Efficient Resource Allocation for Multi-Tenant Monitoring of Edge Infrastructures

By relying on small sized and massively distributed infrastructures, the Edge computing paradigm aims at supporting the low latency and high bandwidth requirements of the next generation services that will leverage IoT devices (e.g., video cameras, sensors). To favor the advent of this paradigm, management services, similar to the ones that made the success of Cloud computing platforms, should be proposed. However, they should be designed in order to cope with the limited capabilities of the resources that are located at the edge. In that sense, they should mitigate as much as possible their footprint. Among the different management services that need to be revisited, we investigate in this paper the monitoring one. Monitoring functions tend to become compute-, storage- and network-intensive, in particular because they will be used by a large part of applications that rely on real-time data. To reduce as much as possible the footprint of the whole monitoring service, we propose to mutualize identical processing functions among different tenants while ensuring their quality-of-service (QoS) expectations. We formalize our approach as a constraint satisfaction problem and show through micro-benchmarks its relevance to mitigate compute and network footprints.

[1]  Carlos Pignataro,et al.  Service Function Chaining (SFC) Architecture , 2015, RFC.

[2]  Aiman Majid Nassar,et al.  The Internet of Things - A Survey , 2018, مؤتمرات الآداب والعلوم الانسانية والطبيعية.

[3]  Hongming Cai,et al.  Ubiquitous Data Accessing Method in IoT-Based Information System for Emergency Medical Services , 2014, IEEE Transactions on Industrial Informatics.

[4]  Endika Bengoetxea,et al.  Inexact Graph Matching Using Estimation of Distribution Algorithms , 2002 .

[5]  Mário Antunes,et al.  Towards IoT data classification through semantic features , 2017, Future Gener. Comput. Syst..

[6]  Lakhdar Sais,et al.  Reasoning from last conflict(s) in constraint programming , 2009, Artif. Intell..

[7]  Vipin Kumar,et al.  Algorithms for Constraint-Satisfaction Problems: A Survey , 1992, AI Mag..

[8]  Filip De Turck,et al.  Network Function Virtualization: State-of-the-Art and Research Challenges , 2015, IEEE Communications Surveys & Tutorials.

[9]  Christof Fetzer,et al.  A Novel Approach to QoS Monitoring in the Cloud , 2011, 2011 First International Conference on Data Compression, Communications and Processing.

[10]  Valérie Schafer Part of a Whole: RENATER, a Twenty-Year-Old Network within the Internet , 2015 .

[11]  Vincenzo Grassi,et al.  Optimal Operator Replication and Placement for Distributed Stream Processing Systems , 2017, PERV.

[12]  Ejaz Ahmed,et al.  A survey on mobile edge computing , 2016, 2016 10th International Conference on Intelligent Systems and Control (ISCO).

[13]  Eugene C. Freuder,et al.  Solving Dynamic Constraint Satisfaction Problems by Identifying Stable Features , 2009, IJCAI.

[14]  Mathis Obadia,et al.  A graph approach to placement of Service Functions Chains , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[15]  David E. Culler,et al.  BTrDB: Optimizing Storage System Design for Timeseries Processing , 2016, FAST.

[16]  Takao Asano,et al.  Edge-deletion and edge-contraction problems , 1982, STOC '82.

[17]  Levent Gürgen,et al.  Sharing user IoT devices in the cloud , 2014, 2014 IEEE World Forum on Internet of Things (WF-IoT).

[18]  Frédéric Desprez,et al.  Revising OpenStack to Operate Fog/Edge Computing Infrastructures , 2017, 2017 IEEE International Conference on Cloud Engineering (IC2E).

[19]  Rajkumar Buyya,et al.  Distributed data stream processing and edge computing: A survey on resource elasticity and future directions , 2017, J. Netw. Comput. Appl..

[20]  Jim Esch,et al.  Software-Defined Networking: A Comprehensive Survey , 2015, Proc. IEEE.

[21]  Jérôme François,et al.  A Holistic Monitoring Service for Fog/Edge Infrastructures: A Foresight Study , 2017, 2017 IEEE 5th International Conference on Future Internet of Things and Cloud (FiCloud).

[22]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[23]  Xavier Lorca,et al.  Choco: an Open Source Java Constraint Programming Library , 2008 .

[24]  Luciana S. Buriol,et al.  Piecing together the NFV provisioning puzzle: Efficient placement and chaining of virtual network functions , 2015, 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM).