Enterprises have paid attention to consortium blockchains, in which multiple authorized organizations participate to form a consortium, in contrast to public blockchains, which consist of unspecified participants. Since a system using a blockchain is based on participants providing/using resources such as nodes to each other, a mechanism to motivate participants to provide resources is important. Typically, a public blockchain realizes an incentive mechanism for providing nodes by unspecified participants through mining rewards and transaction fees associated with a consensus algorithm such as PoW on the platform layer. On the other hand, a consortium blockchain usually does not have an incentive mechanism in the platform itself. In order to maintain and operate a consortium, it is essential that participating organizations provide resources or pay the costs of providing resources. However, because participating organizations use different percentages of the resources, a uniform fee could cause unfairness among the organizations. Therefore, it is necessary to manage billing to remove the gap between the costs borne and the actual usage or/and the unfairness among participants in the consortium. This research aims to realize a billing management method for consortium blockchain-based systems. We propose a smart contract-based service billing management mechanism named "BillingOpsSC" to ensure cross-organizational transparency for billing calculation and management. Furthermore, to ensure fairness among participants, we design a billing calculation model based on metering of the "contribution" by each organization such as providing resources, and the system "usage" by each organization, in the consortium. We implement a prototype of the proposed method including the billing management mechanism and calculation model for Hyperledger Fabric, which is one of the major consortium blockchains, and evaluate the method using the prototype. The results show that our proposed method is effective in ensuring transparency and fairness in billing management among multiple organizations.

Turning towards exascale systems and beyond, it has been widely argued that the currently available systems software is not going to be feasible due to various requirements such as the ability to deal with heterogeneous architectures, the need for systems level optimization targeting specific applications, elimination of OS noise, and at the same time, compatibility with legacy applications. To cope with these issues, a hybrid design of operating systems where light-weight specialized kernels can cooperate with a traditional OS kernel seems adequate, and a number of recent research projects are now heading into this direction. This paper presents Interface for Heterogeneous Kernels (IHK), a general framework enabling hybrid kernel designs in systems equipped with manycore processors and/or accelerators. IHK provides a range of capabilities, such as resource partitioning, management of heterogeneous OS kernels, as well as a low-level communication layer among the kernels. We describe IHK's interface and demonstrate its feasibility for hybrid kernel designs through executing various different lightweight OS kernels on top of it, which are specialized for certain types of applications. We use the Intel Xeon Phi, Intel's latest manycore coprocessor, as our experimental platform.

As a method for running multiple operating systems on one machine, we propose a new resource partitioning method we have named "single hardware with independent multiple operating systems" (SHIMOS). In SHIMOS, CPU and memory resources are partitioned by multiple native kernels without any architectural virtualization supports. There is nearly no slowdown, unlike VMs, because the kernel and user programs are executed directly by the real CPUs. To evaluate our method, we compare an implementation on x86 with VMs by running benchmarks simultaneously. From this comparison, we show that the method proposed is as fast as a real machine, and that it can be twice as fast as the existing VM method in executing I/O- oriented processes.