Top PDF Cross-layer scheduling in cloud computing systems

Cross-layer scheduling in cloud computing systems

Cross-layer scheduling in cloud computing systems

Routing-Level Scheduling : There has been a lot of previous works such as Orchestra [27] and Seawall [28] which propose to improve the shuffle phase by scheduling flows using a weighted fair scheme. Oktopus [29] and Second- Net [30] propose static reservations throughout the network to implement bandwidth guarantees for the hose model and pipe model, respectively. e main drawback of reservation systems is that they do not achieve the work conservation property, since the unused bandwidth is not shared between tenants. Gatekeeper [31] proposes a per-VM hose model with work conrva- tion. Gatekeeper uses a hypervisor-based mechanism, which, however, works only for full bisection-bandwidth networks. FairCloud [32] improves upon previous approaches by introducing multiple policies to either achieve link- proportionality or congestion-proportionality. Many of these works can be leveraged to work in our network-level scheduler.
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Uplink Cross Layer Scheduling with Differential QoS Requirements in OFDMA Systems

Uplink Cross Layer Scheduling with Differential QoS Requirements in OFDMA Systems

Fair and efficient scheduling is a key issue in cross-layer design for wireless communication systems, such as 3GPP LTE and WiMAX. However, few works have considered the multiaccess of the traffic with differential QoS requirements in wireless systems. In this paper, we will consider an OFDMA-based wireless system with four types of traffic associated with differential QoS requirements, namely, minimum reserved rate, maximum sustainable rate, maximum latency, and tolerant jitter. Given these QoS requirements, the traffic scheduling will be formulated into a cross-layer optimization problem, which is convex fortunately. By separating the power allocation through the waterfilling algorithm in each user, this problem will further reduce to a kind of continuous quadratic knapsack problem in the base station which yields low complexity. It is then demonstrated that the proposed cross-layer method cannot only guarantee the application layer QoS requirements, but also minimizes the integrated residual workload in the MAC layer. To further enhance the ability of QoS assurance in heavily loaded scenario, a call admission control scheme will also be proposed. The simulation results show that the QoS requirements for the four types of traffic are guaranteed effectively by the proposed algorithms.
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Abstract: Task scheduling plays the key role systems in cloud computing. Scheduling of tasks cannot be

Abstract: Task scheduling plays the key role systems in cloud computing. Scheduling of tasks cannot be

Task Scheduling plays a key role in the Cloud Computing system. Scheduling of the task cannot be done on basis of single criteria. It is regulations and rules that can term as an agreement between users and providers of cloud. This agreement is nothing else simply the quality of service that the user or client requires. Providing good quality of the services to the users or clients according to the agreement it is a decisive task for the providers at the same time there is large number of tasks running at the side of provider’s. The task scheduling problem can be viewed or seen as the finding or searching an optimal mapping of set of subtasks of different tasks over the available set of the resources (e.g. processors/computer machines) hence can be achieved or attained the desired goals for tasks. Scheduling is a method or procedure by which threads, processes and data flows are given access to system resources. Scheduling is the fundamental operating system function, almost all computer resources are scheduled before use. The idea of multiprogramming is relatively simple, if a job is waiting for an I/O request, then the CPU switches from that job to another job, so that it always busy in multiprogramming[3].
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Optimized Task Scheduling in Cloud Computing: A Survey

Optimized Task Scheduling in Cloud Computing: A Survey

PeiYun Zhang et al (2018) [1] proposed two phase systems to boost the undertaking planning execution and limit non sensible planning for distributed computing. To order the undertakings that are in the line a classifier called Bayes classifier's standard is utilized. To apportion the undertaking specifically assets numerous quantities of virtual machines are made with various execution abilities. The complete procedure of this two-organize grouping is characterized into four phases task order arrange, Virtual Machine (VM) coordinating stage, prepared lining stage and holding up stage. In the principal arrange, kind of VM and their numbers will be broke down dependent on verifiable assignment booking data with the assistance of Bayes classifier. In coordinating stage, a reasonable VM will be relegated with task. On the off chance that the all out number of line components is lesser than the all out limit then new assignment will be put away in prepared line and if the complete number is higher than all out limit than the undertaking will be distributed in holding up line.
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Angel-Agent Based Scheduling for Real Time Tasks in Virtualized Clouds

Angel-Agent Based Scheduling for Real Time Tasks in Virtualized Clouds

The essential concept of cloud computing system is task scheduling. This algorithm aims at reducing the make span of jobs with lowest resources capably. Scheduling algorithms depends on the type of task to be scheduled. The scheduling algorithm gives better executing efficiency and it maintains the load balancing of systems. The cloud efficiency is depends on the task scheduling algorithm. There are different types of task scheduling. Some are cloud service scheduling, user level scheduling, static and dynamic scheduling, heuristic scheduling, real time scheduling, work flow scheduling etc.
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Scheduling Algorithms in Cloud Computing

