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Cost Efficient Scheduling Through Auction Mechanism in Cloud Computing

Cost Efficient Scheduling Through Auction Mechanism in Cloud Computing

running cost in regards execution time and utilization. However, the auction strategy does not include a user demand as part of their auction factor. To address the user demand issue, the effective allocation algorithm that uses auction principles has been introduced in [5]. Their work achieves a great improvement compared to the traditional auction mechanism. They proposed two combinatorial auction-based allocation techniques where considered the user demand in a particular request and the greedy extension mechanism. Their results illustrate that the proposed combinatorial auction- based algorithm evidently beat the fixed-price strategy not only in term of increasing the providers’ income but also in improving resource utilization and allocation efficiency. Another auction mechanism which uses the greedy combinatorial-auction mechanism is introduced in [17] to solve the resource allocation issue. They utilized the price group that represents truthfulness in the auction process. The simulation results show that the auction mechanism indicate a good improvement in performance than the fixed price mechanisms in terms of both users’ and providers’ utility. On the other hand, the scheduling algorithm needs to consider the suitable compute matching between demand and supply in heterogeneous environment. Dynamic pricing reverse auction mechanism is designed in order to achieve an efficient allocation of Cloud resources [8]. In their model, the bidders choose the best resource which offers the shortest turnaround time and lower completion cost. The provider who offers lowest price is the winner. At the end of each auction, the Cloud providers update their products price based on the trade situation. The experimental results show that the dynamic pricing auction accomplished higher provider’s revenue and lower financial cost for users while improving the resources utility.
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Study on Combinatorial Auction Mechanism for Resource Allocation in Cloud Computing Environment

Study on Combinatorial Auction Mechanism for Resource Allocation in Cloud Computing Environment

36 | CATNETS by Eymann et al. [34 –37] compares the decentralized Catallactic approach with the centralized auction-based approach for resource allocation in Grid computing environment. Although the authors concluded that the decentralized approach fits to the Grid environment in terms of scalability, hereafter only the centralized approach is focused as it is the main concern of this thesis. The centralized approach employs periodical, combinatorial, double-sided, sealed-bid, K-pricing auctions. CATNETS divides the trading into two layers: a service market and a resource market. In the service market, the buyers are Complex Service Agents on behalf of the end-users, the sellers are Basic Service Agents, and the goods are services like PDF generation service. In the resource market, the buyers are Basic Service Agents, the sellers are Resource Service Agents on behalf of the owners, and the goods are resources like CPU/memory/storage/etc. In both markets, the auctioneer is an independent entity. The trading procedure is (1) a Resource Service Agent asks for his resource as seller in the resource market, (2) a Basic Service Agent asks for his service as seller in the service market, (3) a Complex Service Agent bids for a bundle of services as a buyer in the service market, (4) the auctioneer of the service market determines the winners, (5) the winning Basic Service Agent bids for a bundle of resources as a buyer in the resource market, (6) the auctioneer of the resource market determines the winners, (7) the winning Basic Service Agent uses the resources to provide his service to the winning Complex Service Agent, and (8) the winning Complex Service Agent uses the service. Unlike former literatures, CATNETS’ scheme is much likely to fit the cloud computing environment as its Resource/Basic/Complex Services respectively correspond to the IaaS/PaaS/SaaS layers in the cloud. Nonetheless, the CATNETS’ combinatorial auction model lacks the ability to deal with a workflow-oriented application which consists of multiple services running not at the same time.
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An Efficient Auction Mechanism in Cloud Computing For Computing Jobs with Soft Deadlines

An Efficient Auction Mechanism in Cloud Computing For Computing Jobs with Soft Deadlines

They do not provide any performance guarantee on the competitive ratio for committed scheduling. Navendu et al. design a truthful allocation and pricing mechanism for computing jobs with deadlines, but restrict attention to the offline setting. Construct online mechanisms for pre-emptive scheduling with deadlines. The mechanism is truthful and achieves a constant competitive ratio.

