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Long Chen - One of the best experts on this subject based on the ideXlab platform.

  • quick qos guaranteed efficient Cloudlet placement in wireless metropolitan area networks
    The Journal of Supercomputing, 2018
    Co-Authors: Long Chen, Gangqiang Zhou
    Abstract:

    This article defines the QoS-guaranteed efficient Cloudlet deployment problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users, i.e., the average delay when service requests are successfully sent and being served by Cloudlets. Meanwhile, we try to optimize total deployment cost represented by the total number of deployed Cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms, respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used Cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deployment cost optimization problem where all Cloudlets’ computing capabilities have been given, i.e., designated capacity, an efficient heuristic algorithm is further proposed to minimize the number of Cloudlets. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than $$46\%$$ efficient than existing algorithms on the average Cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed Cloudlets by about $$50\%$$ averagely, owing to the calculation processes of shortest paths between APs and the sorting processes of user access delays.

  • quick qos guaranteed efficient Cloudlet placement in wireless metropolitan area networks
    arXiv: Networking and Internet Architecture, 2018
    Co-Authors: Long Chen, Gangqiang Zhou
    Abstract:

    This article defines the QoS-guaranteed efficient Cloudlet deploy problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users i.e. the average delay when service requests are successfully sent and being served by Cloudlets. Meanwhile, we try to optimize total deploy cost represented by the total number of deployed Cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used Cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deploy cost optimization problem where all Cloudlets' computing capabilities have been given, i.e., designated capacity, a minimal Cloudlets efficient heuristic algorithm is further proposed. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than 46% efficient than existing algorithms on the average Cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed Cloudlets by about 50% averagely.

  • Efficient three-stage auction schemes for Cloudlets deployment in wireless access network
    Wireless Networks, 2018
    Co-Authors: Gangqiang Zhou, Long Chen, Guiyuan Jiang, Siew-kei Lam
    Abstract:

    Cloudlet deployment and resource allocation for mobile users (MUs) have been extensively studied in existing works for computation resource scarcity. However, most of them failed to jointly consider the two techniques together, and the selfishness of Cloudlet and access point (AP) are ignored. Inspired by the group-buying mechanism, this paper proposes three-stage auction schemes by combining Cloudlet placement and resource assignment, to improve the social welfare subject to the economic properties. We first divide all MUs into some small groups according to the associated APs. Then the MUs in same group can trade with Cloudlets in a group-buying way through the APs. Finally, the MUs pay for the Cloudlets if they are the winners in the auction scheme. We prove that our auction schemes can work in polynomial time. We also provide the proofs for economic properties in theory. For the purpose of performance comparison, we compare the proposed schemes with HAF, which is a centralized Cloudlet placement scheme without auction. Numerical results confirm the correctness and efficiency of the proposed schemes.

  • tacd a three stage auction scheme for Cloudlet deployment in wireless access network
    Wireless Algorithms Systems and Applications, 2017
    Co-Authors: Gangqiang Zhou, Long Chen
    Abstract:

    Motivated by the group-buying behaviors in recent years, we suggest that Mobile Users (MUs) to gather in Access Point (AP) and bid for a Cloudlet in a grouped way. In this paper, we proposed TACD, a three-stage auction to inspire Cloudlets sharing their resources and manage the deal between MU, AP and Cloudlet, which is efficient, flexible and truthful. This incentive mechanism aims at optimizing the social welfare, and ensures that all kinds of participants (MU, AP, Cloudlet) can benefit from this auction. We also proposed TACDp, an improved algorithm base on TACD, which social welfare is markedly improved.

  • DOTA: Delay Bounded Optimal Cloudlet Deployment and User Association in WMANs
    2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2017
    Co-Authors: Longjie Ma, Jigang Wu, Long Chen
    Abstract:

    In the large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs),choosing the appropriate position to place Cloudlet is very important for reducing the user's access delay. For service provider, it isalways very costly to deployment Cloudlets. How many Cloudletsshould be placed in a WMAN and how much resource eachCloudlet should have is very important for the service provider. In this paper, we study the Cloudlet placement and resourceallocation problem in a large-scale Wireless WMAN, we formulatethe problem as an novel Cloudlet placement problem that givenan average access delay between mobile users and the Cloudlets, place K Cloudlets to some strategic locations in the WMAN withthe objective to minimize the number of use Cloudlet K. Wethen propose an exact solution to the problem by formulatingit as an Integer Linear Programming (ILP). Due to the poorscalability of the ILP, we devise a clustering algorithm K-Medoids(KM) for the problem. For a special case of the problem whereall Cloudlets computing capabilities have been given, we proposean efficient heuristic for it. We finally evaluate the performanceof the proposed algorithms through experimental simulations. Simulation result demonstrates that the proposed algorithms areeffective.

