Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> There is disclosed a vehicle wash apparatus including a wash arch frame carried on tracks extending longitudinally of a wash stall in which a car to be washed is parked. Overhead arms are pivotally carried on their respective one ends from an overhead transverse horizontal pivot shaft and mount a horizontally disposed rotary top brush from the free ends thereof for selective lowering into a position for contacting the front of a vehicle to wash the grill and for travel rearwardly along the hood, top and trunk of the vehicle. Side brush arms are carried pivotally from the sides of the arch frame for rotation about vertical axes and project in a direction opposite the direction in which the top brush arms, in their retracted positions, project and are urged inwardly to wash the sides of the car as the arch carries the top brush rearwardly on the tracks. Such side arms are pivoted inwardly as the side brushes clear the rear corners of the car to carry the side brushes about the rear corners of the vehicle to wash the rear thereof as the top brush completes washing of the top of the trunk. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Resource scheduling based on SLA (Service Level Agreement) in cloud computing is NP-hard problem. There is no efficient method to solve it. This paper proposes a new method to solve the problem by applying stochastic integer programming for optimal resource scheduling in cloud computing. Applying Gr\"{o}bner bases theory for solving the stochastic integer programming problem and the experimental results of the implementation are also presented. <s> BIB003 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> A key issue for Cloud Computing data-centers is to maximize their profits by minimizing power consumption and SLA violations of hosted applications. In this paper, we propose a resource management framework combining a utility-based dynamic Virtual Machine provisioning manager and a dynamic VM placement manager. Both problems are modeled as constraint satisfaction problems. The VM provisioning process aims at maximizing a global utility capturing both the performance of the hosted applications with regard to their SLAs and the energy-related operational cost of the cloud computing infrastructure. We show several experiments how our system can be controlled through high level handles to make different trade-off between application performance and energy consumption or to arbitrate resource allocations in case of contention. <s> BIB004 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Differential evolution (DE) is an effective and efficient optimization algorithm that has been successfully applied to many problems. However, the DE performance significantly depends on the elaborate settings of its parameters. Designers of DE usually spend great efforts to find proper parameter settings because good parameter values usually vary with different problems. In order to enhance the efficiency and robustness of DE, this paper proposes a novel DE algorithm, PLADE, which uses the learning mechanism in particle swarm optimization (PSO), termed as PSO-Learning (PL) strategy, to adaptively control the DE parameters. PLADE encodes the DE parameters into each individual and evolve the parameters during the evolutionary process. The individuals that achieve good fitness and survive in the evolution imply good parameter settings, the poor individuals use the PL strategy to let their parameters learn from the parameters in the good individuals. With such a PL based parameter self-adaptation strategy, PLADE can evolve the parameters to better values and can adapt the parameters to match the requirements of different evolutionary states and different optimization problems. PLADE is tested by six benchmark functions with unimodal and multimodal characteristics. Experimental results show that PLADE not only outperforms conventional DE with fixed parameter settings, in terms of solution quality, convergence speed, and algorithm reliability, but also is better than or at least comparable to some other state-of-the-art adaptive DE variants. <s> BIB005 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Evolutionary computation (EC) is a kind of optimization methodology inspired by the mechanisms of biological evolution and behaviors of living organisms. In the literature, the terminology evolutionary algorithms is frequently treated the same as EC. This article focuses on making a survey of researches based on using ML techniques to enhance EC algorithms. In the framework of an ML-technique enhanced-EC algorithm (MLEC), the main idea is that the EC algorithm has stored ample data about the search space, problem features, and population information during the iterative search process, thus the ML technique is helpful in analyzing these data for enhancing the search performance. The paper presents a survey of five categories: ML for population initialization, ML for fitness evaluation and selection, ML for population reproduction and variation, ML for algorithm adaptation, and ML for local search. <s> BIB006 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. The advent of Cloud computing as a new model of service provisioning in distributed systems, encourages researchers to investigate its benefits and drawbacks in executing scientific applications such as workflows. There are a mass of researches on the issue of scheduling in cloud computing, most of them, however, are bout workflow and job scheduling. A cloud workflow system is a type of platform service which facilitates the automation of distributed applications based on the novel cloud infrastructure. The scheduling of the workflows is typically performed manually by the IT staff. The scheduling entails the selection of the services and the appropriate start time for each workflow. In this paper we have reviewed different types of workflow scheduling algorithms. The main aim of this work is to study various problems, issues and types of scheduling algorithms for cloud workflows. <s> BIB007 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Cloud computing technology virtualizes and offers many services across the