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Journal ArticleDOI: 10.1080/0952813X.2020.1725652

Bi-objective web service composition problem in multi-cloud environment: a bi-objective time-varying particle swarm optimisation algorithm

04 Mar 2021-Journal of Experimental and Theoretical Artificial Intelligence (Taylor & Francis)-Vol. 33, Iss: 2, pp 179-202
Abstract: Cloud computing became an inevitable information technology industry. Despite its several plus points such as economy of scale and rapid elasticity, it suffers from vendor lock-in, resource limitat...

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Journal ArticleDOI: 10.1016/J.SUSCOM.2020.100374
Abstract: This paper formulates a new multi-objective virtual machine placement (VMP) problem, which is a challenging task in cloud datacenters (DCs). In cloud environment, there are two stakeholders, namely, users and providers. Both sides try to take more benefit whereas a trade-off between conflicting benefits is crucial. From providers’ perspective, power consumption and resource wastage are two objectives to be optimized whereas gaining high quality of service (QoS) is a critical point for users. The unpleasant issue that a user endures in the cloud environment is network delay; this is affected by common bandwidth linkage which is shared between different users’ applications; the reason for considering bandwidth usage optimization as the third objective function in users’ viewpoint. However, inefficient network bandwidth usage has drastic impact on overall performance even makes network links to get saturated and throttles communication-intensive applications. Therefore, VMs with high affinity and traffic dependency must be physically placed as close as possible so less traffic is sent on network layers. To figure out this combinatorial multi-objective problem, we extend a hybrid multi-objective genetic-based optimization solution. To evaluate this work, we conducted extensive scenarios with variable correlation coefficients between resources in requested VMs. The simulation results prove that our proposed hybrid meta-heuristic algorithm outperforms against state-of-the-art ACO-based, well-known heuristic-based FFD algorithms, and random-based approach in terms of total power consumption, resource wastage, the total data transfer rate in network, and number of active servers in use. Also, the simulations in larger search space demonstrated proposed approach has high potential of scalability.

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Topics: Network delay (57%), Cloud computing (56%), Quality of service (55%) ... read more

23 Citations


Journal ArticleDOI: 10.1007/S00500-020-05523-1
18 Jan 2021-
Abstract: Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reach the goal, a system framework is presented which has different modules. The main concentration of the paper is on virtual machine (VM) consolidation module which launches users requested VMs on the minimum number of active servers to reduce datacenter total power consumption (TPC). In this paper, the VMs consolidation is abstracted to two-dimensional bin-packing problem and also is formulated to an integer linear programming. Since the papers in the literature scarcely are aware of skewness in resources of requested VMs and for discrete nature of search space, this paper presents the resource skewness-aware VMs consolidation algorithm based on improved thermodynamic simulated annealing approach because resource skewness potentially compels the algorithm to activate additional servers. The proposed SA-based algorithm is validated in extensive scenarios with different resource skewness in comparison with two heuristics and two meta-heuristics. The average results reported from different scenarios proves superiority of proposed algorithm in comparison with other approaches in terms of the number of used servers, TPC, and total resource wastage of datacenter.

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Topics: Server (55%), Cloud computing (55%), Elasticity (cloud computing) (54%) ... read more

5 Citations


Journal ArticleDOI: 10.1007/S12652-020-02645-0
Sasan Gharehpasha1, Mohammad Masdari1Institutions (1)
Abstract: Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers (DCs) has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine (VM) assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines (PM). In current paper a new approach is proposed that using a combination of the sine cosine algorithm (SCA) and ant lion optimizer (ALO) as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Second objective is reducing the resources wastage by using optimal VM assignment on PMs in cloud DCs. Reducing SLA levels was another purpose of this research. By using the method, the number of increase of migration of VMs to PMs is prevented. In this paper, several performance metrics such as resource wastage, power consumption, overall memory utilization, overall CPU utilization, overall storage space, and overall bandwidth, a number of active PMs, a number of shutdowns, a number of migrations, and SLA are used. Ultimately, the results obtained from the proposed algorithm were compared with those of the algorithms used in this regard, including First Fit (FF), VMPACS and MGGA.

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Topics: Cloud computing (57%), Assignment problem (53%), Energy consumption (52%) ... read more

5 Citations



Open accessJournal ArticleDOI: 10.1007/S40747-021-00368-Z
Abstract: Nowadays, fog computing as a complementary facility of cloud computing has attracted great attentions in research communities because it has extraordinary potential to provide resources and processing services requested for applications at the edge network near to users. Recent researchers focus on how efficiently engage edge networks capabilities for execution and supporting of IoT applications and associated requirement. However, inefficient deployment of applications’ components on fog computing infrastructure results bandwidth and resource wastage, maximum power consumption, and unpleasant quality of service (QoS) level. This paper considers reduction of bandwidth wastage in regards to application components dependency in their distributed deployment. On the other hand, the service reliability is declined if an application’s components are deployed on a single node for the sake of power consumption management viewpoint. Therefore, a mechanism for tackling single point of failure and application reliability enhancement against failure are presented. Then, the components deployment is formulated to a multi-objective optimization problem with minimization perspective of both power consumption and total latency between each pair of components associated to applications. To solve this combinatorial optimization problem, a multi-objective cuckoo search algorithm (MOCSA) is presented. To validate the work, this algorithm is assessed in different conditions against some state-of the arts. The simulation results prove the amount 42%, 29%, 46%, 13%, and 5% improvement of proposed MOCSA in terms of average overall latency respectively against MOGWO, MOGWO-I, MOPSO, MOBA, and NSGA-II algorithms. Also, in term of average total power consumption the improvement is about 43%, 28%, 41%, 30%, and 32% respectively.

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Topics: Cloud computing (53%), Single point of failure (52%), Quality of service (51%) ... read more

2 Citations


References
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43 results found


Proceedings ArticleDOI: 10.1109/ICNN.1995.488968
06 Aug 2002-
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

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Topics: Multi-swarm optimization (77%), Metaheuristic (69%), Stochastic diffusion search (67%) ... read more

32,237 Citations


Journal ArticleDOI: 10.1109/4235.996017
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.

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Topics: Sorting (57%), Evolutionary algorithm (56%), Mating pool (56%) ... read more

30,928 Citations


ReportDOI: 10.6028/NIST.SP.800-145
28 Sep 2011-
Abstract: Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models.

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Topics: NIST (62%), Community cloud (59%), Cloud computing (59%) ... read more

14,469 Citations


Open access
01 Jan 1999-
Abstract: : This research organizes, presents, and analyzes contemporary Multiobjective Evolutionary Algorithm (MOEA) research and associated Multiobjective Optimization Problems (MOPs). Using a consistent MOEA terminology and notation, each cited MOEAs' key factors are presented in tabular form for ease of MOEA identification and selection. A detailed quantitative and qualitative MOEA analysis is presented, providing a basis for conclusions about various MOEA-related issues. The traditional notion of building blocks is extended to the MOP domain in an effort to develop more effective and efficient MOEAs. Additionally, the MOEA community's limited test suites contain various functions whose origins and rationale for use are often unknown. Thus, using general test suite guidelines appropriate MOEA test function suites are substantiated and generated. An experimental methodology incorporating a solution database and appropriate metrics is offered as a proposed evaluation framework allowing absolute comparisons of specific MOEA approaches. Taken together, this document's classifications, analyses, and new innovations present a complete, contemporary view of current MOEA "state of the art" and possible future research. Researchers with basic EA knowledge may also use part of it as a largely self-contained introduction to MOEAs.

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Topics: Test suite (51%)

1,237 Citations


Journal ArticleDOI: 10.1145/1232722.1232728
Tao Yu1, Yue Zhang1, Kwei-Jay Lin1Institutions (1)
Abstract: Service-Oriented Architecture (SOA) provides a flexible framework for service composition Using standard-based protocols (such as SOAP and WSDL), composite services can be constructed by integrating atomic services developed independently Algorithms are needed to select service components with various QoS levels according to some application-dependent performance requirements We design a broker-based architecture to facilitate the selection of QoS-based services The objective of service selection is to maximize an application-specific utility function under the end-to-end QoS constraints The problem is modeled in two ways: the combinatorial model and the graph model The combinatorial model defines the problem as a multidimension multichoice 0-1 knapsack problem (MMKP) The graph model defines the problem as a multiconstraint optimal path (MCOP) problem Efficient heuristic algorithms for service processes of different composition structures are presented in this article and their performances are studied by simulations We also compare the pros and cons between the two models

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Topics: Knapsack problem (56%), Service-oriented architecture (55%), Web service (55%) ... read more

1,201 Citations