Weifa Liang - One of the best experts on this subject based on the ideXlab platform.

  • optimal Cloudlet placement and user to Cloudlet allocation in wireless metropolitan area networks
    IEEE Transactions on Cloud Computing, 2017
    Co-Authors: Mike Jia, Jiannong Cao, Weifa Liang
    Abstract:

    Mobile applications are becoming increasingly computation-intensive, while the computing capability of portable mobile devices is limited. A powerful way to reduce the completion time of an application in a mobile device is to offload its tasks to nearby Cloudlets, which consist of clusters of computers. Although there is a significant body of research in mobile Cloudlet offloading technology, there has been very little attention paid to how Cloudlets should be placed in a given network to optimize mobile application performance. In this paper we study Cloudlet placement and mobile user allocation to the Cloudlets in a wireless metropolitan area network (WMAN). We devise an algorithm for the problem, which enables the placement of the Cloudlets at user dense regions of the WMAN, and assigns mobile users to the placed Cloudlets while balancing their workload. We also conduct experiments through simulation. The simulation results indicate that the performance of the proposed algorithm is very promising.

  • Efficient Algorithms for Capacitated Cloudlet Placements
    IEEE Transactions on Parallel and Distributed Systems, 2016
    Co-Authors: Zichuan Xu, Mike Jia, Wenzheng Xu, Weifa Liang, Song Guo
    Abstract:

    Mobile cloud computing is emerging as a main ubiquitous computing platform to provide rich cloud resources for various applications of mobile devices. Although most existing studies in mobile cloud computing focus on energy savings of mobile devices by offloading computing-intensive jobs from mobile devices to remote clouds, the access delays between mobile users and remote clouds usually are long and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and can enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper, we study the Cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs). We first formulate the problem as a novel capacitated Cloudlet placement problem that places Cloudlets to some strategic locations in the WMAN with the objective to minimize the average access delay between mobile users and the Cloudlets serving the users. We then propose an exact solution to the problem by formulating it as an Integer Linear Programming (ILP). Due to the poor scalability of the ILP, we instead propose an efficient heuristic for the problem. For a special case of the problem where all Cloudlets have identical computing capacities, we devise novel approximation algorithms with guaranteed approximation ratios. We also devise an online algorithm for dynamically allocating user requests to different Cloudlets, if the Cloudlets have already been placed. We finally evaluate the performance of the proposed algorithms through experimental- simulations. Simulation results demonstrate that the proposed algorithms are promising and scalable.

  • Capacitated Cloudlet placements in Wireless Metropolitan Area Networks
    2015 IEEE 40th Conference on Local Computer Networks (LCN), 2015
    Co-Authors: Zichuan Xu, Mike Jia, Wenzheng Xu, Weifa Liang, Song Guo
    Abstract:

    In this paper we study the Cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) that consists of many wireless Access Points (APs). Although most existing studies in mobile cloud computing mainly focus on energy savings of mobile devices by offloading computing-intensive jobs from them to remote clouds, the access delay between mobile users and the clouds usually is large and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and has been demonstrated to enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper we consider placing multiple Cloudlets with different computing capacities at some strategic local locations in a WMAN to reduce the average Cloudlet access delay of mobile users at different APs. We first formulate this problem as a novel capacitated Cloudlet placement problem that places K Cloudlets to some locations in the WMAN with the objective to minimize the average Cloudlet access delay between the mobile users and the Cloudlets serving their requests. We then propose a fast yet efficient heuristic. For a special case of the problem where all Cloudlets have the identical computing capacity, we devise a novel approximation algorithm with a guaranteed approximation ratio. In addition, We also consider allocating user requests to Cloudlets by devising an efficient online algorithm for such an assignment. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are promising and scalable.