network. It mainly aims at scalability, availability, throughput, and resource utilization. C loud model promotes availability and is composed of five essential characteristics , three service models, and four deployment models.We proposed different research papers on cloud computing and scheduling . <s> BIB008 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Cloud computing is being used to avoid maintenance costs and upfront investment, while providing elasticity to the available computational power in a pay-per-use basis. Customers can make use of the cloud as a software (SaaS), platform (PaaS), or infrastructure (IaaS) provider. When one customer utilizes an environment provided by a SaaS cloud, she is unaware of any details about the computational infrastructure where her requests are being processed. Therefore, such infrastructure can be composed of computational resources from a datacenter owned by the SaaS or its resources can be leased from a cloud infrastructure provider. In this paper we present an integer linear program (ILP) formulation for the problem of scheduling SaaS customer's workflows into multiple IaaS providers where SLA exists at two levels. In addition, we present heuristics to solve the relaxed version of the presented ILP. Simulation results show that the proposed ILP is able to find low-cost solutions for short deadlines, while the proposed heuristics are effective when deadlines are larger. <s> BIB009 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by human creative problem solving process. Human being is the most intelligent organism in the world and the brainstorming process popularly used by them has been demonstrated to be a significant and promising way to create great ideas for problem solving. BSO transplants the brainstorming process in human being into optimization algorithm design and gains successes. BSO generally uses the grouping, replacing, and creating operators to produce ideas as many as possible to approach the problem global optimum generation by generation. In this paper, we propose two novel designs to enhance the conventional BSO performance. The first design of the modified BSO (MBSO) is that it uses a simple grouping method (SGM) in the grouping operator instead of the clustering method to reduce the algorithm computational burden. The second design is that MBSO uses a novel idea difference strategy (IDS) in the creating operator instead of the Gaussian random strategy. The IDS not only contains open minded element to avoid the ideas being trapped by local optima, but also can match the search environment to create better new ideas for problem solving. Experiments have been conducted to illustrate the effectiveness and efficiency of the MBSO algorithm. Moreover, the contributions of SGM and IDS are investigated to show how and why MBSO can perform better than BSO. <s> BIB010 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> In this paper, we propose a service-aware approach to enhance survivability in virtualized data centers. The idea is to create and maintain a Survivable Virtual Infrastructure (SVI) for each service or tenant, which includes Virtual Machines (VMs) hosting the corresponding application and their backup VMs. A fundamental problem is to determine how to map each SVI to a data center network with minimum operational costs while satisfying each VM's resource requirements and bandwidth demands between VMs before and after failures. This problem can be naturally divided into two subproblems: VM Placement (VMP) and Virtual Link Mapping (VLM). We first present a general optimization framework. Then we propose an efficient algorithm for VMP, and a polynomial-time optimal algorithm for VLM, which can be used as subroutines in the framework. We also present an effective heuristic algorithm that jointly solves two subproblems. It has been shown by extensive simulation results based on the real VM workload traces collected from Syracuse University's green data center that compared to the First Fit Decreasing (FFD) and shortest path routing based baseline algorithm, the proposed algorithms significantly reduce the reserved bandwidth, and yield comparable results in terms of the number of active servers. <s> BIB011 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO. <s> BIB012 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Cloud computing is the latest distributed computing paradigm and it offers tremendous opportunities to solve large-scale scientific problems. However, it presents various challenges that need to be addressed in order to be efficiently utilized for workflow applications. Although the workflow scheduling problem has been widely studied, there are very few initiatives tailored for cloud environments. Furthermore, the existing works fail to either meet the user's quality of service (QoS) requirements or to incorporate some basic principles of cloud computing such as the elasticity and heterogeneity of the computing resources. This paper proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) clouds. We present an algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints. Our heuristic is evaluated using CloudSim and various well-known scientific workflows of different sizes. The results show that our approach performs better than the current state-of-the-art algorithms. <s> BIB013 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> The performance of differential evolution (DE) largely depends on its mutation strategy and control parameters. In this paper, we propose an adaptive DE (ADE) algorithm with a new mutation strategy DE/lbest/1 and a two-level adaptive parameter control scheme. The DE/lbest/1 strategy is a variant of the greedy DE/best/1 strategy. However, the population is mutated under the guide of multiple locally best individuals in DE/lbest/1 instead of one globally best individual in DE/best/1. This strategy is beneficial to the balance between fast convergence and population diversity. The two-level adaptive parameter control scheme is implemented mainly in two steps. In the first step, the population-level parameters $F_{p}$ and $CR_{p}$ for the whole population are adaptively controlled according to the optimization