ABSTRACT
Edge Computing (EC) represents the most promising solution to the real-time or near-real-time processing needs of the data generated by Internet of Things devices. The emergence of Edge Infrastructure Providers (EIPs) will bring the EC benefits to those enterprises that cannot afford to purchase, deploy, and manage their own edge infrastructures. The main goal of EIPs will be that of max-imizing their profit, i.e. the difference of the revenues they make to host applications, and the cost they incur to run the infrastructure plus the penalty they have to pay when QoS requirements of hosted applications are not met. To maximize profit, an EIP must strike a balance between the above two factors.
In this paper we present the Online Profit Maximization (OPM) algorithm, an approximation algorithm that aims at increasing the profit of an EIP without a priori knowledge. We assess the performance of OPM by simulating its behavior for a variety of realistic scenarios, in which data are generated by a population of moving users, and by comparing the results it yields against those attained by an oracle (i.e., an unrealistic algorithm able to always make optimal decisions) and by a state-of-the-art alternative. Our results indicate that OPM is able to achieve results that are always within 1% of the optimal ones, and that always outperforms the alternative solution.
- M. Aazam et al. 2015. Dynamic resource provisioning through Fog micro data-center. In IEEE Int. Conf. on PerCom Workshops. 105--110.Google Scholar
- M. Aazam et al. 2015. Fog Computing Micro Datacenter Based Dynamic Resource Estimation and Pricing Model for IoT. In 29th Conf. on AINA. 687--694.Google Scholar
- S.F. Abedin et al. 2015. A Fog based system model for cooperative IoT node pairing using matching theory. In 17th Asia-Pacific APNOMS. 309--314.Google Scholar
- L. Albano, C. Anglano, M. Canonico, and M. Guazzone. 2013. Fuzzy-Q&E: Achieving QoS Guarantees and Energy Savings for Cloud Applications with Fuzzy Control. In 3rd Int. Cloud and Green Computing Conference (CGC'13). 159--166. Google ScholarDigital Library
- C. Anglano, M. Canonico, P. Castagno, M. Guazzone, and M. Sereno. 2018. A Game-Theoretic Approach to Coalition Formation in Fog Provider Federations. In 3rd IEEE Int. Conf. on Fog and Mobile Edge Computing (FMEC'18).Google Scholar
- C. Anglano, M. Canonico, and M. Guazzone. 2015. FC2Q: Exploiting fuzzy control in server consolidation for cloud applications with SLA constraints. Concurrency and Computation: Practice and Experience 27, 17 (2015), 4491--4514. Google ScholarDigital Library
- C. Anglano, M. Canonico, and M. Guazzone. 2017. FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints. Concurrency and Computation: Practice and Experience 29, 5 (2017).Google Scholar
- C. Anglano, M. Canonico, and M. Guazzone. 2018. Prometheus: A flexible toolkit for the experimentation with virtualized infrastructures. Concurrency and Computation: Practice and Experience 30, 11 (2018), e4400.Google ScholarCross Ref
- C. Anglano, M. Guazzone, and M. Sereno. 2014. Maximizing profit in green cellular networks through collaborative games. Computer Networks 75, Part A (2014), 260--275. Google ScholarDigital Library
- T. Bahreini et al. 2017. Efficient Placement of Multi-component Applications in Edge Computing Systems. In 2nd ACM/IEEE SEC. ACM, Article 5, 11 pages. Google ScholarDigital Library
- J. Banks et al. 2010. Discrete-Event System Simulation (5th ed.). Prentice Hall.Google Scholar
- G. Bolch et al. 2006. Queueing Networks and Markov Chains: Modeling and Performance Evaluation With Computer Science Applications (2nd ed.). Wiley. Google ScholarDigital Library
- F. Bonomi et al. 2014. Fog Computing: A Platform for Internet of Things and Analytics. Springer, Cham, 169--186.Google Scholar