  • LCN - Capacitated Cloudlet placements in Wireless Metropolitan Area Networks
    2015 IEEE 40th Conference on Local Computer Networks (LCN), 2015
    Co-Authors: Weifa Liang, Mike Jia, Song Guo
    Abstract:

    In this paper we study the Cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) that consists of many wireless Access Points (APs). Although most existing studies in mobile cloud computing mainly focus on energy savings of mobile devices by offloading computing-intensive jobs from them to remote clouds, the access delay between mobile users and the clouds usually is large and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and has been demonstrated to enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper we consider placing multiple Cloudlets with different computing capacities at some strategic local locations in a WMAN to reduce the average Cloudlet access delay of mobile users at different APs. We first formulate this problem as a novel capacitated Cloudlet placement problem that places K Cloudlets to some locations in the WMAN with the objective to minimize the average Cloudlet access delay between the mobile users and the Cloudlets serving their requests. We then propose a fast yet efficient heuristic. For a special case of the problem where all Cloudlets have the identical computing capacity, we devise a novel approximation algorithm with a guaranteed approximation ratio. In addition, We also consider allocating user requests to Cloudlets by devising an efficient online algorithm for such an assignment. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results demonstrate that the proposed algorithms are promising and scalable.

Nirwan Ansari - One of the best experts on this subject based on the ideXlab platform.

  • green Cloudlet network a sustainable platform for mobile cloud computing
    IEEE Transactions on Cloud Computing, 2020
    Co-Authors: Xiang Sun, Nirwan Ansari
    Abstract:

    In the Green Cloudlet Network (GCN) architecture, each User Equipment (UE) is associated with an Avatar (a private virtual machine for executing its UE's offloaded tasks) in a Cloudlet located at the network edge. In order to reduce the operational expenditure for maintaining the distributed Cloudlets, each Cloudlet is powered by green energy and uses on-grid power as a backup. Owing to the spatial dynamics of energy demands and green energy generations, the energy gap (i.e., energy demand minus green energy generation) among different Cloudlets in the network is unbalanced, i.e., some Cloudlets’ energy demands can be fully provisioned by their green energy generations but others need to utilize on-grid power to meet their energy demands. The unbalanced energy gap increases the on-grid power consumption of the Cloudlets. In this paper, we propose the Green-energy aware Avatar Placement (GAP) strategy to minimize the total on-grid power consumption of the Cloudlets by migrating Avatars among the Cloudlets according to the Cloudlets’ residual green energy, while guaranteeing the service level agreement (the End-to-End (E2E) delay requirement between a UE and its Avatar). Simulation results show that GAP can save 57.1 and 57.6 percent of on-grid power consumption as compared to the two other Avatar placement strategies, i.e., Static Avatar Placement and Follow me AvataR, respectively.

  • Adaptive Avatar Handoff in the Cloudlet Network
    IEEE Transactions on Cloud Computing, 2019
    Co-Authors: Xiang Sun, Nirwan Ansari
    Abstract:

    In a traditional big data network, data streams generated by User Equipments (UEs) are uploaded to the remote cloud (for further processing) via the Internet. However, moving a huge amount of data via the Internet may lead to a long End-to-End (E2E) delay between a UE and its computing resources (in the remote cloud) as well as severe traffic jams in the Internet. To overcome this drawback, we propose a Cloudlet network to bring the computing and storage resources from the cloud to the mobile edge. Each base station is attached to one Cloudlet and each UE is associated with its Avatar in the Cloudlet to process its data locally. Thus, the E2E delay between a UE and its computing resources in its Avatars is reduced as compared to that in the traditional big data network. However, in order to maintain the low E2E delay when UEs roam away, it is necessary to hand off Avatars accordingly—it is not practical to hand off the Avatars’ virtual disks during roaming as this will incur unbearable migration time and network congestion. We propose the LatEncy Aware Replica placemeNt (LEARN) algorithm to place a number of replicas of each Avatar's virtual disk into suitable Cloudlets. Thus, the Avatar can be handed off among its Cloudlets (which contain one of its replicas) without migrating its virtual disk. Simulations demonstrate that LEARN reduces the average E2E delay. Meanwhile, by considering the capacity limitation of each Cloudlet, we propose the LatEncy aware Avatar hanDoff (LEAD) algorithm to place UEs’ Avatars among the Cloudlets such that the average E2E delay is minimized. Simulations demonstrate that LEAD maintains the low average E2E delay.