states, namely, the exploration state and the exploitation state in each generation. These optimization states are estimated by measuring the population distribution. Then, the individual-level parameters $F_{i}$ and $CR_{i}$ for each individual are generated by adjusting the population-level parameters. The adjustment is based on considering the individual's fitness value and its distance from the globally best individual. This way, the parameters can be adapted to not only the overall state of the population but also the characteristics of different individuals. The performance of the proposed ADE is evaluated on a suite of benchmark functions. Experimental results show that ADE generally outperforms four state-of-the-art DE variants on different kinds of optimization problems. The effects of ADE components, parameter properties of ADE, search behavior of ADE, and parameter sensitivity of ADE are also studied. Finally, we investigate the capability of ADE for solving three real-world optimization problems. <s> BIB014 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> Artificial bee colony (ABC) algorithm is a novel heuristic algorithm inspired from the intelligent behavior of honey bee swarm. ABC algorithm has a good performance on solving optimization problems of multivariable functions and has been applied in many fields. However, traditional ABC algorithm chooses solutions on the onlooker stage with roulette wheel selection (RWS) strategy which has several disadvantages. Firstly, RWS is suitable for maximization optimization problem. The fitness value has to be converted when solving minimization optimization problem. This makes RWS difficult to be generally used in real-world applications. Secondly, RWS has no any parameter that can control the selection pressure. Therefore, RWS is not easy to adapt to various optimization problems. This paper proposes a tournament selection based ABC (TSABC) algorithm to avoid these disadvantages of RWS based ABC. Moreover, this paper proposes an elitist strategy that can be applied to traditional ABC, TSABC, and any other ABC variants, so as to avoid the phenomenon that ABC algorithm may abandon the globally best solution in the scout stage. We compare the performance of traditional ABC and TSABC on a set of benchmark functions. The experiment results show that TSABC is more flexible and can be efficiently adapted to solve various optimization problems by controlling the selection pressure. <s> BIB015 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> Cloud Resource Scheduling Algorithms <s> This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a "best improvement local search" (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm. <s> BIB016
There exist scheduling algorithms that solve relatively routine problems in cloud computing, as reported in BIB007 , BIB008 , and Chawla and Bhonsle [2012] . These algorithms are often exhaustive in nature and can hence work well if the size of the scheduling problem is manageable through converting it to a combinatorial optimization problem such as a Linear Programming (LP)  BIB002 , Integer Programming (IP) BIB003 , Integer Linear Programming (ILP) BIB009 , or constraint satisfaction problem BIB004 . However, being an NP-hard problem, cloud scheduling can break down exhaustive or enumerative approaches with an increase in dimensionality or in the number of variables to be optimized. Therefore, a heuristic method, such as that based on LP relaxation, has been adopted by BIB002 for minimizing server costs when scheduling VMs onto PMs. Also, two ILP-relaxation-based heuristics have been used by BIB009 to schedule cloud resources so as to execute user tasks with two SLA levels from both the cloud user and the provider. BIB003 have applied a minimized geometric Buchberger algorithm to a stochastic IP problem for SLA-aware resource scheduling. BIB011 have also proposed heuristic methods to solve the problem of scheduling VMs on PMs. A heuristic backtracking algorithm, which performs a "depth first" search-like method to enumerate a subset of possible VM placement solutions, and a LP rounding based optimal algorithm for virtual link mapping are proposed to determine how to map user requirements to a data center network with minimum operational costs. Most reported heuristic approaches are single-point based. Although single-point heuristic approaches and a greedy approach like First-Fit Decreasing (FFD) BIB001 can be fast to find a nonprecise close solution, they would be ineffective if a global solution is sought and are hence inefficient in many practical applications BIB013 . Among all heuristic methods, the most powerful of all are the population-based EC algorithms, which effectively exchange search information among team members. As the problem of cloud resource scheduling is seen NP-hard, its intractability is best tackled by an EC algorithm BIB012 . EC is a nonconventional optimization paradigm, inspired by the mechanisms of natural evolution and behaviors of living organisms BIB006 . In general, EC algorithms include Evolutionary Algorithms (EAs) such as the Genetic Algorithm (GA), swarm intelligence algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), and other nature-inspired algorithms BIB014 BIB005 BIB010 BIB015 . Recent work has shown an emerging trend in the use of EC algorithms for improved effectiveness and efficiency in complex optimization systems BIB016 ]. This trend would continue with the increase of the proliferation, ambition, and complexity of cloud computing. Based on the taxonomy of Figure 3 , we shall analyze why and how GA, ACO, PSO, and/or other EC algorithms are used for cloud resource scheduling. Various characteristics of different EC methods for various objectives are provided in the next three sections. Algorithm design, scheduling objectives, experimental environments, scales, and results of the work surveyed are also to be listed and compared in tables for the readers to form a quick overview on the EC-based cloud resource scheduling methods. We shall also offer an overview on the advantages and disadvantages of using EC algorithms, following an overview of the EC framework in the next subsection for a wider readership.