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An Auction Mechanism for a Cloud Spot Market

An Auction Mechanism for a Cloud Spot Market

Several authors have presented strategies for customers to utilize Amazon spot instances (cost-)effectively [12–16]. However, as of yet a limited amount of work has been conducted that focuses on the design of auction mechanisms to the benefit of cloud providers, and the associated algorithms for allocating resources and capacity planning to maximize the provider’s revenue. The prob- lem of dynamically allocating resources to different spot markets in order to maximize a cloud provider’s revenue has been investigated by Zhang et al. [7]. Danak and Manno [6] present a uniform-price auction for resource allocation that suits the dynamic nature of grid systems. Mihailescu and Teo [17] inves- tigate Amazon EC2’s spot market as a case in a federated cloud environment. They argue that spot pricing used by Amazon is truthful only in a market with a single provider, and show that rational users can increase their utility by being untruthful in a federated cloud environment. Recently, Zaman et al. have in- vestigated the applicability of combinatorial auction mechanisms for allocation and pricing of VM instances in cloud computing [18].
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Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing

Auction Mechanisms Toward Efficient Resource Sharing for Cloudlets in Mobile Cloud Computing

Individual rationality ensures that a buyer is never charged more than its bid, while a seller is paid not less than its ask. Budget balance requires that the auctioneer, which acts as an intermediate agent between buyers and sellers, hosts and runs the auction without a deficit. Truthfulness is essential to resist market manipulation and ensure auction fairness. An auction mechanism is truthful (also known as incentive compatible) if revealing the private valuation truthfully is always the weakly dominant strategy for each participant to receive an optimal utility, irrespective of what strategies other participants are taking. There are various definitions of efficiency in economics from different perspectives, among which allocative efficiency is a main type that aims to maximize social welfare, i.e., the sum of valuations of the buyers who receive their desired commodities. In this paper, we are particularly interested in double auction, in which buyers and sellers submit to an auctioneer their bids and asks, respectively. For such bilateral trade, a mechanism is efficient if whenever a buyer’s bid is greater than the seller’s ask, the corresponding commodity is allocated to the buyer [4]. In addition to the above economic properties, computational efficiency ensures that the auction outcome is tractable with a polynomial time complexity, which is important to enable feasible implementation.
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Efficient Task Scheduling using Load Balancing in Cloud Computing

Efficient Task Scheduling using Load Balancing in Cloud Computing

In the proposed approach, Pareto distribution instead of random initialization. If random distributions are used, more time will be taken to converge and sometime enforces the convergence by iteration but enforcing of convergence will increase the computation and execution time therefore does not meet the deadline condition. So, task initialization is an important task as defined in this paper. Another thing represented in these graphs and tables is that PSO_GWO performs better in comparison to BAT for reduction of cost and time because of the random crossover.
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Systematic Review of Energy efficient Scheduling Techniques in Cloud Computing

Systematic Review of Energy efficient Scheduling Techniques in Cloud Computing

Now a days cloud computing is a promising computing paradigm which is to support virtualization, scalable resource utilization, para-virtulizaion and provide services such as Infrastructure as a Service, Software as a Service, Platform as a Service. Computer scientist predicting that cloud system is next generation operating system. Google engineers say a magnificent sentence maintains thousands of servers, warned that if power consumption continues to increase, power cost can easily overtake hardware cost by a large margin [2]. Consumer of cloud only needs internet connection. The burden of purchasing a new license copy, installing an application, update that copy monthly all burden can be removed, if users are cloud utilize [1],[6],[7]. Cloud can easily provide all these facilities. Cloud computing is open source for cloud developers [1]. The development of hypervisors Xen, KVM, VMware, Virtual box, Eucalyptus [3] etc. triggering development of commercial and Open source Cloud environment. It can offer services on base of energy, power Pay-per use model. In office environments, computers, Monitors account for highest energy consumption after lighting. Power dissipation is also a major concern in portable battery operated devices that have rapidly increased [2]. Green computing is a new inclination, trend for high end computer. For example, a 360 –Tflops supercomputer (such as IBM Blue Gene/L) with conventional processor requires 20MW to operate which is approximately equal to sum of 22,000 US households power consumption [15], [16], [14]. It is 0.5 % of world total electricity usage [14], [17].
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Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Time and Resource Efficient Task Scheduling in Cloud Computing Environment

