Service Placement and Request Scheduling for Data-Intensive Applications in Edge Clouds
| Autor: | Konstantinos Poularakis, Thomas F. La Porta, Fidan Mehmeti, Ting He, Hana Khamfroush, Shiqiang Wang, Vajiheh Farhadi, Kevin S. Chan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Mobile edge computing
Computer Networks and Communications business.industry Computer science Distributed computing 020206 networking & telecommunications Cloud computing 02 engineering and technology Computer Science Applications Scheduling (computing) Resource (project management) Server 0202 electrical engineering electronic engineering information engineering Graph (abstract data type) Enhanced Data Rates for GSM Evolution Electrical and Electronic Engineering business Software Edge computing |
| Zdroj: | IEEE/ACM Transactions on Networking. 29:779-792 |
| ISSN: | 1558-2566 1063-6692 |
| DOI: | 10.1109/tnet.2020.3048613 |
| Popis: | Mobile edge computing provides the opportunity for wireless users to exploit the power of cloud computing without a large communication delay. To serve data-intensive applications (e.g., video analytics, machine learning tasks) from the edge, we need, in addition to computation resources, storage resources for storing server code and data as well as network bandwidth for receiving user-provided data. Moreover, due to time-varying demands, the code and data placement needs to be adjusted over time, which raises concerns of system stability and operation cost. In this paper, we address these issues by proposing a two-time-scale framework that jointly optimizes service (code and data) placement and request scheduling, while considering storage, communication, computation, and budget constraints. First, by analyzing the hardness of various cases, we completely characterize the complexity of our problem. Next, we develop a polynomial-time service placement algorithm by formulating our problem as a set function optimization, which attains a constant-factor approximation under certain conditions. Furthermore, we develop a polynomial-time request scheduling algorithm by computing the maximum flow in a carefully constructed auxiliary graph, which satisfies hard resource constraints and is provably optimal in the special case where requests have homogeneous resource demands. Extensive synthetic and trace-driven simulations show that the proposed algorithms achieve 90% of the optimal performance. |
| Databáze: | OpenAIRE |
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