Scheduling Algorithms in Cloud Computing

Genetic Algorithm and Simulated Annealing are two other general methods in heuristic approach which is used to perform near optimal scheduling. In Genetic Algorithm approach [23] we perform four different operations, evaluation, selection, cross over and mutation. The initial population represents the possible mappings of the given task list on the available machines. Each job is represented as a vector in which each position of that vector represents a task in the task list. The value in each position represents the machine to which the task is mapped. Each job represents a chromosome. Every chromosome has a fitness value indicating the overall execution time of all the tasks (makespan) which is formed from the mapping of tasks to resources constituting that chromosome and it is selected such that it reduces makespan. This method uses past results with present results to get better possible mappings and survival of the fittest takes place.
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Cloud Computing Online Scheduling

Cloud Computing Online Scheduling

Cloud scheduling is categorized at user level and system level [3]. user level scheduling deals with problems raised by service provision between providers and customers [13]. The system level scheduling handles resource management [2, 12]. A novel approach of heuristic-based request scheduling at each server, in each of the geographically distributed datacenters, to globally minimize the penalty charged to the cloud computing system is proposed in [14]. Scheduling based genetic algorithm is proposed in [15, 16]. This algorithms optimize the energy consumption, carbon dioxide emissions and the generated profit of a geographically distributed cloud computing infrastructure. The QoS Min-Min scheduling algorithm is proposed in [17]. Min-min is heuristic used for batch mode scheduling. This enables batch heuristics to know about the actual execution times of a larger number of tasks. Join-shortest-queue (JSQ) and Join-Idle-Queue (JIQ) assign jobs to processors to reduce average queue length of jobs at each processor [18]. Task scheduling in the cloud should decide optimal number of systems required in the cloud and allocate the tasks in an efficient way so that the total cost is minimized.
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TASK SCHEDULING IN CLOUD COMPUTING

TASK SCHEDULING IN CLOUD COMPUTING

Abstract: Recently, there has been a dramatic increase in the popularity of cloud computing systems that rent computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same physical infrastructure. 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. One of the goals is to use the resources efficiently and gain maximum profit. Scheduling is a critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud computing system. So scheduling is the major issue in establishing Cloud computing systems. The scheduling algorithms should order the jobs in a way where balance between improving the performance and quality of service and at the same time maintaining the efficiency and fairness among the jobs. This paper aims at studying various scheduling methods. A good scheduling technique also helps in proper and efficient utilization of the resources. Many scheduling techniques have been developed by the researchers like GA (Genetic Algorithm), PSO (Particle Swarm Optimization), Min-Min, Max-Min, Priority based Job Scheduling Algorithm . This paper reviews certain papers on resource management and job scheduling in cloud computing.
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Cloud Scheduling   A Survey

Cloud Scheduling A Survey

Service-orientation is the reference model for Cloud computing systems. It is the logical way of organizing software systems to provide end users or other entities distributed over the network with services through published and discoverable interfaces. It introduces two important fundamental concepts, Quality of Service (QoS) and Software as a Service (SaaS) for Cloud computing. Quality of Service identifies response time, security attributes, transactional integrity, reliability, scalability, and availability as performance metrics to evaluate the behavior of a service from different perspectives. The SaaS approach allows the delivery of complex business processes and transactions as a service, while allowing applications to be composed on the fly and services to be reused from everywhere by anybody.
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A Cross Breed Scheduling Approach to Minimize Energy consumptions at Green Cloud Computing

A Cross Breed Scheduling Approach to Minimize Energy consumptions at Green Cloud Computing

In this manner, datacenters dependably keep up the dynamic servers as per current interest, which brings about low vitality utilization. Second element is Multi- occupancy. Utilizing multi tenure approach, the same server permits the smoothing of the general top interest which can minimize the requirement for additional foundation and results in more noteworthy vitality reserve funds. Third variable is Server Utilization. When all is said in done, on reason operations run at 5 to 10 percent of normal usage while cloud may achieve 40 to 70 percent use. What's more, the last component is Data Center Efficiency. The Green Cloud needs a decent advertising mark for IT association to be green and expecting to decrease the vitality cost, administrative standard for lessening the carbon emanation and enhance the usage [20].
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Energy Efficient Scheduling Algorithm for Cloud Computing Systems Based on Prediction Model

