A control‐theoretic approach towards joint admission control and resource allocation of cloud computing services

Dechouniotis, Dimitrios; Leontiou, Nikolaos; Αθανασόπουλος, Νικόλαος; Christakidis, Athanasios; Denazis, Spyros

doi:10.1002/nem.1889

Cited by 12 publications

(8 citation statements)

References 28 publications

(39 reference statements)

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“…Then RLS algorithm used these data sets to produce three LTI models for the week before the major earthquake, for the week after the earthquake and for both weeks together for each geographical area. All derived models are evaluated using the Best Fit Rate (BFR) [16], [17], that is a well-known metric of system identification process. In order to produced unbiased results, different data were used for the training and the evaluation process.…”

Section: Validation and Resultsmentioning

confidence: 99%

A Linear Modeling Approach on LTGP Signals and Seismic Activity

Dechouniotis¹,

Ifantis²

2018

IJCCMS

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Section: Validation and Resultsmentioning

confidence: 99%

A Linear Modeling Approach on LTGP Signals and Seismic Activity

Dechouniotis¹,

Ifantis²

2018

IJCCMS

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“…The interesting reader may refer to survey [28] for an extended analysis of control theoretic approaches on performance modeling and cloud elasticity. Close to DRUID-NET concepts, Dechouniotis et al [29] proposed Linear Parameter Varying (LPV) modeling of cloud applications combined with set-theoretic controllers to guarantee a feasible solution of the elasticity in cloud data centers, while Leontiou et al [30] derived fuzzy Takaki-Sugeno models and designed robust controllers to address simultaneously the problems of vertical and horizontal scaling, and load balancing with stability guarantee.…”

Section: Performance Modeling and Resource Allocation In Cloud And Edmentioning

confidence: 99%

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Dechouniotis

Αθανασόπουλος

Leivadeas

et al. 2020

Sensors

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The potential offered by the abundance of sensors, actuators, and communications in the Internet of Things (IoT) era is hindered by the limited computational capacity of local nodes. Several key challenges should be addressed to optimally and jointly exploit the network, computing, and storage resources, guaranteeing at the same time feasibility for time-critical and mission-critical tasks. We propose the DRUID-NET framework to take upon these challenges by dynamically distributing resources when the demand is rapidly varying. It includes analytic dynamical modeling of the resources, offered workload, and networking environment, incorporating phenomena typically met in wireless communications and mobile edge computing, together with new estimators of time-varying profiles. Building on this framework, we aim to develop novel resource allocation mechanisms that explicitly include service differentiation and context-awareness, being capable of guaranteeing well-defined Quality of Service (QoS) metrics. DRUID-NET goes beyond the state of the art in the design of control algorithms by incorporating resource allocation mechanisms to the decision strategy itself. To achieve these breakthroughs, we combine tools from Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory. DRUID-NET constitutes the first truly holistic, multidisciplinary approach that extends recent, albeit fragmented results from all aforementioned fields, thus bridging the gap between efforts of different communities.

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“…In specific, an optimization problem is formulated whose solution retrieves both the operating condition and the control strategy. This approach is less conservative than the multi-step approaches in the literature [7], [8].…”

Section: Vertical Scalingmentioning

confidence: 99%

Adaptive Resource Allocation for Computation Offloading

Avgeris

Dechouniotis

Αθανασόπουλος

et al. 2019

ACM Trans. Internet Technol.

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Although mobile devices today have powerful hardware and networking capabilities, they fall short when it comes to executing compute-intensive applications. Computation offloading (i.e., delegating resource-consuming tasks to servers located at the edge of the network) contributes toward moving to a mobile cloud computing paradigm. In this work, a two-level resource allocation and admission control mechanism for a cluster of edge servers offers an alternative choice to mobile users for executing their tasks. At the lower level, the behavior of edge servers is modeled by a set of linear systems, and linear controllers are designed to meet the system’s constraints and quality of service metrics, whereas at the upper level, an optimizer tackles the problems of load balancing and application placement toward the maximization of the number the offloaded requests. The evaluation illustrates the effectiveness of the proposed offloading mechanism regarding the performance indicators, such as application average response time, and the optimal utilization of the computational resources of edge servers.

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A control‐theoretic approach towards joint admission control and resource allocation of cloud computing services

Cited by 12 publications

References 28 publications

A Linear Modeling Approach on LTGP Signals and Seismic Activity

A Linear Modeling Approach on LTGP Signals and Seismic Activity

Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective

Adaptive Resource Allocation for Computation Offloading

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