Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> Cloud Computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established Grid Computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both. <s> BIB001 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> Cloud computing is a disruptive technology with profound implications not only for Internet services but also for the IT sector as a whole. Its emergence promises to streamline the on-demand provisioning of software, hardware, and data as a service, achieving economies of scale in IT solutions' deployment and operation. This issue's articles tackle topics including architecture and management of cloud computing infrastructures, SaaS and IaaS applications, discovery of services and data in cloud computing infrastructures, and cross-platform interoperability. Still, several outstanding issues exist, particularly related to SLAs, security and privacy, and power efficiency. Other open issues include ownership, data transfer bottlenecks, performance unpredictability, reliability, and software licensing issues. Finally, hosted applications' business models must show a clear pathway to monetizing cloud computing. Several companies have already built Internet consumer services such as search, social networking, Web email, and online commerce that use cloud computing infrastructure. Above all, cloud computing's still unknown "killer application" will determine many of the challenges and the solutions we must develop to make this technology work in practice. <s> BIB002 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> Cloud computing has been endorsed by the IT community as the new paradigm shift in the industry that charts the way forward. Unfortunately, the field is still on its path to rigor and robustness. This is epitomized by the numerous fuzzy articulations of “what is cloud computing”. This paper makes a first attempt at remedying this conundrum by providing a core technical specification of the model for cloud computing and demonstrating how current and future cloud deployments can use this to foster more productive technical discussion in future. <s> BIB003 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> Network-based cloud computing is rapidly expanding as an alternative to conventional office-based computing. As cloud computing becomes more widespread, the energy consumption of the network and computing resources that underpin the cloud will grow. This is happening at a time when there is increasing attention being paid to the need to manage energy consumption across the entire information and communications technology (ICT) sector. While data center energy use has received much attention recently, there has been less attention paid to the energy consumption of the transmission and switching networks that are key to connecting users to the cloud. In this paper, we present an analysis of energy consumption in cloud computing. The analysis considers both public and private clouds, and includes energy consumption in switching and transmission as well as data processing and data storage. We show that energy consumption in transport and switching can be a significant percentage of total energy consumption in cloud computing. Cloud computing can enable more energy-efficient use of computing power, especially when the computing tasks are of low intensity or infrequent. However, under some circum- stances cloud computing can consume more energy than conventional computing where each user performs all com- puting on their own personal computer (PC). <s> BIB004 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <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> BIB005 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <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> BIB006 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <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> INTRODUCTION <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> INTRODUCTION <s> Cloud computing is based on the concepts of distributed computing, grid computing, utility computing and virtualization. It is a virtual pool of resources which are provided to users via Internet. It gives users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure. Cloud computing service providers' one of the goals is to use the resources efficiently and gain maximum profit. This leads to task scheduling as a core and challenging issue in cloud computing. This paper gives different scheduling strategies and algorithms in cloud computing. <s> BIB009 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <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> BIB010 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> A brief review of the Internet history reveals the fact that the Internet evolved after the formation of primarily independent networks. Similarly, interconnected clouds, also called Inter-cloud, can be viewed as a natural evolution of cloud computing. Recent studies show the benefits in utilizing multiple clouds and present attempts for the realization of an Inter-cloud or federated cloud environment. However, cloud vendors have not taken into account cloud interoperability issues, and each cloud comes with its own solution and interfaces for services. This survey initially discusses all the relevant aspects motivating cloud interoperability. Furthermore, it categorizes and identifies possible cloud interoperability scenarios and architectures. The spectrum of challenges and obstacles that the Inter-cloud realization is faced with are covered, a taxonomy of them is provided, and fitting enablers that tackle each challenge are identified. All these aspects require a comprehensive review of the state of the art, including ongoing projects and studies in the area. We conclude by discussing future directions and trends toward the holistic approach in this regard. <s> BIB011 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users’ satisfaction level. The amount of this decrease depends on user's characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user's hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users’ satisfaction level that cause to gain in profitability. <s> BIB012 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> The popularity and rapid development of cloud computing in recent years has led to a huge amount of publications containing the achieved knowledge of this area of research. Due to the interdisciplinary nature and high relevance of cloud computing research, it becomes increasingly difficult or even impossible to understand the overall structure and development of this field without analytical approaches. While evaluating science has a long tradition in many fields, we identify a lack of a comprehensive scientometric study in the area of cloud computing. Based on a large bibliographic data base, this study applies scientometric means to empirically study the evolution and state of cloud computing research with a view from above the clouds. By this, we provide extensive insights into publication patterns, research impact and research productivity. Furthermore, we explore the interplay of related subtopics by analyzing keyword clusters. The results of this study provide a better understanding of patterns, trends and other important factors as a basis for directing research activities, sharing knowledge and collaborating in the area of cloud computing research. <s> BIB013 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm . <s> BIB014 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <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> BIB015 </s> Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches <s> INTRODUCTION <s> This paper proposes an information sharing mechanism (ISM) to improve the performance of particle swarm optimization (PSO). The ISM allows each particle to share its best search information, so that all the other particles can take advantage of the shared information by communicating with it. In this way, the particles could enhance the mutual interaction with the others sufficiently and heighten their search ability greatly by using the search information of the whole swarm. Also, a competitive and cooperative (CC) operator is designed for a particle to utilize the shared information in a proper and efficient way. As the ISM share the search information among all the particles, it is an appropriate way to mix up information of the whole swarm for a better exploration of the landscape. Therefore, the competitive and cooperative PSO with ISM (CCPSO-ISM) is capable to prevent the premature convergence when solving global optimization problems. The satisfactory performance of CCPSO-ISM is evaluated by comparing it with other variants of PSOs on a set of 16 global optimization functions. Moreover, the effectiveness and efficiency of CCPSO-ISM is validated under different test environments such as biased initialization, coordinate rotated and high dimensionality. <s> BIB016
With the ubiquitous growth of Internet access and of big data in their volume, velocity, and variety through the Internet, cloud computing becomes more and more proliferating in industry, academia, and society  BIB011 . The "cloud" provides worldwide users, being either stationary or on the move, with a new dimension of computing resources in the form of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) . These types of resources are provided on demand in a pay-per-use/subscription fashion in an Internet-based environment BIB001  . Consisting of a large number of resources as a federation, cloud computing shares some characteristics common to parallel computing such as cluster computing and grid computing, but differs in that it uses virtualization for resources management BIB003 . This allows computing resources to be scheduled for delivery to the end-user as a utility, dubbed the fifth utility, in a similar way to traditional utilities, namely, water, electricity, natural gas, and telephony  ]. For example, massively elastic computing nodes are now disposable at one's fingertips, anywhere and anytime, with payment only required when it is used BIB011 ]. This would not have been achievable with a traditional computing means or data center. The explosive demand in cloud computing poses a challenge to, as well as opens up, a new area of research, where the traditional boundary of computing resources is blurred by virtualization. As cloud computing is a market-oriented utility, optimally scheduling resources can allow the cloud providers and users separately to focus on their own businesses to maximize their profit and return on investment  . In meeting Service Level Agreements (SLAs), for example, optimally scheduling resources is seen as of paramount importance to cloud computing services BIB012 . Underestimating the provision of resources would lead to broken SLAs and penalties, while overestimating the provision would lead to resource underutilization and lost revenue BIB002 . Cloud resource scheduling is also a challenging problem for the provider's resource-economic and power-efficient requirements. Further, it is a significant issue to the user in terms of Quality of Service (QoS) requirements under pay-per-use provision BIB004 . Much work tackling the cloud resource scheduling problem has been reported in the literature, although mostly in conference proceedings due to the dynamically emerging nature of the problem BIB013 BIB011 . Google Scholar, for example, has reported 195,000 entries under the keyword of "cloud computing" and 95,800 entries under "cloud computing+resource" from 2009 to 2014, as depicted in Figure 1 (a). This helps one visualize the significant role that resource management and scheduling play in cloud computing. In both academia and industry, the problem of cloud resource scheduling is seen to be as hard as a Nondeterministic Polynomial (NP) optimization problem BIB006 BIB010 , that is, an NP-hard problem, whose intractability increases exponentially with the number of variables if using a deterministic algorithm such as exhaustive search is attempted. Hence, algorithms that solve relatively routine cloud scheduling problems can suffer from a dimensionality breakdown when the size of the problem grows. This problem becomes more challenging with the increase of the proliferation, ambition, and complexity of cloud computing. Using an Evolutionary Computation (EC) algorithm to tackle cloud resource scheduling has received increasing attention in recent years, as such an algorithm offers an NP-hard problem global solution acceptable in a time frame proportional to the number of variables  BIB005 BIB016 . Following their successes in resource scheduling for grid computing  , EC algorithms have gained momentum in tackling the increasing structural complexity of cloud resource management and scheduling BIB014 BIB015 . As reported in Google Scholar and summarized in Figure 1 (b), over 17.5% of the works involving "resources" are estimated concerning the use of an evolutionary method. Although there exist surveys on "green cloud computing" BIB004 , cloud workflow scheduling BIB007 , cloud task scheduling BIB008 BIB009 , virtual machine performance management in cloud computing  , and "interconnected cloud computing" BIB011 ], they have not discussed EC algorithms in solving these problems. This article aims to provide an overview of the problem and a systematic review of the state-of-theart techniques for cloud resource scheduling, with contributions including -a two-leveled taxonomic structure, through analysis of the scheduling layers; -the resultant clarity of landscape of the cloud scheduling problem; -an ensuing systematic survey on the state-of-the-art approaches to cloud scheduling with analysis of their pros and cons; -suggestions on how to use various EC approaches to various layers of cloud resource scheduling; -analysis of challenges ahead and potential future research directions, such as realtime, adaptive dynamic, large-scale, multiobjective, and distributed scheduling; and -outlook at the dawn of Industry 4.0, with the rapid fusion of information and data technology for cloud-based cyber-physical integration. The rest of the article is organized as follows. A framework of the problem is presented in Section 2, summarized with a two-leveled taxonomic structure. This helps classify the rapidly advancing research and, according to the taxonomy, present survey details in Sections 3, 4, and 5 systematically. Publications surveyed were selected by considering their quality, popularity, number of citations, and coverage of different aspects of the topic. In Section 6, challenges and future directions of research are discussed and highlighted. These are followed by a concluding summary in Section 7. 