a central management unit responsible for executing information interaction and growth quota reallocation, and multiple population evolution executors to perform crossover and regeneration within each population. The proposed MPGGA combines elite sharing and priority support for the weaker population (ESPW), resulting in better convergence and optimality than other combinations of strategies. This outcome is corroborated by extensive ablation experiments on various strategies. Furthermore, the experimental results for minimizing the maximum utilization of resources in each dimension indicate that MPGGA-ESPW outperforms other popular algorithms, such as GHW-NSGA II (1.363x), GHW-MOEA/D (1.339x), NSGA II (1.948x), and MOEA/D (2.151x) in terms of convergence speed. For energy consumption-related optimization problems, the experimental results demonstrate that the adaptability of a single algorithm in MPGGA family is limited by the algorithm of growth route, while also showing that the MPGGA framework is flexible to allow various algorithms as its growth route to adapt to various scenarios. • Multi-Population GGA to optimize multi-dimensional resources utilization in cloud. • Elite sharing information interaction to exchange solutions between populations. • Partial growth and priority support the worse growth quota reallocation strategy. [ABSTRACT FROM AUTHOR]

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure, and be charged on pay-per-use basis. However, cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements violations. So as to achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be nondeterministic polynomial time (NP)-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to hosts in infrastructure clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated, and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements. [ABSTRACT FROM AUTHOR]

Multi-Dimensional Resources Scheduling Problem (MDRSP, usually a multi-objective optimization problem) has attracted focus in the management of large-scale cloud computing systems as the collaborative operation of various devices in the cloud affects resource utilization and energy consumption. Effective management of the cloud requires a higher performance method to solve MDRSP. Considering the complex coupling between multi-dimensional resources and focusing on virtual machines allocation, we propose GGA-HLSA-RW (GHW, a novel family of genetic algorithms) to optimize the utilization and energy consumption of the cloud. In GGA-HLSA-RW, we add a growth stage to the genetic algorithm and construct a Growable Genetic Algorithm (GGA) using the Heuristic-based Local Search Algorithm (HLSA) with Random multi-Weights (RW) as the growth route. Based on the GHW, we propose GHW-NSGA II and GHW-MOEA/D by applying the sorting strategies and population regeneration mechanism of NSGA II and MOEA/D. To evaluate the performance of GHW, we carry out extensive experiments on the simulation dataset and AzureTraceforPacking2020 for the problems of minimizing the maximum utilization rate of resources for each dimension and minimizing total energy consumption. Experiment results demonstrate the advantages of growth strategy and dimensionality reduction strategy of GHW, as well as validate the applicability and optimality of GHW in realistic cloud computing. The experiments also demonstrate our proposed GHW-NSGA II and GHW-MOEA/D have better convergence rates and optimality than state-of-the-art NSGA II and MOEA/D. • For multi-dimensional resource scheduling of Cloud, Growable GA is better than GA. • GHW: Heuristic-based local search as the growth of GGA. • GHW-NSGA II and GHW-MOEA/D outperform NSGA II and MOEA/D. [ABSTRACT FROM AUTHOR]

Large-scale MapReduce clusters that routinely process big data bring challenges to the cloud computing. One of the key challenges is to reduce the response time of these MapReduce clusters by minimizing their makespans. It is observed that the order in which these jobs are executed can have a significant impact on their overall makespans and resource utilization. In this work, we consider a scheduling model for multiple MapReduce jobs. The goal is to design a job scheduler that minimizes the makespan of such a set of MapReduce jobs. We exploit classical Johnson model and propose a novel framework HScheduler, which combines features of both classical Johnson's algorithm and MapReduce to minimize the makespan for both offline and online jobs. Our Offline HScheduler reaches the theoretical lower bound (optimum) and Online HScheduler is 2-competitive which is the best-known constant ratio for minimizing the makespan. Through extensive real data tests, we find that HScheduler has better performance than the best-known approach by 10.6-11.7 % on average for offline scheduling and 8-10 % on average for online scheduling. The HScheduler can be applied to improve responsive time, throughput and energy efficiency in cloud computing. [ABSTRACT FROM AUTHOR]

Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be controlled or predicted. Performance evaluations of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations are difficult. Therefore, simulators are developed. To understand and apply better the state-of-the-art of Cloud computing simulators, and to improve them, we study four known open-source simulators. They are compared in terms of architecture, modeling elements, simulation process, performance metrics and scalability in performance. Finally, a few challenging issues as future research trends are outlined. [ABSTRACT FROM AUTHOR]

Cloud computing is an emerging computing paradigm that can offer unprecedented scalability and resources on demand, and is getting more and more adoption in the science community, while scientific workflow management systems provide essential support such as management of data and task dependencies, job scheduling and execution, provenance tracking, etc., to scientific computing. As we are entering into a “big data” era, it is imperative to migrate scientific workflow management systems into the cloud to manage the ever increasing data scale and analysis complexity. We propose a reference service framework for integrating scientific workflow management systems into various cloud platforms, which consists of eight major components, including Cloud Workflow Management Service, Cloud Resource Manager, etc., and six interfaces between them. We also present a reference framework for the implementation of Cloud Resource Manager, which is responsible for the provisioning and management of virtual resources in the cloud. We discuss our implementation of the framework by integrating the Swift scientific workflow management system with theOpenNebula and Eucalyptus cloud platforms, and demonstrate the capability of the solution using a NASA MODIS image processing workflow and a production deployment on the Science@Guoshi network with support for the Montage image mosaic workflow. [ABSTRACT FROM PUBLISHER]

Our paper considers the following fundamental scheduling problem. There are n deterministic jobs to be scheduled offline on multiple identical machines, which have bounded capacities. Each job is associated with a start-time, an end-time, a process time, and demand for machine capacity. The goal is to schedule all of the jobs non-preemptively in their start-time-end-time windows, subject to machine capacity constraints such that the total busy time of the machines is minimized. We refer to this problem as minimizing the total busy time for the scheduling of multiple identical machines (MinTBT). This problem has important applications in power-aware scheduling for Cloud computing, optical network design, customer service systems, and other related areas. Scheduling to minimize busy times is already NP-hard in the special case where all jobs have the same process time and can be scheduled in a fixed time interval. One best-known result for this problem is a 5-approximation algorithm for special instances using first-fit-decreasing algorithm. In this paper, we propose and prove a 3-approximation algorithm, modified first-fit-decreasing-earliest for the general case and obtain more results for special cases. We then show how our results are applied in cloud computing to improve the energy efficiency. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Resource scheduling in infrastructure as a service (IaaS) is one of the keys for large-scale Cloud applications. Extensive research on all issues in real environment is extremely difficult because it requires developers to consider network infrastructure and the environment, which may be beyond the control. In addition, the network conditions cannot be predicted or controlled. Therefore, performance evaluation of workload models and Cloud provisioning algorithms in a repeatable manner under different configurations and requirements is difficult. There is still lack of tools that enable developers to compare different resource scheduling algorithms in IaaS regarding both computing servers and user workloads. To fill this gap in tools for evaluation and modeling of Cloud environments and applications, we propose CloudSched. CloudSched can help developers identify and explore appropriate solutions considering different resource scheduling algorithms. Unlike traditional scheduling algorithms considering only one factor such as CPU, which can cause hotspots or bottlenecks in many cases, CloudSched treats multidimensional resource such as CPU, memory and network bandwidth integrated for both physical machines and virtual machines (VMs) for different scheduling objectives (algorithms). In this paper, two existing simulation systems at application level for Cloud computing are studied, a novel lightweight simulation system is proposed for real-time VM scheduling in Cloud data centers, and results by applying the proposed simulation system are analyzed and discussed. [ABSTRACT FROM PUBLISHER]

This paper considers online energy-efficient scheduling of virtual machines (VMs) for Cloud data centers. Each request is associated with a start-time, an end-time, a processing time and a capacity demand from a Physical Machine (PM). The goal is to schedule all of the requests non-preemptively in their start-time-end-time windows, subjecting to PM capacity constraints, such that the total busy time of all used PMs is minimized (called MinTBT-ON for abbreviation). This problem is a fundamental scheduling problem for parallel jobs allocation on multiple machines; it has important applications in power-aware scheduling in cloud computing, optical network design, customer service systems, and other related areas. Offline scheduling to minimize busy time is NP-hard already in the special case where all jobs have the same processing time and can be scheduled in a fixed time interval. One best-known result for MinTBT-ON problem is a g-competitive algorithm for general instances and unit-size jobs using First-Fit algorithm where g is the total capacity of a machine. In this paper, a $(1+\frac{g-2}{k}-\frac{g-1}{k^{2}})$ -competitive algorithm, Dynamic Bipartition-First-Fit (BFF) is proposed and proved for general case, where k is the ratio of the length of the longest interval over the length of the second longest interval for k>1 and g≥2. More results in general and special cases are obtained to improve the best-known bounds. [ABSTRACT FROM AUTHOR]