  • On cost aware Cloudlet placement for mobile edge computing
    IEEE CAA Journal of Automatica Sinica, 2019
    Co-Authors: Qiang Fan, Nirwan Ansari
    Abstract:

    As accessing computing resources from the remote cloud inherently incurs high end-to-end ( E2E ) delay for mobile users, Cloudlets, which are deployed at the edge of a network, can potentially mitigate this problem. Although some research works focus on allocating workloads among Cloudlets, the Cloudlet placement aiming to minimize the deployment cost ( i.e., consisting of both the Cloudlet cost and average E2E delay cost ) has not been addressed effectively so far. The locations and number of Cloudlets have a crucial impact on both the Cloudlet cost in the network and average E2E delay of users. Therefore, in this paper, we propose the Cost Aware Cloudlet PlAcement in moBiLe Edge computing ( CAPABLE ) strategy, where both the Cloudlet cost and average E2E delay are considered in the Cloudlet placement. To solve this problem, a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution. After Cloudlets are placed in the network, we also design a workload allocation scheme to minimize the E2E delay between users and their Cloudlets by considering the user mobility. The performance of CAPABLE has been validated by extensive simulations.

  • Workload Allocation in Hierarchical Cloudlet Networks
    IEEE Communications Letters, 2018
    Co-Authors: Qiang Fan, Nirwan Ansari
    Abstract:

    Edge Cloudlets are promising to mitigate the high network delay incurred by the remote cloud in executing workloads offloaded from a user equipment (UE). However, the response time of a task request consists of both the network delay and computing delay. Considering the spatial and temporal dynamics of workloads among Cloudlets, if the workload of an edge Cloudlet is heavy, the computing delay in the Cloudlet may be unbearable. In this letter, we design a hierarchical Cloudlet network and propose a workload allocation scheme to minimize the average response time of UEs’ requests by deciding which Cloudlet a UE is assigned to and how much computing resource is provisioned to serve it. The performance of the proposed scheme is validated by extensive simulations.

  • Green Cloudlet Network: A Distributed Green Mobile Cloud Network
    IEEE Network, 2017
    Co-Authors: Xiang Sun, Nirwan Ansari
    Abstract:

    This article introduces a Green Cloudlet Network (GCN) architecture in the context of mobile cloud computing. The proposed architecture is aimed at providing seamless and low End-to-End (E2E) delay between a User Equipment (UE) and its Avatar (its software clone) in the Cloudlets to facilitate the application workloads offloading process. Furthermore, Software Define Networking (SDN) based core network is introduced in the GCN architecture by replacing the traditional Evolved Packet Core (EPC) in the LTE network in order to provide efficient communications connections between different end points. Cloudlet Network File System (CNFS) is designed based on the proposed architecture in order to protect Avatars' dataset against hardware failure and improve the Avatars' performance in terms of data access latency. Moreover, green energy supplement is proposed in the architecture in order to reduce the extra Operational Expenditure (OPEX) and CO2 footprint incurred by running the distributed Cloudlets. Owing to the temporal and spatial dynamics of both the green energy generation and energy demands of Green Cloudlet Systems (GCSs), designing an optimal green energy management strategy based on the characteristics of the green energy generation and the energy demands of eNBs and Cloudlets to minimize the on-grid energy consumption is critical to the Cloudlet provider.

Dusit Niyato - One of the best experts on this subject based on the ideXlab platform.

  • Offloading in Mobile Cloudlet Systems with Intermittent Connectivity
    IEEE Transactions on Mobile Computing, 2015
    Co-Authors: Yang Zhang, Dusit Niyato, Ping Wang
    Abstract:

    The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., Cloudlets) to reduce energy consumption and improve performance. However, due to mobility and Cloudlet capacity, the connections between a mobile user and mobile Cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of Cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected Cloudlet system, considering the users’ local load and availability of Cloudlets. We examine users’ mobility patterns and Cloudlets’ admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

  • Optimal energy management policy of a mobile Cloudlet with wireless energy charging
    2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014, 2015
    Co-Authors: Dusit Niyato, Peter Chong Han Joo, Zhu Han, Ping Wang, Dong In Kim
    Abstract:

