A Cooperative Coevolution Genetic Programming Hyper-Heuristics Approach for On-Line Resource Allocation in Container-Based Clouds

Tan, Boxiong; Ma, Hui; Mei, Yi; Zhang, Mengjie

doi:10.1109/tcc.2020.3026338

Cited by 35 publications

(14 citation statements)

References 57 publications

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“…In solving cloud resources scheduling problems, Zhan et al (2015) presented a comprehensive survey of cloud computing resource scheduling by EC algorithms. Tan et al (2020) combined the genetic programming algorithm with the CC method (CCGP) to deal with the online resource allocation problem in container-based cloud environments. CCGP can deal with two interact allocations (containers to virtual machines (VMs) and VMs to physical machines) separately to improve the performance.…”

Section: Extending Application Fieldmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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Section: Extending Application Fieldmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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“…The authors in [35] propose a Cooperative Coevolution Genetic programming approach to allocate containers to virtual machines, and then virtual machines to physical nodes. In their approach, they consider virtual machine overheads, virtual machine types, and affinity constraints.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Marquez¹,

Mondragon²,

González³

2021

Applied Sciences

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Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.

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“…In addition, Shukla et al [34] proposed a mechanism for migrating running streaming dataflow across VMs. Tan et al [35] proposed a Cooperative Coevolution Genetic Programming (CCGP) hyper-heuristic approach. The proposed approach allocates resources on a two-level architecture that addresses the allocation of containers to VMs and the allocation of VMs to physical machines.…”

Section: Related Workmentioning

confidence: 99%

Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

Chung

Lee

et al. 2020

Applied Sciences

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Resource allocation is vital for improving system performance in big data processing. The resource demand for various applications can be heterogeneous in cloud computing. Therefore, a resource gap occurs while some resource capacities are exhausted and other resource capacities on the same server are still available. This phenomenon is more apparent when the computing resources are more heterogeneous. Previous resource-allocation algorithms paid limited attention to this situation. When such an algorithm is applied to a server with heterogeneous resources, resource allocation may result in considerable resource wastage for the available but unused resources. To reduce resource wastage, a resource-allocation algorithm, called the minimizing resource gap (MRG) algorithm, for heterogeneous resources is proposed in this study. In MRG, the gap between resource usages for each server in cloud computing and the resource demands among various applications are considered. When an application is launched, MRG calculates resource usage and allocates resources to the server with the minimized usage gap to reduce the amount of available but unused resources. To demonstrate MRG performance, the MRG algorithm was implemented in Apache Spark. CPU- and memory-intensive applications were applied as benchmarks with different resource demands. Experimental results proved the superiority of the proposed MRG approach for improving the system utilization to reduce the overall completion time by up to 24.7% for heterogeneous servers in cloud computing.

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A Cooperative Coevolution Genetic Programming Hyper-Heuristics Approach for On-Line Resource Allocation in Container-Based Clouds

Cited by 35 publications

References 57 publications

A survey on evolutionary computation for complex continuous optimization

A survey on evolutionary computation for complex continuous optimization

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Minimizing Resource Waste in Heterogeneous Resource Allocation for Data Stream Processing on Clouds

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