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Current Project:

 Scalable Consensus Protocols and System Architectures for Blockchain Services


Bitcoin’s blockchain technology is emerging as an important approach for decentralized management of digital assets ownership. A blockchain is a replicated ledger maintained in a decentralized manner, without requiring a central authority. A distributed consensus protocol is needed to ensure a globally-agreed total order on the blocks in the chain. For this, Bitcoin uses a technique called Proof-of-Work (PoW) which requires each node wanting to append a new block to solve some hard cryptographic puzzle. This PoW-based mechanism of Bitcoin provides probabilistic guarantees for consensus. This leads to several inherent difficulties in scaling its performance. The PoW based consensus technique also requires an inordinate amount of computing and electrical power.

The goal of this project is to investigate alternative approaches for building scalable blockchain services. Our research aims to  use of classical consensus protocols with Byzantine Fault Tolerance (BFT), sharding for parallel execution of validation tasks, alternate data models in place of a linear chain, and use of other trust models and mechanisms in place of the PoW model. Our work is focused on  hybrid environments with both the permissioned model of user participation and Bitcoin’s permissionless open access model. These two models present different kinds of design constraints and challenges. Some of the approaches being investigated are  based on sharding and multi-chain structures for performance scaling and storage efficiency. In place of the PoW model, alternate trust models such Proof-of-Stake (PoS), Proof-of-Authority (PoA), and Proof-of-Elapsed Time (PoET) are being considered in our work.


Recently Completed Project:

Supported by NSF Award 1319333

Computing resources provided by Minnesota Supercomputing Institute


Scalable Transaction Management and Geo-Replication in Cloud Data Storage Services


In this project we are developing scalable techniques for transaction management in Cloud data storage services based on key-value based NoSQL models. Specifically our current focus is on Hadoop/Hbase in a Cloud datacenter environment. Our approach is based on Snapshot Isolation (SI) based concurrency control.  We are investigating scalable techniques and system architectures for supporting serializable SI-based transactions on NoSQL data management services. The broad goal is to provide autonomically scalable transaction management techniques within a datacenter. Another thrust of this research is to develop techniques for transaction management for geographically replicated data across multiple datacenters.  here we are exploring data management techniques supporting a spectrum of consistency guarantees, ranging from eventual  consistency, causal consistency,  to serializability. In this context we have developed the Causally Coordinated Snapshot Isolation model for geo-replicated data.


Beehive: A Parallel Programming Framework for Graph Problems

(Open Source software  released under GNU GPL V3 license)

The Beehive project's focus is on the development of a parallel programming framework which will provide a simple, efficient, and robust model for large-scale graph data analytics applications on large-scale clusters and cloud computing environments. In such applications, parallelism tends to be fine-grain and amorphous, which makes it difficult to extract parallelism at a coarse-grain level using the commonly available techniques. For efficiently harnessing fine-grain amorphous parallelism in graph problems, the the Beehive computing model is based on speculative parallel execution of tasks in a cluster computing environment. This approach is supported providing a key-value based in-memory storage implemented on a cluster of computers, for storing and manipulating graph data. The intermediate results of the parallel computations all stored in the shared storage and exposed to all the processes. In this model multiple tasks are scheduled to execute in parallel, and each task is executed as a transaction, ensuring the atomicity and isolation properties of concurrent task executions. This project is investigating techniques for supporting transactional executing of parallel tasks and methods for ensuring fault-tolerance in large-scale computing problems.


Past Projects

Middleware for Scalable Location Based Services

In pervasive and mobile computing systems, there is a growing interest in location-based services (LBS). The focus of this project is on the development of a middleware architecture for building location-based services (LBS). This project is addressing the requirements of a variety of LBS applications such as find-a-friend in mobile social network groups, location-based advertisements in M-commerce, traffic alerts and public safety notifications, workflow management for mobile workers, public transit information assistance for commuters, location-based messages, geo-notes, and location-based micro-blogs. The goal of the project is to develop a middleware architecture that would  support design and deployment of such variety of location-based services over the Internet. It would provide a fabric for communication and interactions between the services and the mobile users based on a publish/subscribe model. The middleware services and components would be provisioned through Internet-based computing clusters or cloud environments. The proposed research is investigating client-plus-cloud paradigms for fro scalable deployment of LBS services.



