An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

Chang, Yu Fan; Fan, Chih Tien; Sheu, Ruey-Kai; Jhu, Syuan Ru; Yuan, Shyan-Ming

doi:10.1002/dac.3401

Cited by 15 publications

(9 citation statements)

References 33 publications

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“…Author et al: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS computing platform [14]. Chang et al [15] adopted rough set theory to estimate the execution time of service request, so as to satisfy the deadline constraint on certain virtual machine (VM) with given resource. Deldari et al [16] divided the workflow into a number of clusters, and then adopted an extendable scoring approach to choose best cluster combinations to minimize the execution cost when considering the deadline constraint.…”

Section: Related Workmentioning

confidence: 99%

A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

et al. 2020

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Cloud computing has attracting more and more attention for its flexibility and economic benefits. To maintain the supply-demand relationship among different participants in cloud computing environment, the exchange of value is the inner drive. From the perspective of cloud service provider, its primary concern is to earn the profit, which can be obtained by finishing the tasks published from customers. In this paper, we consider each task consists of numbers of sub-tasks in the logical order, each sub-task corresponds to a type of service requests, which can be served in unique multi-server system. On this basis, we propose a profit maximization problem in the multistage multi-server queue systems, in which customers are served at more than one stage, arranged in a series structure. Moreover, a deadline constraint is taken into consideration, which demonstrates the maximum tolerance degree that the customers can wait. Therefore, how to configure the parameters in multistage multi-server queue systems to maximize profit on the premise of reducing the waiting times of customers is a critical issue for cloud service provider. To address this problem, we first discuss the probability distribution function of the waiting time for single multi-server system and multistage multi-server queue systems respectively, and then propose a profit maximization model under the deadline constraint. Due to the complexity of this model, the analytical solution can hardly be obtained, we study a heuristic method to search for the optimal solution. At last, a series of numerical simulations are implemented to describe the performance of the proposed profit maximization scheme, the results show that not only the profit can be maximized, but also the waiting time of customers have been reduced effectively. INDEX TERMS Cloud computing, deadline, queueing model, multi-server system, profit maximization, waiting time.

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Section: Related Workmentioning

confidence: 99%

A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

et al. 2020

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“…The ideal location search algorithm (Algorithm 1) computes the distance (w kl ) between nodes v k and v l (see lines [1][2][3][4][5]. In this context, our approach works in two phases.…”

Section: Algorithmsmentioning

confidence: 99%

“…Details of the first phase is discussed with the help of Algorithms 1 and 2. The ideal location search algorithm (Algorithm 1) computes the distance (w kl ) between nodes v k and v l (see lines [1][2][3][4][5]. Each edge in  between nodes v k and v l carries a nonnegative weight w kl ≥ 0 and if w kl = 0 nodes are not connected.…”

Section: Algorithmsmentioning

confidence: 99%

“…It takes O(N 1 logN 1 ) to sort locations in  in a descending order (line 1). Checking if a location meet end user's D max constraint, the time complexity is O(N 1 N 2 ) (lines [3][4][5][6][7][8]. The time complexity of calculating U QoS le is O(N 1 ) (line 9).…”

Section: Algorithmsmentioning

confidence: 99%

“…Cloud computing offers ubiquitous on-demand access to globally distributed resources. 3,4 The on-demand resource allotment policy of the cloud helps to enhance the utilization of resources and the responsiveness of the end users. 5,6 Cloud-based CDN (CCDN) blend the features of cloud such as flexible pricing model, virtualization techniques, and unlimited on-demand cloud storage with CDN features like content replication and routing.…”

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An eigenvalue‐based edge infrastructure for cloud‐based CDN

