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Proceedings ArticleDOI

A NSGA-II-based Approach for Multi-objective Micro-service Allocation in Container-based Clouds

11 May 2020-pp 282-289
TL;DR: This work proposes a multi-objective NSGA-II to optimize the availability of applications and the energy consumption requirement of container-based clouds to provide solutions with different tradeoffs between two objectives for cloud providers to choose from.
Abstract: Micro-services is a widely adopted architecture to develop large scale web applications. To provide a scalable and low-overhead resource service to micro-service applications, the new container-based clouds are proposed. The new clouds use both containers and VMs to manage resources to achieve a low-overhead, high-utilization data center. However, existing resource allocation approaches either do not consider the dependencies between containers or can only be applied in OS-level container clouds which allocate containers directly to physical machines. To address the multi-objective optimization problem, this work proposes a multi-objective NSGA-II to optimize the availability of applications and the energy consumption requirement of container-based clouds. Our goal is to provide solutions with different tradeoffs between two objectives for cloud providers to choose from. We evaluate the algorithm with a wide range of scenarios by simulation and compare with state-of-the-art algorithms. The results show that our approach significantly outperforms other approaches.
Citations
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Journal ArticleDOI
TL;DR: This paper proposes a priority, power and traffic-aware approach for efficiently solving the VM placement problem in a CDC and aims to jointly minimize power consumption, network consumption and resource wastage in a multi-dimensional and heterogeneous CDC.

28 citations

Journal ArticleDOI
TL;DR: Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, is proposed to solve the multi-objective microservice deployment problem (MMDP) in edge computing and has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure.
Abstract: The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increased node loads. In this article, we first model the invocation relationships among microservices as an undirected and weighted interaction graph to characterize the communication overhead. Then, we propose a multi-objective microservice deployment problem (MMDP) in edge computing. MMDP aims to minimize the communication overhead while achieving load balance between edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, to solve MMDP and obtain the optimal deployment strategy. In addition, to improve the scalability of the services, we propose an Elastic Scaling algorithm (ES) based on heuristics to deal with the dynamic pressure of requests. Finally, we conduct a series of experiments in Kubernetes to evaluate the performance of our approach. Experimental results indicate that, compared with interaction-aware strategy and Kubernetes default strategy, RSDQL has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure.

12 citations

Journal ArticleDOI
TL;DR: In this paper , a three-step scheduling model is proposed to combine scheduling of container-based workflows and the deployment of containers on a cloud-edge environment, where three evolution strategies are designed and combined with two multi-objective algorithm frameworks.

4 citations

Journal ArticleDOI
TL;DR: In this paper , a multi-objective microservice deployment problem (MMDP) in edge computing is proposed, which aims to minimize the communication overhead while achieving load balance between edge nodes.
Abstract: The microservice deployment strategy is promising in reducing the overall service response time in the microservice-oriented edge computing platform. However, existing works ignore the effect of different interaction frequencies among microservices and the decrease in service execution performance caused by the increased node loads. In this article, we first model the invocation relationships among microservices as an undirected and weighted interaction graph to characterize the communication overhead. Then, we propose a multi-objective microservice deployment problem (MMDP) in edge computing. MMDP aims to minimize the communication overhead while achieving load balance between edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q Learning (RSDQL), a learning-based algorithm, to solve MMDP and obtain the optimal deployment strategy. In addition, to improve the scalability of the services, we propose an Elastic Scaling algorithm (ES) based on heuristics to deal with the dynamic pressure of requests. Finally, we conduct a series of experiments in Kubernetes to evaluate the performance of our approach. Experimental results indicate that, compared with interaction-aware strategy and Kubernetes default strategy, RSDQL has shorter response times, more balanced resource loads, and makes services scale elastically according to the request pressure.

4 citations

Journal ArticleDOI
TL;DR: CSS, a dynamic resource manager utilizing system call data collected for security purposes, is proposed, which utilizes the SBCC algorithm, which uses the number of futex system calls as a heuristic measure to determine thenumber of IO-intensive workload occurrences.
Abstract: Multiple containers running scientific workflows in SMP-based high-performance computers generate some bottlenecks due to workload flexibility. To improve system resource utilization by minimizing these bottlenecks, vertical resource management is required to determine an appropriate resource usage policy according to the resource usage type of the container. However, the traditional methods have additional overhead for collecting monitoring metrics, and the structure of the resource manager is complex. In this paper, in order to compensate for these shortcomings, we propose CSS, a dynamic resource manager utilizing system call data collected for security purposes. The CSS utilizes the SBCC algorithm, which uses the number of futex system calls as a heuristic measure to determine the number of IO-intensive workload occurrences. In addition, the CTBRA algorithm is used to determine the range of resources to be allocated for each container and to perform actual resource allocation. We implemented a prototype of CSS and conducted experiments on NPB to analyze the performance of CSS with various types of large-scale tasks of a scientific workflow. As a result of the experiment, it showed a performance improvement of up to 7% compared with the environment where Linux cgroups were not applied. In addition, CANU performance analysis was performed to verify the effectiveness of applications used in the real world, and performance improvement of up to 4.5% was shown.
References
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Journal ArticleDOI
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Abstract: Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and sharing have been criticized mainly for: (1) their O(MN/sup 3/) computational complexity (where M is the number of objectives and N is the population size); (2) their non-elitism approach; and (3) the need to specify a sharing parameter. In this paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties. Specifically, a fast non-dominated sorting approach with O(MN/sup 2/) computational complexity is presented. Also, a selection operator is presented that creates a mating pool by combining the parent and offspring populations and selecting the best N solutions (with respect to fitness and spread). Simulation results on difficult test problems show that NSGA-II is able, for most problems, to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the Pareto-archived evolution strategy and the strength-Pareto evolutionary algorithm - two other elitist MOEAs that pay special attention to creating a diverse Pareto-optimal front. Moreover, we modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint nonlinear problem, are compared with another constrained multi-objective optimizer, and the much better performance of NSGA-II is observed.

