Mobile Big Data: Foundations, State of the Art, and Future Directions

Chang, Chii; Hadachi, Amnir; Srirama, Satish Narayana; Min, Mart

doi:10.1007/978-3-319-63962-8_46-1

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…The relevance of Big-data computing systems is visible in the number of studies devoted to the analysis of software applications for such environments [38]. In many cases the absence of well-known benchmarking tools has forced researchers to design their own application suites.…”

Section: Related Workmentioning

confidence: 99%

Memory Hierarchy Characterization of NoSQL Applications through Full-System Simulation

Colaso

Prieto

Herrero

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In this work, we conduct a detailed memory characterization of a representative set of modern datamanagement software (Cassandra, MongoDB, OrientDB and Redis) running an illustrative NoSQL benchmark suite (YCSB). These applications are widely popular NoSQL databases with different data models and features such as in-memory storage. We compare how these data-serving applications behave with respect to other well-known benchmarks, such as SPEC CPU2006, PARSEC and NAS Parallel Benchmark. The methodology employed for evaluation relies on state-of-the-art full-system simulation tools, such as gem5. This allows us to explore configurations unattainable using performance monitoring units in actual hardware, being able to characterize memory properties. The results obtained suggest that NoSQL application behavior is not dissimilar to conventional workloads. Therefore, some of the optimizations present in state-of-the-art hardware might have a direct benefit. Nevertheless, there are some common aspects that are distinctive of conventional benchmarks that might be sufficiently relevant to be considered in architectural design. Strikingly, we also found that most database engines, independently of aspects such as workload or database size, exhibit highly uniform behavior. Finally, we show that different data-base engines make highly distinctive demands on the memory hierarchy, some being more stringent than others.

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Section: Related Workmentioning

confidence: 99%

Memory Hierarchy Characterization of NoSQL Applications through Full-System Simulation

Colaso

Prieto

Herrero

et al. 2018

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

show abstract

“…However, it also raises a new challenge regarding the work assignment optimisation. Here, Figure 1 illustrates a Mobile Big Data acquisition [6] scenario that expresses the challenge.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Edge Process Migration for IoT in Heterogeneous Cloud-Fog-Edge Computing Environment

Chang,

Hadachi,

Srirama

2018

Preprint

Self Cite

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The latency issue of the cloud-centric IoT management system has motivated Fog and Edge Computing (FEC) architecture, which distributes the tasks from the cloud to the edge resources such as routers, switches or the IoT devices themselves. Specifically, mobile sensors of IoT system can also carry certain tasks for FEC. Considering the need of dynamic process migration from the mobile sensors to other resources when the mobile sensors unable to continue their tasks, the IoT system needs to provide a flexible mechanism that allows the mobile sensors dynamically migrate their tasks to the other FEC resource at runtime. However, it raises a question in what is the optimal approach when the mobile sensors intend to migrate their tasks to multiple heterogeneous FEC resource? In order to address the question, the authors propose REM scheme, which is capable of optimising the process migration decision. Further, in order to realise such a system and to validate the REM scheme, the authors have developed EPIoT host framework. Finally, the authors have implemented and have evaluated the REM scheme and framework, the results have shown that the REM scheme is capable of enhancing the performance of the process migration in heterogeneous FEC environment.

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Adaptive Edge Process Migration for IoT in Heterogeneous Fog and Edge Computing Environments

Chang

Hadachi

Srirama

2022

Research Anthology on Edge Computing Protocols, Applications, and Integration

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To address the latency issue of the cloud-centric IoT management systems, researchers have introduced fog and edge computing (FEC) architecture, which distributes the computational tasks and decision making from the distant cloud to the edge or near the edge of IoT network. Considering the need for dynamic process migration from the mobile sensors to other resources when the mobile sensors are unable to continue their tasks, the IoT system needs to provide a flexible mechanism that allows the mobile sensors to dynamically migrate their tasks to the other FEC resources at runtime. The paper proposes a resource-aware edge process migration (REM) scheme that is capable of optimising the process migration decision. To realise such a system and to validate the REM scheme, the authors have developed a framework called edge process-enabled internet of thing (EPIoT) host. The framework and the REM scheme are implemented and evaluated. The results have shown that the REM scheme is capable of enhancing the performance of the process migration in heterogeneous FEC environment.

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Mobile Big Data: Foundations, State of the Art, and Future Directions

Cited by 3 publications

References 53 publications

Memory Hierarchy Characterization of NoSQL Applications through Full-System Simulation

Memory Hierarchy Characterization of NoSQL Applications through Full-System Simulation

Adaptive Edge Process Migration for IoT in Heterogeneous Cloud-Fog-Edge Computing Environment

Adaptive Edge Process Migration for IoT in Heterogeneous Fog and Edge Computing Environments

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