Abstract - Cloud computing has become a new age technology that has got huge potentials in enterprises and markets. Cloud Computing has Large Scale Distributed Infrastructure which is accessible and scalable infrastructure. Cloud computing provides a pay as you go model in which the user has to pay for the services he uses. However one of the major challenges in cloud computing is related to optimizing the resources being allocated. Because of the uniqueness of the model, resource allocation should be performed with the objective of minimizing the costs associated with it. This optimized use of cloud can only be done by efficient and effective algorithm to select the best resources. In this paper, the Task Based allocation of resources is used to minimize the makespan of the cloud system and also to increase the resource utilization. The simulation is done using CloudSim and results show that TBA algorithm reduces the makespan, execution time and cost as compared to Random Algorithm and FCFS algorithm.
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Energy-efficient Scheduling Policy for Collaborative Execution in Mobile Cloud Computing

Energy-efficient Scheduling Policy for Collaborative Execution in Mobile Cloud Computing

In this paper, we consider the collaborative application execution between the mobile device and the cloud by task offloading to conserve the energy consumed by the mobile device. Specifically, each task can be executed on the mobile device or offloaded to the cloud for execution. We aim to develop the energy-efficient task scheduling policy to conserve the energy on the mobile device under a Markovian stochastic channel. Mathematically, we model the minimum-energy task scheduling problem as a constrained stochastic shortest path problem on a directed acyclic graph. Then, we adopt the classical “LARAC” (Lagrangian Relaxation Based Aggregated Cost) algorithm to obtain the approximate solution of this constrained optimization problem. We show, via simulations with inputs from existing measurement data that, a one-climb offloading policy (i.e., the execution only migrates once from the mobile device to the cloud if ever) is optimal under the Markovian stochastic channel. Moreover, compared to standalone mobile or cloud execution, the collaborative task execution can significantly save the energy on the mobile device, thus prolonging its lifetime.
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Review on Improved Energy Efficient job scheduling in cloud computing

Review on Improved Energy Efficient job scheduling in cloud computing

Abstract : Cloud computing has offered services related to utility aligned IT services. Reducing the schedule length is considered as one of the significant QoS need of the cloud provider for the satisfaction of budget constraints of an application. Task scheduling in a parallel environment is one of the NP problems, which deals with the optimal assignment of a task. To deal with the favorable assignment of some task, task scheduling is considered as one of the NP problem. In this research work the jobs are distributed in a centralized environment. In Centralized environment every job request is forwarded to a central server. The central server passed the jobs to sub servers that are present with in the area of request. This has been performed by using distance formula. In our research work we reduce the energy consumption by each sub-server and it is possible by using formation of feedback queue. Job scheduling has been optimized on the basis of priority by using genetic algorithm Fuzzy logic also used for classification of the jobs to decide which job has been allotted to the system. Metrics namely, SLR, CCR (Computation Cost Ratio) and Energy consumption are used for the evaluation of the proposed work. All the simulations will be carried out in Cloud sim environment.
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An Efficient Data Locality Driven Task Scheduling Algorithm for Cloud Computing

An Efficient Data Locality Driven Task Scheduling Algorithm for Cloud Computing

Here we use the task scheduling algorithm which is used first for the balancing the load on cluster and then reduce the performance cost. We consider scheduling a set of independent tasks on a homogeneous platform. On one hand, as input blocks are fixed-size, we assume that data-local tasks take identical constant local cost. On the other hand, as a larger remote task number will cause a higher network contention, remote cost is increased when the remote task number become larger. A job is not completed until all tasks are finished. In addition, we take account of cluster workload: at the start time, if most servers are idle, the cluster is under loaded; in an overloaded cluster, many servers can not be idle in a short time. Base on these assumptions, our goal is to find an allocation strategy that minimizes the job completion time. Load Balancing is used to make sure that none of our existing resources are idle while others are being utilized. To balance load distribution, we can migrate the load from the source nodes which have the surplus workload to the comparatively lightly loaded destination nodes. While balancing load we take care of data, we schedule the task on the server where the data required for that server is present.
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A Survey Based on Secure  and Efficient  Task Scheduling Technique For Cloud Computing

A Survey Based on Secure and Efficient Task Scheduling Technique For Cloud Computing

