Modern supply chain is a complex system and plays an important role for different sectors under the globalization economic integration background. Supply chain management system is proposed to handle the increasing complexity and improve the efficiency of flows of goods. It is also useful to prevent potential frauds and guarantee trade compliance. Currently, most companies maintain their own IT system for supply chain management. However, this approach has some limitations that prevent one to get most of the supply chain information. Using emerging decentralized ledger technology to build supply chain management system is a promising direction. However, decentralized ledger usually suffers from low performance and lack of capability to protect information stored on the ledger. To overcome these challenges, we propose CoC, a novel supply chain management system based on hybrid decentralized ledger. We develop an efficient block construction method with the model and security mechanism to prevent unauthorized access to data stored on the ledger.

Blockchain, or distributed ledger, provides a way to build various decentralized systems without relying on any single trusted party. This is especially attractive for smart contracts, that different parties do not need to trust each other to have a contract, and the distributed ledger can guarantee correct execution of the contract. Most existing distributed ledger based smart contract systems process smart contracts in a serial manner, i.e., all users have to run a contract before its result can be accepted by the system. Although this approach is easy to implement and manage, it is not scalable and greatly limits the system's capability of handling a large number of smart contracts. In order to address this problem, we propose a scalable smart contract execution scheme that can run multiple smart contract in parallel to improve throughput of the system. Our scheme relies on two key techniques: a fair contract partition algorithm leveraging integer linear programming to partition a set of smart contracts into multiple subsets, and a random assignment protocol assigning subsets randomly to a subgroup of users. We prove that, our scheme is secure as long as more than 50% of the computational power is possessed by honest nodes. We then conduct experiments with data from existing smart contract system to evaluate the efficiency of our scheme. The results demonstrate that our approach is scalable and much more efficient than the existing smart contract platform.

Transaction system build on top of blockchain, especially smart contract, is becoming an important part of world economy. However, there is a lack of formal study on the behavior of users in these systems, which leaves the correctness and security of such system without a solid foundation. Unlike mining, in which the reward for mining a block is fixed, different execution results of a smart contract may lead to significantly different payoffs of users, which gives more incentives for some user to follow a branch that contains a wrong result, even if the branch is shorter. It is thus important to understand the exact probability that a branch is being selected by the system. We formulate this problem as the (+-)-Biased Ballot Problem as follows: there are n voters one by one voting for either of the two candidates A and B. The probability of a user voting for A or B depends on whether the difference between the current votes of A and B is positive or negative. Our model takes into account the behavior of three different kinds of users when a branch occurs in the system -- users having preference over a certain branch based on the history of their transactions, and users being indifferent and simply follow the longest chain. We study two important probabilities that are closely related with a blockchain based system - the probability that A wins at last, and the probability that A receives d votes first. We show how to recursively calculate the two probabilities for any fixed n and d, and also discuss their asymptotic values when n and d are sufficiently large.

Recent advances in virtualization technology and wide acceptance of the cloud computing model are having significant impact on the software service industry. Though cloud computing and virtualization technology has been widely applied in supporting the information processing needs of conventional enterprise and business applications, there has been little success to-date in enabling realtime 3D virtual appliances in the cloud. This paper aims to address this deficiency by presenting SHARC, a solution for enabling scalable support of realtime 3D applications in a cloud computing environment. The solution uses a scalable pipelined processing infrastructure which consists of three processing networks according to the principle of division-of-labor, a virtualization server network for running 3D virtual appliances, a graphics rendering network for processing graphics rendering workload with load balancing, and a media streaming network for transcoding rendered frames into H.264/MPEG-4 media streams and streaming the media streams to a cloud user. The paper describes a prototype implementation of SHARC and reports test results that demonstrate the viability of this approach.