Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile termin...Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.展开更多
At present, there are many effective ways to achieve high performance in cluster system storage management, including server-end disk, server-end caching, local caching and cooperative caching. The cooperative caching...At present, there are many effective ways to achieve high performance in cluster system storage management, including server-end disk, server-end caching, local caching and cooperative caching. The cooperative caching mechanism shares caches among different clients so as to avoid expensive disk access costs and to improve overall throughput of cluster system. In this paper, a Single Copy Cooperative Cache model is proposed together with block lookup algorithm, block replacement algorithm and the consistency algorithm based on the model. Meanwhile, the prototype system of the model is implemented in PVFS file system. Finally, the performance of this system is tested in InfiniBand Framework, the result of which shows that in contrast to the original PVFS system, read performance of PVFS file system is improved by about two times, while write performance is reduced by nearly ten percent.展开更多
Due to the proliferation of Internet and Intranet,the distributed storage systems have received a lot of attention. These systems span a large number of machines and store huge amount of data for a lot of users.In the...Due to the proliferation of Internet and Intranet,the distributed storage systems have received a lot of attention. These systems span a large number of machines and store huge amount of data for a lot of users.In the distributed storage systems,a row can be directly accessed using a row key.We concentrate on a problem of efficient processing of queries whose predicate is on a column but not a row key.In this paper,we present a cache management technique,called DICE which maintains query results of range queries to support the next range queries.To accelerate the search time of the cached query results,we use modified Interval Ski Lists.In addition,we devise a novel cache replacement policy since DICE maintains an interval rather than a data item.Since our cache replacement policy considers the properties of intervals,our proposed technique is more efficient than traditional buffer replacement algorithms.Our experimental result demonstrates the efficiency of our proposed technique.展开更多
Our study introduces a novel distributed query plan refinement phase in an enhanced architecture of distributed query processing engine （DQPE）. Query plan refinement generates potentially efficient distributed query...Our study introduces a novel distributed query plan refinement phase in an enhanced architecture of distributed query processing engine （DQPE）. Query plan refinement generates potentially efficient distributed query plan by reusable aggregate query shipping （RAQS） approach. The approach improves response time at the cost of pre-processing time. If the overheads could not be compensated by query results reusage, RAQS is no more favorable. Therefore a globM cost estimation model is employed to get proper operators： RR_Agg, R_Agg, or R_Scan. For the purpose of reusing results of queries with aggregate function in distributed query processing, a multi-level hybrid view caching （HVC） scheme is introduced. The scheme retains the advantages of partial match and aggregate query results caching. By our solution, evaluations with distributed TPC-H queries show significant improvement on average response time.展开更多
With the rapid development of vehicle-based applications, entertainment videos have gained popularity for passengers on public vehicles. Therefore, how to provide high quality video service for passengers in typical p...With the rapid development of vehicle-based applications, entertainment videos have gained popularity for passengers on public vehicles. Therefore, how to provide high quality video service for passengers in typical public transportation scenarios is an essential problem. This paper proposes a quality of experience(QoE)-based video segments caching(QoE-VSC) strategy to guarantee the smooth watching experience of passengers. Consequently, this paper considers a jointly caching scenario where the bus provides the beginning segments of a video, and the road side unit(RSU) offers the remaining for passengers. To evaluate the effectiveness, QoE hit ratio is defined to represent the probability that the bus and RSUs jointly provide passengers with desirable video segments successfully. Furthermore, since passenger volume change will lead to different video preferences, a deep reinforcement learning(DRL) network is trained to generate the segment replacing policy on the video segments cached by the bus server. And the training target of DRL is to maximize the QoE hit ratio, thus enabling more passengers to get the required video. The simulation results prove that the proposed method has a better performance than baseline methods in terms of QoE hit ratio and cache costs.展开更多
文摘Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as possible.In an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user experience.However,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of users.Recently,techniques for caching content at the edge are becoming popular for addressing the above challenges.It is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud servers.However,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity data.To address this challenge,we introduce a novel method for content caching over MEC,i.e.,PRIME.It synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching schedules.Experimental results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.
