Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing

To overcome devices’ limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, the current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users’ power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are reassociated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user–server association policy is proposed, taking into account the channel quality as well as the servers’ computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user–server association is solved in the long timescale, while a dynamic task offloading and resource allocation policy are executed in the short timescale. The simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly reliable task computation and lower delay performance, compared to several baselines.

[1]  Mehdi Bennis,et al.  Wireless Network Intelligence at the Edge , 2018, Proceedings of the IEEE.

[2]  Hao Liang,et al.  Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption , 2016, IEEE Internet of Things Journal.

[3]  Nirwan Ansari,et al.  Towards Workload Balancing in Fog Computing Empowered IoT , 2020, IEEE Transactions on Network Science and Engineering.

[4]  Jun Zhang,et al.  Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems , 2017, IEEE Transactions on Wireless Communications.

[5]  Walid Saad,et al.  Matching theory for future wireless networks: fundamentals and applications , 2014, IEEE Communications Magazine.

[6]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[7]  Shaolei Ren,et al.  Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing , 2017, IEEE Transactions on Cognitive Communications and Networking.

[8]  K. B. Letaief,et al.  A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.

[9]  Thomas D. Burd,et al.  Processor design for portable systems , 1996, J. VLSI Signal Process..

[10]  Mehdi Bennis,et al.  Millimeter-Wave V2V Communications: Distributed Association and Beam Alignment , 2016, IEEE Journal on Selected Areas in Communications.

[11]  PROPAGATION DATA AND PREDICTION METHODS FOR THE PLANNING OF INDOOR RADIOCOMMUNICATION SYSTEMS AND RADIO LOCAL AREA NETWORKS IN THE FREQUENCY RANGE 900 MHz TO 100 GHz , 1997 .

[12]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[13]  Kun Yang,et al.  Energy Efficiency and Delay Tradeoff in Multi-User Wireless Powered Mobile-Edge Computing Systems , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[14]  Jukka K. Nurminen,et al.  Energy Efficiency of Mobile Clients in Cloud Computing , 2010, HotCloud.

[15]  Nirwan Ansari,et al.  Application Aware Workload Allocation for Edge Computing-Based IoT , 2018, IEEE Internet of Things Journal.

[16]  Adam Wierman,et al.  Peer Effects and Stability in Matching Markets , 2011, SAGT.

[17]  Walid Saad,et al.  Proactive edge computing in latency-constrained fog networks , 2017, 2017 European Conference on Networks and Communications (EuCNC).

[18]  Lingyang Song,et al.  Sub-Channel Assignment, Power Allocation, and User Scheduling for Non-Orthogonal Multiple Access Networks , 2016, IEEE Transactions on Wireless Communications.

[19]  Zhu Han,et al.  Computing Resource Allocation in Three-Tier IoT Fog Networks: A Joint Optimization Approach Combining Stackelberg Game and Matching , 2017, IEEE Internet of Things Journal.

[20]  H. Vincent Poor,et al.  Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale , 2018, Proceedings of the IEEE.

[21]  Antonio Pascual-Iserte,et al.  Joint scheduling of communication and computation resources in multiuser wireless application offloading , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[22]  Wanshi Chen,et al.  5G ultra-reliable and low-latency systems design , 2017, 2017 European Conference on Networks and Communications (EuCNC).

[23]  Kaibin Huang,et al.  Wireless Networks for Mobile Edge Computing: Spatial Modeling and Latency Analysis , 2017, IEEE Transactions on Wireless Communications.

[24]  Mehdi Bennis,et al.  Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach , 2018, IEEE Communications Letters.

[25]  Shiwen Mao,et al.  Energy Delay Tradeoff in Cloud Offloading for Multi-Core Mobile Devices , 2015, IEEE Access.

[26]  Raymond Hemmecke,et al.  Nonlinear Integer Programming , 2009, 50 Years of Integer Programming.

[27]  Khaled Ben Letaief,et al.  Power-Delay Tradeoff in Multi-User Mobile-Edge Computing Systems , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[28]  Osvaldo Simeone,et al.  Energy-Efficient Resource Allocation for Mobile Edge Computing-Based Augmented Reality Applications , 2016, IEEE Wireless Communications Letters.

[29]  Jie Xu,et al.  EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks , 2017, IEEE Journal on Selected Areas in Communications.

[30]  Kaibin Huang,et al.  Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading , 2016, IEEE Transactions on Wireless Communications.

[31]  Alvin E. Roth,et al.  Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis , 1990 .

[32]  J. Little A Proof for the Queuing Formula: L = λW , 1961 .

[33]  Roch H. Glitho,et al.  A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges , 2017, IEEE Communications Surveys & Tutorials.

[34]  Xing Zhang,et al.  A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications , 2017, IEEE Access.

[35]  Preben E. Mogensen,et al.  Fundamental tradeoffs among reliability, latency and throughput in cellular networks , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).

[36]  Jeongho Kwak,et al.  Dual-Side Optimization for Cost-Delay Tradeoff in Mobile Edge Computing , 2017, IEEE Transactions on Vehicular Technology.

[37]  Nirwan Ansari,et al.  Workload Allocation in Hierarchical Cloudlet Networks , 2018, IEEE Communications Letters.

[38]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[39]  Walid Saad,et al.  An online secretary framework for fog network formation with minimal latency , 2017, 2017 IEEE International Conference on Communications (ICC).

[40]  H. Vincent Poor,et al.  Latency and Reliability-Aware Task Offloading and Resource Allocation for Mobile Edge Computing , 2017, 2017 IEEE Globecom Workshops (GC Wkshps).

[41]  Farooq Khan,et al.  System design and network architecture for a millimeter-wave mobile broadband (MMB) system , 2011, 34th IEEE Sarnoff Symposium.

[42]  Yue Chen,et al.  Many-to-Many Matching With Externalities for Device-to-Device Communications , 2017, IEEE Wireless Communications Letters.

[43]  Sangtae Ha,et al.  Clarifying Fog Computing and Networking: 10 Questions and Answers , 2017, IEEE Communications Magazine.

[44]  Jeongho Kwak,et al.  DREAM: Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems , 2015, IEEE Journal on Selected Areas in Communications.