• Dynamic task scheduling in grid computing using prioritized round robin algorithm:-a novel grid scheduling heuristic that adaptively and dynamically schedules task without requiring any prior information on the workload of incoming tasks. This models the grid system in the form of a state – transition diagram with job replication to optimally schedule jobs. This algorithm uses prediction information on processor utilization. In this algorithm they uses concept of job replication that is, a job can be replicated to other resource if that resource completes execution of current job than the resource it is currently allocated. This algorithm uses two types of queue namely, Waiting Queue and Execution Queue. This approach is based on exploiting information on processing capability of individual grid resources and applying replication on tasks assigned to the slowest processors. The approach facilitates replication of tasks, and also assigned to execute on slower machines, on machines with higher processing capacity. In this approach the communication cost are ignored. Experimental results show the better performance of this approach compared to traditional round robin algorithm.
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Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm

Efficient Cloud Computing Scheduling: Comparing Classic Algorithms with Generic Algorithm

The input can be explained as directed acyclic graph called tasks graph of G=(V,E) [9]. Each node is a member of V set, and it shows a task unit of the program. The weight of these nodes determines execution time of related task unit. Also, this graph involves a set of edges, E, showing prerequisite relations among task units. When there is an edge in the form of (ti,tj) , tj can not initiate the executing until ti completes its execution . These edges are weighted, and the weight of each edge shows communication cost and sending the message between two task units. This cost of time is considered when two related task units are executed in different processors. If the processors are same, then communication cost will be Zero.
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Tape Cloud: Scalable and Cost Efficient Big Data Infrastructure for Cloud Computing

Tape Cloud: Scalable and Cost Efficient Big Data Infrastructure for Cloud Computing

FUSE [17] is a framework to help develop customized file system. FUSE module has been officially merged into the Linux kernel tree since kernel version 2.6.14. FUSE provides 35 interfaces to fully comply with POSIX file operations. We design a file system using FUSE used at different tiers in the architecture. The file system is monolithic but logi- cally distributed and staged based on functionality as shown Figure 7. Figure 8 provides a block representation of the filesystem which is an important part of the middleware. The collection servers (the PUT-Collection servers and GET- Collection servers) implement modules which acquire client data to be written to or retrieved from tapes. Based on client specific policies, the data to be stored on tapes is encrypted and segmented. Each of the segments are identified and accounted in local databases. Similarly, to retrieve data from tapes, the filesystem queries the local databases and requests particular blocks from tapes and converts it to the pristine data. The Tape Interface Machines (TIM) are networked to the collection servers and blocks of data are sent and received via high speed connections. The load balancing server manages a workload based scheduling system in order to efficiently distribute data to various TIMs to avoid IO bottle necks. The filesystem is highly customizable in the sense, data from clients can be blocked and stored based on preference chosen by the clients. For example, video surveillance data, should the client be able to obtain data by the hour, must be handled differently as compared to be able to obtain data by days. So the collection servers are responsible to block these data in a manner easy for retrieval.
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Efficient Task Scheduling Over Cloud Computing with An Improved Firefly Algorithm

Efficient Task Scheduling Over Cloud Computing with An Improved Firefly Algorithm

The thesis proposes an improved firefly algorithm for solving the job scheduling problem in cloud computing. The approach has two aspects. The First approach deals with the development of the cloud framework for job scheduling while the second with the development of firefly algorithm which would be applied on the cloud so as to improve the job scheduling scheme. The number of resources should be timely processed “used by each cloud user based” on the time occupied and network access charges. The Job scheduling algorithm will not only focus on whether the total time required to complete the task is minimized, but also on the time cost for finishing the subtasks. This will prevent the misallocation of time and resources in the multiple tasks performed by the cloud users.
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Energy-Efficient Scheduling Scheme for Virtual Machines in Cloud Computing

Energy-Efficient Scheduling Scheme for Virtual Machines in Cloud Computing

Now a day’s cloud computing is a promising computing technology which is to support virtualization, scalable resource utilization and provide services such as IaaS, SaaS, PaaS. A Computer scientist predicting that cloud system is next generation operating system. Google engineers say a magnificent sentence maintains thousands of servers, warned that if power consumption continues to increase, power cost can easily overtake hardware cost by a large margin [1]. Cloud computing is open source for cloud developers. The development of hypervisors Xen, KVM [17], VMware ESXi [25], VirtualBox, Eucalyptus are triggering development of commercial and open source cloud environment. It can offer services on base of energy, power pay-per use model. Self- service, per usage metering, billing, elasticity, customization these are desired features of cloud [20],[23],[24]. There are different types of cloud that can subscribe by consumer depending upon needs such as public cloud, private cloud, community cloud, hybrid cloud. Let us consider a simple example in real life, when we plugging an electric instrument into electric outlet, we neither think how electric power generated, where generated, how it is gets to outlet this is
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Energy Efficient Scheduling Scheme for Virtual Machines in Cloud Computing

