Autonomic scheduling of deadline-constrained bag of tasks in hybrid clouds

Peláez, Víctor; Campos, Antonio M.; García, Daniel; Entrialgo, Joaquín

doi:10.1109/spects.2016.7570526

Cited by 16 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different from other works regarding private clouds as resources pools, Wang et al [26] considered private clouds as multiple discrete physical machines, and designed a greedy heuristic to schedule tasks among these physical machines. The problem of scheduling BoT applications with varying tasks' runtimes was studied in [27]. A method for estimating tasks' runtimes was developed to update the scheduling results according to the estimated task durations.…”

Section: Related Workmentioning

confidence: 99%

A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

et al. 2019

View full text Add to dashboard Cite

This paper aims at scheduling bag-of-tasks (BoT) applications under budget constraints on hybrid clouds for minimizing the makespan. To solve this NP-hard problem, we propose a novel firefly algorithm (NFA) in which the evaluation of a firefly consists of two steps: (1) mapping a firefly to a scheduling solution (a task sequence); (2) calculating the solution's objective (its corresponding makespan). In the first step, different from the well-known ranked-order value (ROV) rule, we propose a distancebased mapping operator that relies on the distance between a firefly and the brightest one to determine the mapping relationship between a firefly and a solution. We use a probability model in which solutions corresponding to fireflies closer to the brightest one would have higher probabilities to inherit tasks from the current best solution. In this manner, these solutions can inherit more ''good genes'' hidden inside the current best solution to evolve into more high-quality solutions. In the second step, we employ an effective heuristic to evaluate solution objectives. We further develop a composite heuristic to generate the initial best solution, providing the proposed NFA with a good start. We also establish a new movement scheme such that fireflies distant from the brightest one can explore a wide range in the search space, whereas fireflies nearby the brightest one can search in a small neighborhood. Experimental results show that, by employing the above-mentioned strategies, NFA outperforms the standard firefly algorithm and the existing best algorithm, in terms of scheduling effectiveness and computational efficiency. Specifically, the distance-based mapping operator is verified to be both more effective and more efficient than the ROV rule. The composite heuristic is capable of generating a good initial solution, leading to the high quality of the final schedule. The movement scheme can further reduce the makespan of BoT applications.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Pelaez et al [29] argue that a scheduling framework should also take into account the variation of task execution during runtime. Therefore different approaches to estimate the task execution time during execution is used to constantly update the scheduling plan.…”

Section: Hybrid Cloudmentioning

confidence: 99%

“…The majority of the existing work considers both constraints and objectives whilst making a scheduling decision. For instance, some researchers have focused on optimising cloud usage by minimising the monetary cost while satisfying the deadline constraint [18,20,21,26,27,29,33,37,39,40,41,42], which aims to achieve the desired performance with the minimal cost. Researchers [24,32,46] have addressed the problem of performance maximisation with a budget constraint with the objective of obtaining with the maximum performance within a budgetary constraint.…”

Section: Literature Analysismentioning

confidence: 99%

“…This is because they can produce timely results although there is a trade-off against the optimality of solutions obtained. 14 X X X X X [19] X X X X X [15] X X X X X X [18] X X X X X X [13] X X X X X X [20] X X X X X X [21] X X X X X X [22] X X X X X X [36] X X X X X X [23] X X X X X X [34] X X X X X X X [27] X X X X X X X X X X [33] X X X X X [28] X X X X X X X [38] X X X X X X X [24] X X X X X X X [46] X X X X X X X [31,32] X X X X X X X X [26] X X X X X X X X [37] X X X X X X X X [39] X X X X X X X [42] X X X X X X [30] X X X X X X X X [25] X X X X X X X [29] X X X X X X X X [35] X X X X X X [40,41] X X X X X X X X X X [16] X X X X X X X X…”

Section: Current Trendsmentioning

confidence: 99%

See 1 more Smart Citation

A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Thai

Varghese

Barker

2018

Future Generation Computer Systems

View full text Add to dashboard Cite

Cloud computing has been widely adopted due to the flexibility in resource provisioning and on-demand pricing models. Entire clusters of Virtual Machines (VMs) can be dynamically provisioned to meet the computational demands of users. However, from a user's perspective, it is still challenging to utilise cloud resources efficiently. This is because an overwhelmingly wide variety of resource types with different prices and significant performance variations are available. This paper presents a survey and taxonomy of existing research in optimising the execution of Bag-of-Task applications on cloud resources. A BoT application consists of multiple independent tasks, each of which can be executed by a VM in any order; these applications are widely used by both the scientific communities and commercial organisations. The objectives of this survey are as follows: (i) to provide the reader with a concise understanding of existing research on optimising the execution of BoT applications on the cloud, (ii) to define a taxonomy that categorises current frameworks to compare and contrast them, and (iii) to present current trends and future research directions in the area.

show abstract

“…The authors proposed a greedy heuristic that dispatches tasks to available physical machines on the private cloud and assigns them to public clouds while there are no available ones. Reference [27] solved deadline-constrained BTSP with the variation of tasks' runtimes. Thus, the authors constructed a method to estimate tasks' runtimes so that the scheduling plan can be updated accordingly.…”

Section: Related Workmentioning

confidence: 99%

Scheduling Parallel Intrusion Detecting Applications on Hybrid Clouds

Zhang

Sun

et al. 2018

Security and Communication Networks

View full text Add to dashboard Cite

Recently, Parallel Intrusion Detection (PID) becomes very popular and its procedure of the parallel processing is called a PID application (PIDA). This PIDA can be regarded as a Bag-of-Tasks (BoT) application, consisting of multiple tasks that can be processed in parallel. Given multiple PIDAs (i.e., BoT applications) to be handled, when the private cloud has insufficiently available resources to afford all tasks, some tasks have to be outsourced to public clouds with resource-used costs. The key challenge here is how to schedule tasks on hybrid clouds to minimize makespan given a limited budget. This problem can be formulated as an Integer Programming model, which is generally NP-Hard. Accordingly, in this paper, we construct an Iterated Local Search (ILS) algorithm, which employs an effective heuristic to obtain the initial task sequence and utilizes an insertion-neighbourhood-based local search method to explore better task sequences with lower makespans. A swap-based perturbation operator is adopted to avoid local optimum. With the objective of improving the proposal’s efficiency without loss of any effectiveness, to calculate task sequences’ objectives, we construct a Fast Task Assignment (FTA) method by integrating an existing Task Assignment (TA) method with an acceleration mechanism designed through theoretical analysis. Accordingly, the proposed ILS is named FILS. Experimental results show that FILS outperforms the existing best algorithm for the considered problem, considerably and significantly. More importantly, compared with TA, FTA achieves a 2.42x speedup, which verifies that the acceleration mechanism employed by FTA is able to remarkably improve the efficiency. Finally, impacts of key factors are also evaluated and analyzed, exhaustively.

show abstract

Autonomic scheduling of deadline-constrained bag of tasks in hybrid clouds

Cited by 16 publications

References 17 publications

A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

A Novel Firefly Algorithm for Scheduling Bag-of-Tasks Applications Under Budget Constraints on Hybrid Clouds

A survey and taxonomy of resource optimisation for executing bag-of-task applications on public clouds

Scheduling Parallel Intrusion Detecting Applications on Hybrid Clouds

Contact Info

Product

Resources

About