Ellora Framework for Resilient Internet Services 

This project is investigating a system architecture for building highly available services that are resilient to such adverse conditions such as overload situations, attacks, network outages. The techniques being investigated are based on dynamic replication, relocation and regeneration of services in case of overload conditions. This research is utilizing the Ajanta mobile agent framework for deployment, relocation, and replication of service components. This project is being conducted using the facilities of the PlanetLab infrastructure, which poses unique challenges as the resource capacities available to a service are based on the proportional share model. The resource capacity available at at node can change unpredictably due to usage by other users and applications. This project is investigating techniques for dynamic scaling of service capacity and load distribution. This project has developed autonomic mechanisms for service scaling and fault-tolerance. We also developed an infrastructure service, called for monitoring the PlanetLab nodes for available resource capacities in order to assist a service agent in selecting a target node for relocation.


Secure Context Aware Distributed Collaboration Systems

This project developed a generative programming based approach for developing context-aware applications in active spaces. Building upon the programming model and the middleware developed in the distributed collaboration system project, mechanisms and specification models were developed for dynamic discovery and binding of ambient services and resources in multi-user applications developed in active-space environments based the context conditions and physical locations of users. The focus of our work was on security and robustness.  This work resulted in the development of the Context-Aware Role Based Access Control Model (CA-RBAC) and an exception handling model to deal with binding failures and services failures.

Policy Driven Secure Distributed Collaboration

This project developed a policy-driven middleware for building secure distributed collaboration systems from their high level specifications. Our specification model supports nested collaboration activities and uses role based security policies and event count based coordination specification. From the specifications of a collaboration environment, appropriate policy modules are derived for enforcing security and coordination requirements. A policy-driven distributed middleware provides services to the users to join roles in an activity, perform role specific operations, or create new activities.   In our model, a policy-driven collaboration system is realized in three steps. Initially, the coordination and security policy for a collaboration is specified based on a schema. From the specification, various policy modules are derived for different kinds of requirements, such as role based security, object level access control, and event notification for coordination. Finally, through these modules, the collaboration environment is realized by a generic middleware. We have developed a specification model using XML, in which a collaborative system is defined in terms of activities, roles and objects. The model allows dynamic assignments of roles, ''separation of duties'' constraints, multiple user participation in a role, active security policies, and hierarchical activity definitions. We used SPIN to develop model checking techniques for verifying the security properties of a design.


Konark System Agent-Based Distributed Event Stream Processing

Konark is an agent-based event stream processing system based on the Ajanta platform. In Konark, mobile-agents are used for  monitoring nodes in a network to detect and communicate events to other agents for further filtering, aggregation and correlation.  Event are communicated among agents using a publish-subscribe model. An agent can subscribe to events from different remote agents in the network to perform event filtering and  correlation functions. We also developed policy-driven autonomic mechanisms for  configuration and resilient operations of an ensemble of Konark agents in an event stream processing application.  We used the  Konark system for developing a  system for monitoring computers in our lab facilities for attacks and intrusions. The Konark system was also used for building context detection service based on monitoring of events from RFID readers and Bluetooth device sensors for experiments in context aware distributed collaboration systems,


Ajanta Project (1997-2002) 

Ajanta mobile agent programming framework was developed during 1997-2002 at the University of Minnesota. A mobile agent is a Java object that which cab  securely migrate over the Internet to perform designated tasks at one or more nodes.The Ajanta system provides an infrastructure for secure and robust execution of mobile agents. In a broad sense, a mobile agent is a program which represents a user in a network and is capable of migrating autonomously from node to node, performing computations on behalf of the user. The programmer can define agents as active application components that traverse the network performing computations relevant to their current location. For example, agents can be used for information searching, filtering and retrieval, and for electronic commerce on the Web, thus acting as personal assistants for their owners. As tools for system administration, they can be used in low­level network maintenance, testing, fault diagnosis, and for installing or upgrading software on remote machines. Agents are also useful for extending or modifying the capabilities of existing services by dynamically adding to their functionality. Ajanta is implemented using the Java language and its security mechanisms are designed based on Java's security model. It also makes use of several other facilities of Java, such as object serialization, reflection, and remote method invocation.




National Science Foundation Awards: CNS 1319333,  CNS 0834357, NSF 0411961,  ITR 0082215, ANI 0087514 , CNS 0708604, EIA 9818338, ANIR 9813703,


Minnesota Supercomputing Institute has provided computing resources for these projects.



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