- Charles C Byers. 2017. Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for Fog-Enabled IoT Networks. IEEE Commun. Mag. 55, 8 (2017), 14--20.Google ScholarDigital Library
- P. Garcia Lopez et al. 2015. Edge-centric Computing: Vision and Challenges. SIGCOMM Comput. Commun. Rev. 45, 5 (2015), 37--42. Google ScholarDigital Library
- M. Guazzone, C. Anglano, and M. Canonico. 2011. Energy-Efficient Resource Management for Cloud Computing Infrastructures. In 3rd IEEE Int. Conf. on Cloud Computing Technology and Science (CloudCom'11). 424--431. Google ScholarDigital Library
- M. Guazzone, C. Anglano, and M. Canonico. 2012. Exploiting VM Migration for the Automated Power and Performance Management of Green Cloud Computing Systems. In 1st Int. Workshop on Energy-Efficient Data Centres (E2DC). 81--92. Google ScholarDigital Library
- M. Guazzone, C. Anglano, and M. Sereno. 2014. A Game-Theoretic Approach to Coalition Formation in Green Cloud Federations. In 14th IEEE/ACM Int. Symposium on Cluster, Cloud and Grid Computing (CCGRID'14). 618--625.Google Scholar
- IBM. 2018. ILOG CPLEX Optimization Studio. (2018).Google Scholar
- K. Intharawijitr et al. 2016. Analysis of fog model considering computing and communication latency in 5G cellular networks. In IEEE Conf. on PerCom. 1--4.Google ScholarCross Ref
- D.B. Johnson et al. 1996. Dynamic Source Routing in Ad Hoc Wireless Networks. Springer US, 153--181.Google Scholar
- C. Mouradian et al. 2017. A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges. IEEE Commun. Surveys Tuts. (2017).Google Scholar
- S. Ningning et al. 2016. Fog computing dynamic load balancing mechanism based on graph repartitioning. China Commun. 13, 3 (2016), 156--164.Google ScholarCross Ref
- J. Oueis et al. 2015. Small cell clustering for efficient distributed fog computing: A multi-user case. In 2015 IEEE 82nd VTC Fall. 1--5.Google Scholar
- Primate Labs, Inc. 2018. GeekBench. (2018).Google Scholar
- S. Rivoire et al. 2008. A Comparison of High-level Full-system Power Models. In 2008 Conf. on HotPower. USENIX, 3--3. Google ScholarDigital Library
- M. Satyanarayanan et al. 2009. The Case for VM-Based Cloudlets in Mobile Computing. IEEE Pervasive Comput. 8, 4 (2009), 14--23. Google ScholarDigital Library
- W. Vogels. 2008. Beyond Server Consolidation. Queue 6, 1 (2008), 20--26. Google ScholarDigital Library
- J. Weinman. 2017. The Economics of the Hybrid Multicloud Fog. IEEE Cloud Computing 4, 1 (2017), 16--21.Google ScholarCross Ref
- D. Ye et al. 2016. Scalable fog computing with service offloading in bus networks. In IEEE 3rd Int. Conf. on CSCloud. 247--251.Google ScholarCross Ref
Index Terms
- Profit-aware Resource Management for Edge Computing Systems
Recommendations
A new genetic algorithm approach for optimizing bidding strategy viewpoint of profit maximization of a generation company
This paper presents a new approach for bidding strategy in a day-ahead market from the viewpoint of a generation company (GENCO) in order to maximize its own profit as a participant in the market. It is assumed that each GENCO submits its own bid as ...
Simple Mechanisms for Profit Maximization in Multi-item Auctions
EC '19: Proceedings of the 2019 ACM Conference on Economics and ComputationWe study a classical Bayesian mechanism design problem where a seller is selling multiple items to a buyer. We consider the case where the seller has costs to produce the items, and these costs are private information to the seller. How can the seller ...
Auction method to prevent bid-rigging strategies in mobile blockchain edge computing resource allocation
AbstractTo introduce blockchain technology to the mobile internet and Internet of Things, offloading proof-of-work tasks to edge computing nodes is considered an effective solution. Among these solutions, pricing edge computing resources ...
Highlights- This study curbs bid rigging in mobile blockchain edge computing resource auction.
Comments