In this paper, we introduce a framework of distributed orthogonal approximate message passing for recovering sparse vector based on sensing by multiple nodes. The iterative recovery process consists of local computation at each node, and global computation performed either by a particular node or joint computation on the overall network by exchanging messages. We then propose a method to reduce the communication cost between the nodes while maintaining the recovery performance.

We formally define the mobile agent allocation problem from a system-wide viewpoint and then prove that it is strongly NP-complete even if each agent communicates only with two agents. This is the first formal definition for scheduling mobile agents from the viewpoint of load balancing, which enables us to discuss its properties on a rigorous basis. The problem is recognized as preemptive scheduling with independent tasks that require mutual communication. The result implies that almost all subproblems of mobile agent allocation, which require mutual communication of agents, are strongly NP-complete.

Data Replication can be used to improve the system availability of distributed systems. In such a system, a mechanism is required to maintain the consistency of the replicated data. The grid structure technique based on quorum is one of the solutions to perform this while providing a high availability of the system. It was shown in the study that, it still requires a bigger number of copies be made available to construct a quorum. So it is not suitable for large systems. In this paper, we propose a technique called the neighbor replication on grid (NRG) technique by considering only neighbors to have the replicated data. In comparison to the grid structure technique, NRG requires a lower communication cost for an operation, while providing a higher system availability, which is preferred for large systems.

This paper first examines the issues related to scheduling loop applications on a software distributed shared memory (DSM) system. Then, a dynamic scheduling scheme is developed based on the examined issues to enhance the performance of loop applications on DSM. Compared with previous works, the proposed scheme has several specialties. The first is that the workload of processors can be effectively balanced even when the computational capabilities of processors and the computational needs of threads are not identical. The second is it divides thread mapping into two phases, each with one consideration, i.e., load balance or communication cost, and adopts thread migration and exchange in the two phases, respectively. The third is the exploitation of data sharing among threads to reduce data-consistency communication, and the last is to attack the negative effect of the unnecessary inter-node sharing caused by thread re-mapping. The proposed scheme has been implemented on a page-based DSM system called Cohesion. Our experiments show that the proposed scheme is more effective to improve the performance of the test programs than related schemes.

Auctions are a critical element of the electronic commerce infrastructure. But for real-time applications, auctions are a potential problem - they can cause significant time delays. Thus, for most real-time applications, sealed-bid auctions are recommended. But how do we handle tie-breaking in sealed-bid auctions? This paper analyzes the use of multi-round auctions where the winners from an auction round participate in a subsequent tie-breaking second auction round. We perform this analysis over the classical first-price sealed-bid auction that has been modified to provide full anonymity. We analyze the expected number of rounds and optimal values to minimize communication costs.

This paper describes a new approach to the scheduling problem that assigns tasks of a parallel program described as a task graph onto parallel machines. The approach handles interprocessor communication and heterogeneity, based on using both the theoretical results developed so far and a lookahead scheduling strategy. The experimental results on randomly generated task graphs demonstrate the effectiveness of this scheduling heuristic.

This paper investigates the problem of scheduling parallel tasks" with consideration of communication cost on an m-processor system, where processors are assumed to be identical and tasks being scheduled are independent such that they can run on more than one processor simultaneously. Once a task is processed in parallel, its finish time will be speeded up, but communication cost will also be incurred and should be taken into account. To find a schedule with minimum finish time for the parallel tasks scheduling problem is NP-hard. Therefore, in this paper, we will propose a heuristic algorithm for this kind of problem and derive its performance bounds for two different cases of applications, respectively.