Cloud computing has grown to become a popular distributed computing service offered by commercial providers. More recently, Edge and Fog computing resources have emerged on the wide-area network as part of Internet of Things (IoT) deployments. These three resource abstraction layers are complementary, and provide distinctive benefits. Scheduling applications on clouds has been an active area of research, with workflow and dataflow models serving as a flexible abstraction to specify applications for execution. However, the application programming and scheduling models for edge and fog are still maturing, and can benefit from learnings on cloud resources. At the same time, there is also value in using these resources cohesively for application execution. In this article, we present a taxonomy of concepts essential for specifying and solving the problem of scheduling applications on edge, for and cloud computing resources. We first characterize the resource capabilities and limitations of these infrastructure, and design a taxonomy of application models, Quality of Service (QoS) constraints and goals, and scheduling techniques, based on a literature review. We also tabulate key research prototypes and papers using this taxonomy. This survey benefits developers and researchers on these distributed resources in designing and categorizing their applications, selecting the relevant computing abstraction(s), and developing or selecting the appropriate scheduling algorithm. It also highlights gaps in literature where open problems remain.
Scheduling for different aspects cloud computing have been examined in the past. @cite_118 present a taxonomy of : application, virtualization and infrastructure. Algorithms are classified based on the specific objectives that should be met at each layer. Scheduling at the virtualization layer has the goal of mapping VMs on to physical machines, which is of particular interest to service providers. This been addressed by other specialized surveys such as @cite_159 @cite_52 . The goal of scheduling at the infrastructure layer is to place the resources and services at different locations in various data centers. @cite_104 drill-down into this layer and offer a taxonomy for inter-cloud architectures, with application brokering in these systems. At the application layer, the problem is to schedule the user's application on VMs, and three goals are discussed by @cite_118 : user QoS, provider efficiency, and negotiation.
@cite_118 @cite_159 @cite_52 @cite_104
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Cloud computing providers have setup several data centers at different geographical locations over the Internet in order to optimally serve needs of their customers around the world However, existing systems do not support mechanisms and policies for dynamically coordinating load distribution among different Cloud-based data centers in order to determine optimal location for hosting application services to achieve reasonable QoS levels Further, the Cloud computing providers are unable to predict geographic distribution of users consuming their services, hence the load coordination must happen automatically, and distribution of services must change in response to changes in the load To counter this problem, we advocate creation of federated Cloud computing environment (InterCloud) that facilitates just-in-time, opportunistic, and scalable provisioning of application services, consistently achieving QoS targets under variable workload, resource and network conditions The overall goal is to create a computing environment that supports dynamic expansion or contraction of capabilities (VMs, services, storage, and database) for handling sudden variations in service demands. This paper presents vision, challenges, and architectural elements of InterCloud for utility-oriented federation of Cloud computing environments The proposed InterCloud environment supports scaling of applications across multiple vendor clouds We have validated our approach by conducting a set of rigorous performance evaluation study using the CloudSim toolkit The results demonstrate that federated Cloud computing model has immense potential as it offers significant performance gains as regards to response time and cost saving under dynamic workload scenarios. With the emergence of Cloud Computing resources of physical machines have to be allocated to virtual machines (VMs) in an on-demand way. However, the efficient allocation of resources like memory, storage or bandwidth to a VM is not a trivial task. On the one hand, the Service Level Agreement (SLA) that defines QoS goals for arbitrary parameters between the Cloud provider and the customer should not be violated. On the other hand, the Cloud providers aim to maximize their profit, where optimizing resource usage is an important part. In this paper we develop a simulation engine that mimics the control cycle of an autonomic manager to evaluate different knowledge management techniques (KM) feasible for efficient resource management and SLA attainment. We especially focus on the use of Case Based Reasoning (CBR) for KM and decision-making. We discuss its suitability for efficiently governing on-demand resource allocation in Cloud infrastructures by evaluating it with the simulation engine. Cloud computing has emerged during the last decade to be widely adopted nowadays in several IT areas. It consists to propose market or not market-oriented resources as services that can be consumed in a ubiquitous, flexible and transparent way. In this paper, we deal with scheduling, one of the major cloud computing issue. According to the targeted cloud configuration, the addressed objectives and the accuracy of the view that one have of the cloud, we propose a taxonomy that identifies three levels of scheduling: service-level, task-level and Virtual Machine-level. We compare different cloud scheduling approaches from the literature and identify the common features and the differences between all the approaches according to each cloud scheduling level using different criteria. The reported results of the comparison show inter alia a weakness in the proposed schedulers, almost no Pareto multi-objective solving algorithms and very few realistic experiments facing the real cloud constraints. Providing real-time cloud services to Vehicular Clients (VCs) must cope with delay and delay-jitter issues. Fog computing is an emerging paradigm that aims at distributing small-size self-powered data centers (e.g., Fog nodes) between remote Clouds and VCs, in order to deliver data-dissemination real-time services to the connected VCs. Motivated by these considerations, in this paper, we propose and test an energy-efficient adaptive resource scheduler for Networked Fog Centers (NetFCs). They operate at the edge of the vehicular network and are connected to the served VCs through Infrastructure-to-Vehicular (I2V) TCP IP-based single-hop mobile links. The goal is to exploit the locally measured states of the TCP IP connections, in order to maximize the overall communication-plus-computing energy efficiency, while meeting the application-induced hard QoS requirements on the minimum transmission rates, maximum delays and delay-jitters. The resulting energy-efficient scheduler jointly performs: (i) admission control of the input traffic to be processed by the NetFCs; (ii) minimum-energy dispatching of the admitted traffic; (iii) adaptive reconfiguration and consolidation of the Virtual Machines (VMs) hosted by the NetFCs; and, (iv) adaptive control of the traffic injected into the TCP IP mobile connections. The salient features of the proposed scheduler are that: (i) it is adaptive and admits distributed and scalable implementation; and, (ii) it is capable to provide hard QoS guarantees, in terms of minimum maximum instantaneous rates of the traffic delivered to the vehicular clients, instantaneous rate-jitters and total processing delays. Actual performance of the proposed scheduler in the presence of: (i) client mobility; (ii) wireless fading; and, (iii) reconfiguration and consolidation costs of the underlying NetFCs, is numerically tested and compared against the corresponding ones of some state-of-the-art schedulers, under both synthetically generated and measured real-world workload traces.