Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can brin...Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can bring computation power closer to the end devices to reduce their computation latency and energy consumption.Therefore,this paradigm increases the computational ability of SMDs by collaboration with edge servers.This is achieved by computation offloading from the mobile devices to the edge nodes or servers.However,not all applications benefit from computation offloading,which is only suitable for certain types of tasks.Task properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading decisions.Hence,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks.In this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and others.For each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed algorithms.We discuss a few research problems that are still open.Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.展开更多
Segment routing has been a novel architecture for traffic engineering in recent years.However,segment routing brings control overheads,i.e.,additional packets headers should be inserted.The overheads can greatly reduc...Segment routing has been a novel architecture for traffic engineering in recent years.However,segment routing brings control overheads,i.e.,additional packets headers should be inserted.The overheads can greatly reduce the forwarding efficiency for a large network,when segment headers become too long.To achieve the best of two targets,we propose the intelligent routing scheme for traffic engineering(IRTE),which can achieve load balancing with limited control overheads.To achieve optimal performance,we first formulate the problem as a mapping problem that maps different flows to key diversion points.Second,we prove the problem is nondeterministic polynomial(NP)-hard by reducing it to a k-dense subgraph problem.To solve this problem,we develop an ant colony optimization algorithm as improved ant colony optimization(IACO),which is widely used in network optimization problems.We also design the load balancing algorithm with diversion routing(LBA-DR),and analyze its theoretical performance.Finally,we evaluate the IRTE in different real-world topologies,and the results show that the IRTE outperforms traditional algorithms,e.g.,the maximum bandwidth is 24.6% lower than that of traditional algorithms when evaluating on BellCanada topology.展开更多
With the rapid development of WiFi and 3G/4G, people tend to view videos on mobile devices. These devices are ubiquitous but have small memory to cache videos. As a result, in contrast to traditional computers, these ...With the rapid development of WiFi and 3G/4G, people tend to view videos on mobile devices. These devices are ubiquitous but have small memory to cache videos. As a result, in contrast to traditional computers, these devices aggravate the network pressure of content providers. Previous studies use CDN to solve this problem. But its static leasing mechanism in which the rental space cannot be dynamically adjusted makes the operational cost soar and incompatible with the dynamically video delivery. In our study, based on a thorough analysis of user behavior from Tencent Video, a popular Chinese on-line video share platform, we identify two key user behaviors. Firstly, lots of users in the same region tend to watch the same video. Secondly, the popularity distribution of videos conforms with the Pareto principle, i.e., the top 20% popular videos own 80% of all video traffic. To turn these observations into silver bullet, we propose and implement a novel cloud- and peer-assisted video on demand system (CPA-VoD). In the system, we group users in the same region as a peer swarm, and in the same peer swarm, users can provide videos to other users by sharing their cached videos. Besides, we cache the 10% most popular videos in cloud servers to further alleviate the network pressure. We choose cloud servers to cache videos because the rental space can be dynamically adjusted. According to the evaluation on a real dataset from Tencent Video, CPA-VoD alleviates the network pressure and the operation cost excellently, while only 20.9% traffic is serviced by the content provider.展开更多
基金supported by National Key R&D Program of China under Grant.No.2018YFB1800805National Natural Science Foundation of China under Grant No.61772345,61902257,61972261Shenzhen Science and Technology Program under Grant No.RCYX20200714114645048,No.JCYJ20190808142207420,No.GJHZ20190822095416463.
