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Multicast-aware optimization for resource allocation with edge computing and caching
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DOI：10.1016/j.jnca.2021.103195.png)
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Mobile edge computing (MEC) is rising as a key technology for computation-intensive and delay-sensitive applications. Many works have focused on MEC, but most of them only consider unicast scenarios, and ignore multicast issues. The reason is that MEC aiming at personalized computing of users conflicts with multicast which demands the same data stream. This makes MEC and multicast seem inconsistent. However, in fact, there will be lots of services, whose computation process is different but the result may be same (e.g. media push), which can greatly benefit from multicast over MEC and make the combination of multicast and MEC meaningful. In this paper, we first consider challenges and propose multicast-aware resource allocation for MEC, which jointly optimizes computing and caching in multicast scenarios. We formulate the problem by considering user request, network communication, service caching and service computing. But this model is knotty because it is an optimization problem with mixed discrete and continuous variables. Besides its optimization objective is the average value over a long time. Considering the complexity, we first transform the problem into an online optimization, which jointly minimizes the average time delay and energy consumption, by stochastic optimization. Then we separate the discrete variables and continuous variable, and decompose the problem into two subproblems. By solving subproblems, an efficient online algorithm called MA-ECC is proposed. Finally, we compare it with other three baseline methods, and result shows that MA-ECC can effectively reduce service latency while still keeping energy consumption low.
Keyword:
Mobile edge computing
Multicast
Stochastic optimization
Resource allocation
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