Energy Efficient Scheduling Scheme for Virtual Machines in Cloud Computing

Now a day’s cloud computing is a promising computing technology which is to support virtualization, scalable resource utilization and provide services such as IaaS, SaaS, PaaS. A Computer scientist predicting that cloud system is next generation operating system. Google engineers say a magnificent sentence maintains thousands of servers, warned that if power consumption continues to increase, power cost can easily overtake hardware cost by a large margin [1]. Cloud computing is open source for cloud developers. The development of hypervisors Xen, KVM [17], VMware ESXi [25], VirtualBox, Eucalyptus are triggering development of commercial and open source cloud environment. It can offer services on base of energy, power pay-per use model. Self- service, per usage metering, billing, elasticity, customization these are desired features of cloud [20],[23],[24]. There are different types of cloud that can subscribe by consumer depending upon needs such as public cloud, private cloud, community cloud, hybrid cloud. Let us consider a simple example in real life, when we plugging an electric instrument into electric outlet, we neither think how electric power generated, where generated, how it is gets to outlet this is
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PGA Scheduling with Cloud Computing

PGA Scheduling with Cloud Computing

Execution of a large task and then further spliting the task into small multiple sub tasks that execute simultaneously and makes the exacution more fast is the concept of parallel computing. Faster output can be drawn with the capability of dividing the large task into modules. It is more beneficial and effective for large number of computation under some constraints like time, space, complexity constraints etc. Another crucial step in handling the parallel computing is the assignment of a set of tasks in the parallel system environment and makes the makespan set the execution in such a way that the total execution time is minimized. Cloud computing is the challanging job in the task scheduling. Efficient execution of the job schedule in parallel environment with cloud schedular that takes the structure of the application and the performance characteristics is proposed in this algorithm. There are number of algorithms in solving the task scheduling have been proposed. Such type of problem is heuristic NP-Hard problem. Research proposes Parallel Genetic Algorithm (PGA) to schedule tasks parallely on hetrogeneous parallel environment using genetic approach. It is a heuristic technique. In this paper the scheduling of jobs is a major problem. It includes - mapping of the task optimally, search of an optimal parallel system and to set sequence of job execution. The mechanism for the optimization of all the these components of scheduling techniques with the help of cloud schedular and genetic approach is experimanted and its performance is evaluated in comparison with some scheduling algorithms like First Come First Serve (FCFS), Round Robin (RR) and optimal scheduling and optimized result be evaluated.
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Efficient Cost Scheduling algorithm with Load Balancing in a Cloud Computing Environment

Efficient Cost Scheduling algorithm with Load Balancing in a Cloud Computing Environment

Resource Scheduling is the important tasks in cloud computing environment. The cost scheduling algorithm which helps us to reduce the cost. Cloud resources that have been used are minimal. This algorithm works fine when the VM in the data center are idle. This algorithm does not work fine when all the VM in the data center's are busy and the new requests are in waiting state. This algorithm will not perform well if any fault occurs in VM and If user required two package then user have to process two VM's because each VM consist package so it increases the cost of users and service providers.
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Cost-aware task scheduling in cloud computing environment

Cost-aware task scheduling in cloud computing environment

Cloud computing environment consists of many heterogeneous resources called data centers, which include a number of hosts (servers) that have several characteristics, where each host has a number of VMs with various configurations (CPU, memory, bandwidth and storage). The requests will be sent to resources from the user by the service provider. The service provider serves these requests with efficient algorithms. The service provider executes tasks in virtual machines using scheduling algorithms that are available on resources. In this proposed approach, we used two clusters with different configuration based on speed: fast speed cluster and slow speed cluster. The clusters have several VMs with different costs. These VMs are independent and parallel processing. The user requests are represented by a group of independent tasks T=T1, T2, T3,…..Tn.
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