Sahoo

Turuk

2019

Int J Communication

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In today's dynamic video landscape, an end user needs services to be delivered to any devices anytime with less delay over the Internet. Now users' expectation has changed; they want faster time-to-market, cost reduction, and the ability to adjust according to the evolving requirements, which are a limit for the traditional server-based approach. The explosive growth of the internet multimedia application needs a new approach to content delivery to overcome the limitations of server-based techniques. Cloud-based content delivery networks (CCDNs) have recently started to emerge where contents are cached from the cloud storage and delivered through the distribution service to meet quality of services (QoS) of requested services. In this paper, we dealt with CCDN deployment problem and proposed a new eigenvalue-based edge infrastructure for a network service provider to serve the users with a variation on proximity interest concerning operational cost and user QoS satisfaction. The edge infrastructure designing is a two-step process: (a) ideal location search for placing edge server and (b) edge server placement and capacity provisioning. The performance of the proposed approach is appraised via modeling and simulation.Performance evaluation outcomes are exhibited to manifest the effectiveness and competitiveness of our approach when compared with existing algorithms.

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A fuzzy logic‐based method for solving the scheduling problem in the cloud environments using a non‐dominated sorted algorithm

Wakil

Badfar

Dehghani

et al. 2019

Concurrency and Computation

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Cloud computing as a new model of delivering IT services on the Internet has attained high attention recently. In this new paradigm, efficient service management causes the high quality of provided services. Scheduling as one of the most important duties of service management is a key problem in cloud computing that affects the total system performance. In most cases, the meta-heuristic methods are used for optimizing the scheduling issues instead of traditional methods. One of the influential evolutionary algorithms for optimizing the complicated problems is a non-dominated sorting particle swarm optimization (NSPSO) technique. In this paper, we propose a meta-heuristic technique using the NSPSO model for decreasing total cost and consumed total time. Furthermore, fuzzy set theory is applied to select the best solution.Simulation results have indicated that the efficiency of NSPSO is improved. In the many types of experiment, the proposed NSPSO algorithm was appropriate to keep a good spread of solutions and good converge. In addition, the diversity preserving mechanism applied in NSPSO has improvement against the other two investigated algorithms. KEYWORDScloud computing, multi-objective optimization, particle swarm optimization algorithm, task scheduling INTRODUCTIONCloud computing as an innovative distributed computing can provide dynamic resources, which are highly virtualized. 1,2 In addition, cloud providers deliver their services (such as software, hardware, platform, and storage) over the Internet. 3,4 Therefore, there are no shrink-wrapped boxes containing discs or hardware for users to buy. The cloud providers typically charge monthly recurring fees based on usage. 5-7 Many benefits, including the cost reduction and flexibility, make cloud computing to be superior technology in computing. 8 Due to the numerous benefits of cloud computing, many users have been attracted to use cloud-based services. 9,10 There are some issues in the cloud computing that must be addressed, such as resource discovery, 11,12 trust evaluation, 13-15 task, 16-18 resource allocation, 19-21 and so on. 22 Among them, task scheduling has a primary role in reaching the efficiency of distributed computing systems such as clouds and grids. [23][24][25] In the cloud systems, a task scheduler allocates the submitted tasks to proper resources for performing. 26-28 From the creation of cloud computing, task scheduling has gained significant attention, and many researchers have investigated many methods based on artificial intelligence-based algorithms to solve it. 29 It is responsible for finding potential cloud resources for satisfying the users' requests in the cloud environment. The main aim of task scheduler is decreasing the total cost, completion time and the computational complexity. 30,31 However, most of the existing research has focused on a single objective task scheduling in cloud systems. However, in real cases, more than one factor should be considered. 29 Since task scheduling is an NP-hard issue, many meta-heuristic algo...

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An agent‐based workflow scheduling mechanism with deadline constraint on hybrid cloud environment

Cited by 15 publications

References 33 publications

A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

A Profit Maximization Scheme in Cloud Computing With Deadline Constraints

An eigenvalue‐based edge infrastructure for cloud‐based CDN

A fuzzy logic‐based method for solving the scheduling problem in the cloud environments using a non‐dominated sorted algorithm

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