文摘Handling the massive amount of data generated by Smart Mobile Devices(SMDs)is a challenging computational problem.Edge Computing is an emerging computation paradigm that is employed to conquer this problem.It can bring computation power closer to the end devices to reduce their computation latency and energy consumption.Therefore,this paradigm increases the computational ability of SMDs by collaboration with edge servers.This is achieved by computation offloading from the mobile devices to the edge nodes or servers.However,not all applications benefit from computation offloading,which is only suitable for certain types of tasks.Task properties,SMD capability,wireless channel state,and other factors must be counted when making computation offloading decisions.Hence,optimization methods are important tools in scheduling computation offloading tasks in Edge Computing networks.In this paper,we review six types of optimization methods-they are Lyapunov optimization,convex optimization,heuristic techniques,game theory,machine learning,and others.For each type,we focus on the objective functions,application areas,types of offloading methods,evaluation methods,as well as the time complexity of the proposed algorithms.We discuss a few research problems that are still open.Our purpose for this review is to provide a concise summary that can help new researchers get started with their computation offloading researches for Edge Computing networks.
基金supported in part by the National Natural Science Foundation of China(Nos.61772345 and 61902258)the Major Fundamental Research Project in the Science and Technology Plan of Shenzhen(Nos.JCYJ20190808142207420,GJHZ20190822095416463,and RCYX20200714114645048)+1 种基金the Natural Science Foundation of Guangdong Basic and Applied Basic Research(No.2021A1515011857)the Pearl River Young Scholars Funding of Shenzhen University.
文摘Segment routing has been a novel architecture for traffic engineering in recent years.However,segment routing brings control overheads,i.e.,additional packets headers should be inserted.The overheads can greatly reduce the forwarding efficiency for a large network,when segment headers become too long.To achieve the best of two targets,we propose the intelligent routing scheme for traffic engineering(IRTE),which can achieve load balancing with limited control overheads.To achieve optimal performance,we first formulate the problem as a mapping problem that maps different flows to key diversion points.Second,we prove the problem is nondeterministic polynomial(NP)-hard by reducing it to a k-dense subgraph problem.To solve this problem,we develop an ant colony optimization algorithm as improved ant colony optimization(IACO),which is widely used in network optimization problems.We also design the load balancing algorithm with diversion routing(LBA-DR),and analyze its theoretical performance.Finally,we evaluate the IRTE in different real-world topologies,and the results show that the IRTE outperforms traditional algorithms,e.g.,the maximum bandwidth is 24.6% lower than that of traditional algorithms when evaluating on BellCanada topology.
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61402294, the Natural Science Foun- dation of Guangdong Province of China under Grant No. S2013040012895, the Foundation for Distinguished Young Talents in Higher Education of Guangdong Province of China under Grant No. 2013LYM_0076, the Major Fundamental Research Project in the Science and Technology Plan of Shenzhen under Grant Nos. JCYJ20140828163633977 and JCYJ20160310095523765, and the Research and Development Program of Shenzhen under Grant Nos. ZDSYS20140509172959989, JSGG20150512162853495, and Shenfagai(2015)986.
文摘With the rapid development of WiFi and 3G/4G, people tend to view videos on mobile devices. These devices are ubiquitous but have small memory to cache videos. As a result, in contrast to traditional computers, these devices aggravate the network pressure of content providers. Previous studies use CDN to solve this problem. But its static leasing mechanism in which the rental space cannot be dynamically adjusted makes the operational cost soar and incompatible with the dynamically video delivery. In our study, based on a thorough analysis of user behavior from Tencent Video, a popular Chinese on-line video share platform, we identify two key user behaviors. Firstly, lots of users in the same region tend to watch the same video. Secondly, the popularity distribution of videos conforms with the Pareto principle, i.e., the top 20% popular videos own 80% of all video traffic. To turn these observations into silver bullet, we propose and implement a novel cloud- and peer-assisted video on demand system (CPA-VoD). In the system, we group users in the same region as a peer swarm, and in the same peer swarm, users can provide videos to other users by sharing their cached videos. Besides, we cache the 10% most popular videos in cloud servers to further alleviate the network pressure. We choose cloud servers to cache videos because the rental space can be dynamically adjusted. According to the evaluation on a real dataset from Tencent Video, CPA-VoD alleviates the network pressure and the operation cost excellently, while only 20.9% traffic is serviced by the content provider.