Energy Efficient Scheduling Algorithm for Cloud Computing Systems Based on Prediction Model

pollution of environments. Authors proposed ‘green cloud’ approach to reduce the power consumption. B. Dinh et al. [9] addressed the solution to reduce the power consumption with the help of prediction algorithms. Authors proved, with the help of machine learning algorithms cloud environments can increase the efficiency of optimization. The proposed lgorithm addressed the solution for energy efficiency. A.T. Makaratzis et al. [10] proposed algorithm for energy model. The whole work developed in simulation tool. Author addressed the simulation framework to solve the power consumption problem. The simulation results proves, effective scheduling algorithm gives the solution for power consumption problems. D. Mehiar et al. [11] proposed an energy efficient algorithm for resource allocation in cloud. This algorithm has taken the advantages of scheduling algorithm to allocate the resource to the cloud applications. Z. Wei et al. [12] proposed a three dimensional virtual scheduling algorithm to reduce the power consumption. The main idea of the proposed system is, using scheduling algorithm with three parameters to allocate the right amount of resources.
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The Future Internet: Towards Convergence of Cognition, Cooperation, Cross-layer, Virtualization and Cloud-computing

The Future Internet: Towards Convergence of Cognition, Cooperation, Cross-layer, Virtualization and Cloud-computing

change IP layer to adapt for future requirements, but adapt various upper and lower layers protocols. With the multiplicity of Internet business and sharp expansion of user’s requirements, the coexistence of different heterogeneous network is formed under the drive of technology innovation and application requirement. Thus, today’s Internet can be seen as the logic combination of various network devices in different medium, frequency and space. To add a new application or service, the Internet needs to be modified correspondingly, which forms a messy structure of a forest of chimneys and severely restricts the sustaining development of Internet.
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An Efficient Fully Dynamic Algorithm to Optimize the Task Scheduling Activities in Cloud Systems

An Efficient Fully Dynamic Algorithm to Optimize the Task Scheduling Activities in Cloud Systems

Cloud computing is one of the most current emerging computing technologies and has received a lot of interest by the researchers now a days. It can be explained as on demand pay-per-use model which provide the software, information and resources in a shared mode as per the users’/clients’ requirements when the need for it [1]. It is evident that the human dependency has been focused on the cloud computing for the last decade. The most popular online gaming sites, document sharing, social networking, email hosting and business sites are moving to cloud environment from their traditional computing environment. Google, IBM, Microsoft, Yahoo, Apple and Amazon are the famous initiators in this field.
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A Brief Review of Scheduling Algorithms in Cloud Computing

A Brief Review of Scheduling Algorithms in Cloud Computing

Abstract— Cloud computing comes in focus development of grid computing, virtualization and web technologies. The cloud computing is a mingle of technologies where a large number of systems are connected in private or public networks. This technology offers dynamically scalable infrastructure for data, file storage, and application. Scheduling is a main task in a cloud computing environment. In cloud computing environment datacenters take care of this task. The selection of a exacting scheduling algorithm depends upon various factors like the parameter to be optimized (cost or time), quality of service to be provided and information available regarding various aspects of job. Workflow applications are the applications which need various sub-tasks to be executed in a particular fashion in order to complete the whole task. Various scheduling algorithms surveyed in this paper. The goal of cloud task scheduling is to achieve high system throughput and to assign various computing resources to applications. The Complexness of scheduling trouble increases with the size of the task and becomes very difficult to solve effectively. Min-Min algorithm is used to lessen the make span of tasks by assuming the task length. Keeping this in mind, cloud providers should achieve user satisfaction
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Task Scheduling in Cloud Computing: Review

Task Scheduling in Cloud Computing: Review

priority they are added to the list of task which is waiting for their turns in order to decreasing priority. Second phase, when the different processors becomes available for serving more, the highest priority subtask is selected from the list and assigned to the processor which is most suitable. So, here searching is narrowed down to small portions of solution space by using some other heuristics; greedy heuristics, thus it gives a better makespan. A drawback is that is not suitable for giving consistent results for heterogeneous computing systems.

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A Review on Task Scheduling in Cloud Computing

A Review on Task Scheduling in Cloud Computing

V. Nelson, V. Uma et al16] The resources held by a single cloud are usually limited and at peak period, the organization may not be able to give the guaranteed services due to insufficient provisioning of resources. So it is essential to organize cloud systems that complement each other such as to procure resources from other participating cloud systems. However, it is difficult to provide the right resources from various cloud providers because management policies and descriptions about various resources are different in each organization. Having these differences, it is hard to provide interoperability among them. Representing cloud environment through ontology can conceptualize common attribute among the various resources semantically. Considering this fact, we propose an Inter-cloud Resource Provisioning System (IRPS) in which the resources and tasks are described semantically and stored using resource ontology and the resources are assignedusing a set of inference rules and a semantic scheduler2.
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A survey for task scheduling in cloud computing

A survey for task scheduling in cloud computing

concept of cloud architecture and compares cloud computing with grid computingand aimed to pinpoint the challenges and issues of cloud computing and identified several challenges from the cloud computing adoption perspective. However, security and privacy issues present a strong barrier for users to adapt into cloud computing systems. Gajender Pal et al. (2014), provides a better understanding of the cloud computing and identifies important research issues in this burgeoning area of computer science. On demand or on pay per use of resource such as: network, storage and server these all facilities are provided by cloud computing through internet is called cloud computing. Although, cloud computing is facilitating the Information Technology industry, the research and development in this arena is yet to be satisfactory. GE Junwei and YUAN Yongsheng presents a genetic algorithm consider total task completion time, average task completion time and cost constraint. Compared with algorithm that only consider cost constraint (CGA) and adaptive algorithm that only consider total task completion time by the simulation experiment. Amit Agarwal and Saloni Jain (2014), presented a Generalized Priority algorithm for efficient execution of task and comparison with FCFS and Round Robin Scheduling. Algorithm should be tested in cloud Sim toolkit and result shows that it gives better performance compared to other traditional scheduling algorithm. Cloud is developing day by day and faces many challenges, one of them is scheduling. Scheduling refers to a set of policies to control the order of work to be performed by a computer system. A good scheduler adapts its scheduling strategy according to the changing environment and the type of task. Ekta Rani and Harpreet Kaur (Ekta Rani, 2017), followeda Raven Roosting Optimization Algorithm (RRO) is followed to light on the load balancing for task scheduling problems solution in cloud environment. Heterogeneity of birds, insects enroll in roosting. In raven Roosting, Roosts are information centers or can say servers and scrounge feature of common ravens inspired to solve problems. This technique is good enough to handle number of overloaded tasks transfer on Virtual Machines (VMs) by determining the availability of VMs capacity. Raven Roosting Optimization (RRO) random allocation of VMs to Cloudlets results huge change in makespan with respect to VM to which allocated.
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Fault tolerant based hyper heuristic algorithm for task scheduling in 
		cloud 

Fault tolerant based hyper heuristic algorithm for task scheduling in cloud 

The needs of job scheduling in cloud computing are load balance, quality of service, economic principles, best running time, throughput. With computing systems being shifted to cloud-based systems progressively, one of the main characteristics is that it works on a pay-as-you- use basis. Several studies attempted to define the scheduling problem on cloud systems as the workflow problem, which can be further classified into two levels: service-level (platform layer and static scheduling) and task-level (unified resource layer and dynamic scheduling). Different from grid computing, the user can install their programs on the virtual machines (VMs) and determine how to execute their programs on the cloud computing system. For these reasons, although both grid computing and cloud computing are heterogeneous, the key issues they face are very different. A good example is the cost and latency of data transfer on these environments. That is why some studies added more considerations to their definitions of scheduling on cloud. For instance, a couple of studies, used directed acyclic graph (DAG) to define the scheduling problem on cloud. The basic idea is to use the vertices of a DAG to represent a set of tasks and the edges between the vertices to represent the dependencies between the tasks.
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Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing

manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered [6]. A task is an activity that uses set of inputs to produce a set of outputs. In Cloud computing, user applications will run on virtual systems where distributed resources are allocated dynamically. Dynamic load-balancing mechanism has to allocate tasks to the processors dynamically as they arrive. Redistribution of tasks has to take place when some processors are overloaded. Every application is completely different in nature and independent where some require more CPU time to compute complex task, and some others may need more memory to store data. Different scheduling algorithms can be used depending on the type of the task to be scheduled. The scheduling algorithms can utilize better executing efficiency and maintain the load balancing of system. The efficiency of the cloud depends on the algorithms used for task scheduling.
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A Survey on Energy Aware Scheduling in Virtualized Clouds

A Survey on Energy Aware Scheduling in Virtualized Clouds

Cloud computing has recently received considerable attention, as a promising approach for delivering ICT services by improving the utilization of data centre resources. Energy safeguarding is a most important concern in cloud computing systems because it can bring several important benefits such as reducing operating costs, increasing system reliability, and prompting environmental protection. Meanwhile, power-aware scheduling approach is a promising way to achieve that goal. At the same time, many real- time applications, e.g., signal processing, scientific computing have been deployed in clouds. These applications necessitate an Energy- Aware Scheduling in Virtualized Clouds. In principle, cloud computing can be an inherently energy-efficient technology for ICT provided that its potential for significant energy savings that have so far focused on hardware aspects, can be fully explored with respect to system operation and networking aspects. In this backdrop this paper formalizes various energy aware scheduling strategies for virtualized clouds.
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