    A mobile Cloudlet (also called a pocket Cloudlet) is a small computing unit providing data processing and relay transmission services to other nearby mobile devices (i.e., offloading) opportunistically when its computation and communication resources are free. The mobile Cloudlet can request for wireless energy transfer/charging and pay some price to a wireless charger. The mobile Cloudlet can use that energy to run its own applications or process offloaded jobs, and earn some revenue from other mobile devices. In this paper, we study an energy management policy of a mobile Cloudlet, which can be considered as mobile demand response management in smart grid context. Specifically, we formulate a Markov decision process for the mobile Cloudlet to determine whether or not to charge its battery and to accept and process offloaded jobs. We show analytically that the optimal policy of the mobile Cloudlet energy management is a threshold policy. Additionally, we can derive the power demand profile of the wireless charger serving multiple Cloudlets given the optimal policy of each Cloudlet.

  • SmartGridComm - Optimal energy management policy of a mobile Cloudlet with wireless energy charging
    2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014
    Co-Authors: Dusit Niyato, Peter Chong Han Joo, Zhu Han, Ping Wang, Dong In Kim
    Abstract:

    A mobile Cloudlet (also called a pocket Cloudlet) is a small computing unit providing data processing and relay transmission services to other nearby mobile devices (i.e., offloading) opportunistically when its computation and communication resources are free. The mobile Cloudlet can request for wireless energy transfer/charging and pay some price to a wireless charger. The mobile Cloudlet can use that energy to run its own applications or process offloaded jobs, and earn some revenue from other mobile devices. In this paper, we study an energy management policy of a mobile Cloudlet, which can be considered as mobile demand response management in smart grid context. Specifically, we formulate a Markov decision process for the mobile Cloudlet to determine whether or not to charge its battery and to accept and process offloaded jobs. We show analytically that the optimal policy of the mobile Cloudlet energy management is a threshold policy. Additionally, we can derive the power demand profile of the wireless charger serving multiple Cloudlets given the optimal policy of each Cloudlet.

Dong In Kim - One of the best experts on this subject based on the ideXlab platform.

  • Optimal energy management policy of a mobile Cloudlet with wireless energy charging
    2014 IEEE International Conference on Smart Grid Communications, SmartGridComm 2014, 2015
    Co-Authors: Dusit Niyato, Peter Chong Han Joo, Zhu Han, Ping Wang, Dong In Kim
    Abstract:

    A mobile Cloudlet (also called a pocket Cloudlet) is a small computing unit providing data processing and relay transmission services to other nearby mobile devices (i.e., offloading) opportunistically when its computation and communication resources are free. The mobile Cloudlet can request for wireless energy transfer/charging and pay some price to a wireless charger. The mobile Cloudlet can use that energy to run its own applications or process offloaded jobs, and earn some revenue from other mobile devices. In this paper, we study an energy management policy of a mobile Cloudlet, which can be considered as mobile demand response management in smart grid context. Specifically, we formulate a Markov decision process for the mobile Cloudlet to determine whether or not to charge its battery and to accept and process offloaded jobs. We show analytically that the optimal policy of the mobile Cloudlet energy management is a threshold policy. Additionally, we can derive the power demand profile of the wireless charger serving multiple Cloudlets given the optimal policy of each Cloudlet.

  • SmartGridComm - Optimal energy management policy of a mobile Cloudlet with wireless energy charging
    2014 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014
    Co-Authors: Dusit Niyato, Peter Chong Han Joo, Zhu Han, Ping Wang, Dong In Kim
    Abstract:

    A mobile Cloudlet (also called a pocket Cloudlet) is a small computing unit providing data processing and relay transmission services to other nearby mobile devices (i.e., offloading) opportunistically when its computation and communication resources are free. The mobile Cloudlet can request for wireless energy transfer/charging and pay some price to a wireless charger. The mobile Cloudlet can use that energy to run its own applications or process offloaded jobs, and earn some revenue from other mobile devices. In this paper, we study an energy management policy of a mobile Cloudlet, which can be considered as mobile demand response management in smart grid context. Specifically, we formulate a Markov decision process for the mobile Cloudlet to determine whether or not to charge its battery and to accept and process offloaded jobs. We show analytically that the optimal policy of the mobile Cloudlet energy management is a threshold policy. Additionally, we can derive the power demand profile of the wireless charger serving multiple Cloudlets given the optimal policy of each Cloudlet.