Towards Understanding Uncertainty in Cloud Computing Resource Provisioning

Tchernykh, Andrei; Schwiegelsohn, Uwe; Alexandrov, Vassil; Talbi, El‐Ghazali

doi:10.1016/j.procs.2015.05.387

Cited by 84 publications

(26 citation statements)

References 9 publications

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“…The LLC follows the similar concept as MPC, where the next action of the controller is determined using the projected behaviour of the system over a limited look-ahead horizon [93]. The key difference between MPC and LLC is that the former deals with the systems operating in continuous, whereas the latter deals in discrete input-output domains [34].…”

Section: Limited Lookahead Controller (Llc)mentioning

confidence: 99%

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

Ullah

Shen

et al. 2018

Cluster Comput

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The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the highlevel view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field.

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Section: Limited Lookahead Controller (Llc)mentioning

confidence: 99%

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

Ullah

Shen

et al. 2018

Cluster Comput

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“…Consequently, the system and application models used in such studies often make too optimistic assumptions about the performance of the infrastructure by assuming known and stable execution times for all tasks in the workflow application. In principle, scheduling under uncertainty is based on four approaches namely; reactive, fuzzy, stochastic, and robust or proactive [5]. This classification is with regard to the underlying mechanism used to model or manage uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…Scheduling systems that synthesize uncertainty for robustness are traditionally executed via either a probabilistic approach or a worst case scenario [5]. Hence, our proposal is based on the knowledge of the interval of uncertainty.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the estimation of performance of Cloud resources and task processing times need to account for these uncertainties in the theoretical model for scheduling workflows. The work by Tchernykh et al [5] also details the impact of uncertainty for IaaS Cloud resource provisioning. This requires countermeasures to be effected at the planning and scheduling stages to ensure deadlines are met.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment

Muhammad-Bello

Aritsugi

2018

IEICE Trans. Inf. & Syst.

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The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a costoptimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty. key words:handling resource performance uncertainty, deadlineconstrained workflow scheduling, IaaS cloud environment, robust scheduling, uncertain processing time

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“…Workflows can also be executed in Cloud as similar as in traditional clusters, as many workflow management services that allow the effective utilisation of the Cloud's elastic resources already exist [23]. Still, Cloud produces many additional challenges compared with the traditional clusters [2] caused by its on-demand elastic resource provisioning, dynamic starting of instances [20], and variant performance of virtual machines (VMs) during a time period [22].…”

Section: Introductionmentioning

confidence: 99%

A Simplified Model for Simulating the Execution of a Workflow in Cloud

Mathá

Ristov

Prodan

2017

Lecture Notes in Computer Science

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Abstract. Although simulators provide approximate, faster and easier simulation of an application execution in Clouds, still many researchers argue that these results cannot be always generalized for complex application types, which consist of many dependencies among tasks and various scheduling possibilities, such as workflows. DynamicCloudSim, the extension of the well known CloudSim simulator, offers users the capability to simulate the Cloud heterogeneity by introducing noisiness in dozens parameters. Still, it is difficult, or sometimes even impossible to determine appropriate values for all these parameters because they are usually Cloud or application-dependent. In this paper, we propose a new model that simplifies the simulation setup for a workflow and reduces the bias between the behavior of simulated and real Cloud environments based on one parameter only, the Cloud noisiness. It represents the noise produced by the Cloud's interference including the application's (in our case a workflow) noisiness too. Another novelty in our model is that it does not use a normal distribution naively to create noised values, but shifts the mean value of the task execution time by the cloud noisiness and uses its deviation as a standard deviation. Besides our model reduces the complexity of DynamicCloudSim's heterogeneity model, evaluation conducted in Amazon EC2 shows that it is also more accurate, with better trueness (closeness to the real mean values) of up to 9.2% and precision (closeness to the real deviation) of up to 8.39 times.

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Towards Understanding Uncertainty in Cloud Computing Resource Provisioning

Cited by 84 publications

References 9 publications

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

A Robust Algorithm for Deadline Constrained Scheduling in IaaS Cloud Environment

A Simplified Model for Simulating the Execution of a Workflow in Cloud

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