37,111 citations

Journal ArticleDOI
TL;DR: This article provides a general overview of the field now known as "evolutionary multi-objective optimization," which refers to the use of evolutionary algorithms to solve problems with two or more (often conflicting) objective functions.
Abstract: This article provides a general overview of the field now known as "evolutionary multi-objective optimization," which refers to the use of evolutionary algorithms to solve problems with two or more (often conflicting) objective functions. Using as a framework the history of this discipline, we discuss some of the most representative algorithms that have been developed so far, as well as some of their applications. Also, we discuss some of the methodological issues related to the use of multi-objective evolutionary algorithms, as well as some of the current and future research trends in the area.

1,213 citations

Journal Article
TL;DR: The model is shown to accurately predict the convergence ra te of a GA using tournament select ion in the onemax domain for a wide range of t ournament sizes and noise levels.
Abstract: Abstr act . Tournament select ion is a useful and rob ust select ion mechanism commonly used by genet ic algorithms (GAs). The selecti on pr essure of to urnament select ion direc tly varies wit h the tournam en t size-the more compe t it ors , t he higher the resulting select ion pr essur e. This pap er develops a model, based on order stat ist ics, that can be used to quantita tively predict th e resul ting select ion pr essure of a tournament of a given size. T his mo del is used to pr edict the convergence ra tes of GAs utili zing tournament selection. While to urnament selection is often used in conjunct ion wit h noisy (imperfect) fitness fun cti ons, lit tl e is understood abo ut how the noise affect s the resul ting select ion pr essur e. The model is extended to quantit atively pred ict t he select ion pressure for tournam ent select ion utili zing noisy fitn ess functions . Given the to urnament size and noise level of a noisy fitness fun ct ion , the exte nded mod el is used to pr ed ict t he resu lt ing select ion pr essure of to urnament select ion . T he accuracy of the mod el is verified using a simple test domain, t he onemax (bit-count ing) domain . T he model is shown to accurately predict t he convergence ra te of a GA using tournament select ion in the onemax domain for a wide range of t ournament sizes and noise levels. T he model develop ed in this paper has a number of immediat e pra cti cal uses as well as a number of longer term rami fica tions. Immediately, t he mod el may be used for determ ining appropria te ra nges of cont rol para meters , for est imat ing stopping times to achieve a spec ified level of solution qua lity , and for approximating convergence t imes in impor tant classes offunction evaluatio ns that utilize sampling . Longer term, the approach of this st udy may be applied to bet ter underst an d

1,005 citations


"A NSGA-II-based Approach for Multi-..." refers background in this paper

  • ...elitism [34] with size 5 and tournament selection [35] with size 2....

    [...]

Journal ArticleDOI
TL;DR: Docker, an open source project that automates the faster deployment of Linux applications, and Kubernetes, a open source cluster manager for Docker containers, are looked at.
Abstract: This issue's "Cloud Tidbit" focuses on container technology and how it's emerging as an important part of the cloud computing infrastructure. It looks at Docker, an open source project that automates the faster deployment of Linux applications, and Kubernetes, an open source cluster manager for Docker containers.

890 citations


"A NSGA-II-based Approach for Multi-..." refers background in this paper

  • ...Container-based clouds [2], [3] are a new variant of Platform as a Service (PaaS) clouds [4] that are designed for managing large scale web applications with containers....

    [...]

Book ChapterDOI
01 Jan 2005
TL;DR: In this introductory chapter, some fundamental concepts of multiobjective optimization are introduced, emphasizing the motivation and advantages of using evolutionary algorithms.
Abstract: Very often real-world applications have several multiple conflicting objectives. Recently there has been a growing interest in evolutionary multiobjective optimization algorithms that combine two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. In this introductory chapter, some fundamental concepts of multiobjective optimization are introduced, emphasizing the motivation and advantages of using evolutionary algorithms. We then lay out the important contributions of the remaining chapters of this volume.

363 citations