基金This work was supported by the National High Technology Development Program of China under Grant(No.2004AA111110,No.2006AA01A109)
文摘At present, there are many effective ways to achieve high performance in cluster system storage management, including server-end disk, server-end caching, local caching and cooperative caching. The cooperative caching mechanism shares caches among different clients so as to avoid expensive disk access costs and to improve overall throughput of cluster system. In this paper, a Single Copy Cooperative Cache model is proposed together with block lookup algorithm, block replacement algorithm and the consistency algorithm based on the model. Meanwhile, the prototype system of the model is implemented in PVFS file system. Finally, the performance of this system is tested in InfiniBand Framework, the result of which shows that in contrast to the original PVFS system, read performance of PVFS file system is improved by about two times, while write performance is reduced by nearly ten percent.
基金supported by National Research Foundation of Korea under Grant No.2010-0016165supported by the IT R&D Program of MIC/IITA under Grant No.2007-S-016-02.
文摘Due to the proliferation of Internet and Intranet,the distributed storage systems have received a lot of attention. These systems span a large number of machines and store huge amount of data for a lot of users.In the distributed storage systems,a row can be directly accessed using a row key.We concentrate on a problem of efficient processing of queries whose predicate is on a column but not a row key.In this paper,we present a cache management technique,called DICE which maintains query results of range queries to support the next range queries.To accelerate the search time of the cached query results,we use modified Interval Ski Lists.In addition,we devise a novel cache replacement policy since DICE maintains an interval rather than a data item.Since our cache replacement policy considers the properties of intervals,our proposed technique is more efficient than traditional buffer replacement algorithms.Our experimental result demonstrates the efficiency of our proposed technique.
基金partially supported by the National Basic Research 973 Program of China under Grant No. 2005CB321807the National High Technology Rresearch and Development 863 Program of China under Grant Nos. 2006AA01A106 and 2006AA04Z158.
文摘Our study introduces a novel distributed query plan refinement phase in an enhanced architecture of distributed query processing engine （DQPE）. Query plan refinement generates potentially efficient distributed query plan by reusable aggregate query shipping （RAQS） approach. The approach improves response time at the cost of pre-processing time. If the overheads could not be compensated by query results reusage, RAQS is no more favorable. Therefore a globM cost estimation model is employed to get proper operators： RR_Agg, R_Agg, or R_Scan. For the purpose of reusing results of queries with aggregate function in distributed query processing, a multi-level hybrid view caching （HVC） scheme is introduced. The scheme retains the advantages of partial match and aggregate query results caching. By our solution, evaluations with distributed TPC-H queries show significant improvement on average response time.
基金supported by the National Natural Science Foundation of China(61771070)。
文摘With the rapid development of vehicle-based applications, entertainment videos have gained popularity for passengers on public vehicles. Therefore, how to provide high quality video service for passengers in typical public transportation scenarios is an essential problem. This paper proposes a quality of experience(QoE)-based video segments caching(QoE-VSC) strategy to guarantee the smooth watching experience of passengers. Consequently, this paper considers a jointly caching scenario where the bus provides the beginning segments of a video, and the road side unit(RSU) offers the remaining for passengers. To evaluate the effectiveness, QoE hit ratio is defined to represent the probability that the bus and RSUs jointly provide passengers with desirable video segments successfully. Furthermore, since passenger volume change will lead to different video preferences, a deep reinforcement learning(DRL) network is trained to generate the segment replacing policy on the video segments cached by the bus server. And the training target of DRL is to maximize the QoE hit ratio, thus enabling more passengers to get the required video. The simulation results prove that the proposed method has a better performance than baseline methods in terms of QoE hit ratio and cache costs.
