Transitions for Increased Flexibility in Fog Computing: A Case Study on Complex Event Processing

Luthra, Manisha; Koldehofe, Boris; Steinmetz, Ralf

doi:10.1007/s00287-019-01191-0

Cited by 6 publications

(7 citation statements)

References 31 publications

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“…Let us consider a continuous query 3 to detect that a road section on a crossing is congested, as seen in Fig. 1b.…”

Section: Case Study: Iot Traffic Control Applicationmentioning

confidence: 99%

“…The mapping of the operators to these resources is done through an OP mechanism, which must account for the QoS_DEMAND specified within the query. As part of the query specification 3 , these demands such as low latency can be specified according to the users' requirements.…”

Section: Case Study: Iot Traffic Control Applicationmentioning

confidence: 99%

“…The fulfillment of QoS demands, however, is only feasible under limited changes in environmental conditions. For instance, most existing work [12,13,4,33,9,3] builds on stationary networks. Approaches considering dynamic changes, e.g., in the cause of mobility, introduces (i) redundancy by means of duplication [6] or checkpointing [59], (ii) placement update at regular intervals [8], or (iii) operator migrations when changing the placement [60][61][62]40].…”

Section: Operator Placement and Migrationmentioning

confidence: 99%

“…Yet, the proposed system is not restricted to sliding windows and can be applied to other types of windows such as tumbling windows 3. The query is specified in the AdaptiveCEP query language written in Scala[19].…”

mentioning

confidence: 99%

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TCEP: Transitions in operator placement to adapt to dynamic network environments

Luthra

Koldehofe

Danger

et al. 2021

Journal of Computer and System Sciences

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Distributed Complex Event Processing (DCEP) is a commonly used paradigm to detect and act on situational changes of many applications, including the Internet of Things (IoT). DCEP achieves this using a simple specification of analytical tasks on data streams called operators and their distributed execution on a set of infrastructure. The adaptivity of DCEP to the dynamics of IoT applications is essential and very challenging in the face of changing demands concerning Quality of Service. In our previous work, we addressed this issue by enabling transitions, which allow for the adaptive use of operator placement mechanisms. In this article, we extend the transition methodology by optimizing the costs of transition and analyzing the behavior using multiple operator placement mechanisms. Furthermore, we provide an extensive evaluation on the costs of transition imposed by operator migrations and learning, as it can inflict overhead on the performance if operated uncoordinatedly.

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“…Let us consider a continuous query 3 to detect that a road section on a crossing is congested, as seen in Fig. 1b.…”

Section: Case Study: Iot Traffic Control Applicationmentioning

confidence: 99%

Section: Case Study: Iot Traffic Control Applicationmentioning

confidence: 99%

Section: Operator Placement and Migrationmentioning

confidence: 99%

mentioning

confidence: 99%

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TCEP: Transitions in operator placement to adapt to dynamic network environments

Luthra

Koldehofe

Danger

et al. 2021

Journal of Computer and System Sciences

Self Cite

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“…Also, Fog computing expand the same capability in the middle at edge gateways. In another word, fog computing provides the closed resources to many services, which cannot be realized with alternative strategies [8,9].…”

Section: Introductionmentioning

confidence: 99%

DLBS: Decentralize Load-Balance Scheduling Algorithm for Real-Time IoT Services in Mist Computing

Refaat¹,

A.Mead²

2019

IJACSA

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Internet of Things (IoT) has been industrially investigated as Platforms as a Services (PaaS). The naive design of these types of services is to join the classic centralized Cloud computing infrastructure with IoT services. This joining is also called CoT (Cloud of Things). In spite of the increasing resource utilization of cloud computing, but it faces different challenges such as high latency, network failure, resource limitations, fault tolerance and security etc. In order to address these challenges, fog computing is used. Fog computing is an extension of the cloud system, which provides closer resources to IoT devices. It is worth mentioning that the scheduling mechanisms of IoT services work as a pivotal function in resource allocation for the cloud, or fog computing. The scheduling methods guarantee the high availability and maximize utilization of the system resources. Most of the previous scheduling methods are based on centralized scheduling node, which represents a bottleneck for the system. In this paper, we propose a new scheduling model for manage real time and soft service requests in Fog systems, which is called Decentralize Load-Balance Scheduling (DLBS). The proposed model provides decentralized load balancing control algorithm. This model distributes the load based on the type of the service requests and the load status of each fog node. Moreover, this model spreads the load between system nodes like wind flow, it migrates the tasks from the high load node to the closest low load node. Hence the load is expanded overall the system dynamically. Finally, The DLBS is simulated and evaluated on truthful fog environment.

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ProgCEP

Luthra

Koldehofe

2019

Proceedings of the 2nd International Workshop on Distributed Fog Services Design

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Transitions for Increased Flexibility in Fog Computing: A Case Study on Complex Event Processing

Cited by 6 publications

References 31 publications

TCEP: Transitions in operator placement to adapt to dynamic network environments

TCEP: Transitions in operator placement to adapt to dynamic network environments

DLBS: Decentralize Load-Balance Scheduling Algorithm for Real-Time IoT Services in Mist Computing

ProgCEP

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