Call for Papers The 6^(th) International Conference on Grid and Cooperative Computing

Recent years have seen rapid advances in various grid-related technologies, middleware, and applications. The GCC conference has become one of the largest scientific events worldwide in grid and cooperative computing. The 6th international conference on grid and cooperative computing (GCC2007) Sponsored by China Computer Federation (CCF),Institute of Computing Technology, Chinese Academy of Sciences (ICT) and Xinjiang University ,and in Cooperation with IEEE Computer Soceity ,is to be held from August 16 to 18, 2007 in Urumchi, Xinjiang, China.

A Distributed Computing Algorithm for Electricity Carbon Emission Flow and Carbon Emission Intensity

The calculation of the indirect carbon emis-sion is essential for power system policy making,carbon market development,and power grid planning.The em-bedded carbon emissions of the electricity system are commonly calculated by carbon emission flow theory.However,the calculation procedure is time-consuming,especially for a country with 500-1000 thousand nodes,making it challenging to obtain nationwide carbon emis-sions intensity precisely.Additionally,the calculation procedure requires to gather all the grid data with high classified levels from different power grid companies,which can prevent data sharing and cooperation among different companies.This paper proposes a distributed computing algorithm for indirect carbon emission that can reduce the time consumption and provide privacy protection.The core idea is to utilize the sparsity of the nodes’flow matrix of the nationwide grid to partition the computing procedure into parallel sub-procedures exe-cuted in multiple terminals.The flow and structure data of the regional grid are transformed irreversibly for pri-vacy protection,when transmitted between terminals.A 1-master-and-N-slave layout is adopted to verify the method.This algorithm is suitable for large grid compa-nies with headquarter and branches in provinces,such as the State Grid Corporation of China.

Learning-Based Joint Service Caching and Load Balancing for MEC Blockchain Networks

Integrating the blockchain technology into mobile-edge computing(MEC)networks with multiple cooperative MEC servers(MECS)providing a promising solution to improving resource utilization,and helping establish a secure reward mechanism that can facilitate load balancing among MECS.In addition,intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads.In this paper,we investigate a learningbased joint service caching and load balancing policy for optimizing the communication and computation resources allocation,so as to improve the resource utilization of MEC blockchain networks.We formulate the problem as a challenging long-term network revenue maximization Markov decision process(MDP)problem.To address the highly dynamic and high dimension of system states,we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network(DQN)approach.The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.

An Efficient Framework to Utilize Grover Search

This paper proposes an efficient framework to utilize quantum search practically.To the best of our knowledge,this is the first paper to show a concrete usage of quantum search in general programming.In our framework,we can utilize a quantum computer as a coprocessor to speed-up some parts of a program that runs on a classical computer.To do so,we propose several new ideas and techniques,such as a practical method to design a large quantum circuits for search problems and an efficient quantum comparator.

Joint Communication and Computation Optimization for Wireless Powered Mobile Edge Computing with D2D Offloading

This paper studies a wireless powered mobile edge computing(MEC)system with device-to-device(D2D)-enabled task offloading.In this system,a set of distributed multi-antenna energy transmitters(ETs)use collaborative energy beamforming to wirelessly charge multiple users.By using the harvested energy,the actively computing user nodes can offload their computation tasks to nearby idle users(as helper nodes)via D2D communication links for self-sustainable remote computing.We consider the frequency division multiple access(FDMA)protocol,such that the D2D communications of different user-helper pairs are implemented over orthogonal frequency bands.Furthermore,we focus on a particular time block for task execution,which is divided into three slots for computation task offloading,remote computing,and result downloading,respectively,at different user-helper pairs.Under this setup,we jointly optimize the collaborative energy beamforming at ETs,the communication and computation resource allocation at users and helpers,and the user-helper pairing,so as to maximize the sum computation rate(i.e.,the number of task input-bits executed over this block)of the users,subject to individual energy neutrality constraints at both users and helpers.First,we consider the computation rate maximization problem under any given user-helper pairs,for which an efficient solution is proposed by using the techniques of alternating optimization and convex optimization.Next,we develop the optimal user-helper pairing scheme based on exhaustive search and a low-complexity scheme based on greedy selection.Numerical results show that the proposed design significantly improves the sum computation rate at users,as compared to benchmark schemes without such joint optimization.