Optimal data placement strategy considering capacity limitation and load balancing in geographically distributed cloud

Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased. Also, performing this volume of computing operations requires high processing and storage resources leading to hardware cost increment. In such systems, one of the most critical challenges is the task scheduling problem, which tries to find the optimal allocation for maximum resource usage and reduce the response time. Therefore, the purpose of this study is to design an infrastructure for smart home energy management with minimum hardware cost using cloud and fog computing and to propose a latency-aware scheduling algorithm based on virtual machine matching using meta-heuristics. Among heuristic methods, Tabu search makes it a common practice because of its high expansion in various optimization issues, as well as memory and high-speed features. Thereby, a novel algorithm based on the Tabu search is proposed that is improved using approximate nearest neighbor (ANN) and fruit fly optimization (FOA) algorithms. Finally, to validate the proposed method, a case study is simulated and the proposed algorithm is implemented considering target factors of execution time, latency, allocated memory, and cost function to illustrate the performance of the algorithm. The comparison results show that the proposed algorithm outperforms the Tabu search, genetic algorithm, PSO, and simulated annealing methods.

A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

An optimized content caching strategy for video stream in edge-cloud environment

Software-Defined networking (SDN) is a networking paradigm to enable dynamic, flexible, and programmatically efficient configuration of networks to revolutionize network control and management via separation of the control plane and data plane. The SDN technology has evolved in response to the demands from large data centers toward all types of networks, from IoT, enterprise, to ISP networks. On the one hand, SDN has provided solutions for high-demand resources, managing unpredictable data traffic patterns, and rapid network reconfiguration. It is further used to enhance network virtualization and security. On the other hand, SDN is still subject to many traditional network security threats. It also introduces new security vulnerabilities, primarily due to its logically centralized control plane infrastructure and functions. In this paper, we conduct a comprehensive survey on the core functionality of SDN from the perspective of secure communication infrastructure at different scales. A specific focus is put forward to address the challenges in securing SDN-based communications, with efforts taken up to address them. We further categorize the appropriate solutions for specific threats at each layer of SDN infrastructures. Lastly, security implications and future research trends are highlighted to provide insights for future research. 

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SDN Security Review: Threat Taxonomy, Implications, and Open Challenges

Joint edge caching and dynamic service migration in SDN based mobile edge computing

With the rapid development and the widespread use of cloud computing in various applications, the number of users distributed in different regions has grown exponentially. Therefore, the Geo-distributed cloud systems have become a research hotspot and big data processing technology has also emerged. Nowadays, the most widely used big data processing framework is Spark. However, massive amounts of data are generated every moment, and the processing procedure becomes more and more complex, the execution efficiency of Spark has been greatly affected. In the Spark frame of geo-distributed cloud systems, aiming at the data placement problem, the data placement strategy based on RDD dynamic weight is introduced. The target node is selected with a strong computation capacity to place the data. Aiming at the problems of multi-task scheduling, a task will be scheduled to a node whose computation capacity can satisfy the requirement of this task. And then considering job classification and computing node performance, the optimized task scheduling strategy is in traduced. Experiments show that our algorithms can effectively adjust the weight of node data placement according to the actual task execution information, and shorten the task execution time.

Weigang Li

Papers

Joint edge caching and dynamic service migration in SDN based mobile edge computing

Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems