An approach to rollback recovery of collaborating mobile agents

Fault-tolerance is one of the main problems that must be resolved to improve the adoption of the agents' computing paradigm. In this paper, we analyse the execution model of agent platforms and the significance of the faults affecting their constituent components on the reliable execution of agent-based applications, in order to develop a pragmatic framework for agent systems fault-tolerance. The developed framework deploys a communication-pairs independent check pointing strategy to offer a low-cost, application-transparent model for reliable agent- based computing that covers all possible faults that might invalidate reliable agent execution, migration and communication and maintains the exactly-one execution property

CIC : an integrated approach to checkpointing in mobile agent systems

Internet and Mobile Computing Lab (in Department of Computing)Refereed conference paper2006-2007 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Transparently Mixing Undo Logs and Software Reversibility for State Recovery in Optimistic PDES

The rollback operation is a fundamental building block to support the correct execution of a speculative Time Warp-based Parallel Discrete Event Simulation. In the literature, several solutions to reduce the execution cost of this operation have been proposed, either based on the creation of a checkpoint of previous simulation state images, or on the execution of negative copies of simulation events which are able to undo the updates on the state. In this paper, we explore the practical design and implementation of a state recoverability technique which allows to restore a previous simulation state either relying on checkpointing or on the reverse execution of the state updates occurred while processing events in forward mode. Differently from other proposals, we address the issue of executing backward updates in a fully-transparent and event granularity-independent way, by relying on static software instrumentation (targeting the x86 architecture and Linux systems) to generate at runtime reverse update code blocks (not to be confused with reverse events, proper of the reverse computing approach). These are able to undo the effects of a forward execution while minimizing the cost of the undo operation. We also present experimental results related to our implementation, which is released as free software and fully integrated into the open source ROOT-Sim (ROme OpTimistic Simulator) package. The experimental data support the viability and effectiveness of our proposal

Proxy Module for System on Mobile Devices (SyD) Middleware

Nowadays, users of mobile devices are growing. The users expect that they could communicate constantly using their mobile devices while they are also constantly moving. Therefore, there is a need to provide disconnection tolerance of transactions in the mobile devices’ platforms and its synchronization management. System on Mobile Devices (SyD) is taken as one of the examples of mobile devices’ platforms. The thesis studies the existing SyD architecture, from its framework into its kernel, and introduces the proxy module enhancement in SyD to handle disconnection tolerance, including its synchronization. SyD kernel has been extended for the purpose of enabling proxy module. SyDSync has been constructed for synchronization with the proxy. The timeout has been studied for seamless proxy invocation. A Camera application that tries to catch a stolen vehicle has been simulated for the practical purpose of using the proxy module extension

Coordinated collaboration for e-commerce based on the multiagent paradigm.

Lee Ting-on.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 116-121).Abstracts in English and Chinese.Acknowledgments --- p.iAbstract --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Roadmap to the Thesis --- p.5Chapter 2 --- Software Agents and Agent Frameworks --- p.7Chapter 2.1 --- Software Agent --- p.7Chapter 2.1.1 --- Advantages of Agent --- p.10Chapter 2.1.2 --- Roles of Agent --- p.11Chapter 2.2 --- Agent Frameworks --- p.13Chapter 2.3 --- Communication Services and Concepts --- p.15Chapter 2.3.1 --- Message Channel --- p.15Chapter 2.3.2 --- Remote Procedure Call --- p.16Chapter 2.3.3 --- Event Channel --- p.17Chapter 2.4 --- Component --- p.18Chapter 3 --- Related Work --- p.20Chapter 3.1 --- Collaboration Behaviors --- p.20Chapter 3.2 --- Direct Coordination --- p.22Chapter 3.3 --- Meeting-oriented Coordination --- p.23Chapter 3.4 --- Blackboard-based Coordination --- p.24Chapter 3.5 --- Linda-like Coordination --- p.25Chapter 3.6 --- Reactive Tuple Spaces --- p.26Chapter 4 --- Background and Foundations --- p.27Chapter 4.1 --- Choice of Technologies --- p.27Chapter 4.2 --- Jini Technology --- p.28Chapter 4.2.1 --- The Lookup Service --- p.29Chapter 4.2.2 --- Proxy --- p.31Chapter 4.3 --- JavaSpaces --- p.32Chapter 4.4 --- Grasshopper Architecture --- p.33Chapter 5 --- The CoDAC Framework --- p.36Chapter 5.1 --- Requirements for Enabling Collaboration --- p.37Chapter 5.1.1 --- Consistent Group Membership --- p.37Chapter 5.1.2 --- Atomic Commitment --- p.39Chapter 5.1.3 --- Uniform Reliable Multicast --- p.40Chapter 5.1.4 --- Fault Tolerance --- p.40Chapter 5.2 --- System Components --- p.41Chapter 5.2.1 --- Distributed Agent Adapter --- p.42Chapter 5.2.2 --- CollaborationCore --- p.44Chapter 5.3 --- System Infrastructure --- p.45Chapter 5.3.1 --- Agent --- p.45Chapter 5.3.2 --- Distributed Agent Manager --- p.46Chapter 5.3.3 --- Collaboration Manager --- p.46Chapter 5.3.4 --- Kernel --- p.46Chapter 5.4 --- Collaboration --- p.47Chapter 5.5.1 --- Global Collaboration --- p.48Chapter 5.5.2 --- Local Collaboration --- p.48Chapter 6 --- Collaboration Life Cycle --- p.50Chapter 6.1 --- Initialization --- p.50Chapter 6.2 --- Resouces Gathering --- p.53Chapter 6.3 --- Results Delivery --- p.54Chapter 7 --- Protocol Suite --- p.55Chapter 7.1 --- The Group Membership Protocol --- p.56Chapter 7.1.1 --- Join Protocol --- p.56Chapter 7.1.2 --- Leave Protocol --- p.57Chapter 7.1.3 --- Recovery Protocol --- p.59Chapter 7.1.4 --- Proof --- p.61Chapter 7.2 --- Atomic Commitment Protocol --- p.62Chapter 7.3 --- Uniform Reliable Multicast --- p.63Chapter Chapter 8 --- Implementation --- p.66Chapter 8.1 --- Interfaces and Classes --- p.66Chapter 8.1.1 --- The CoDACAdapterInterface --- p.66Chapter 8.1.2 --- The CoDACEventListener --- p.69Chapter 8.1.3 --- The DAAdapter --- p.71Chapter 8.1.4 --- The DAManager --- p.75Chapter 8.1.5 --- The CoDACInternalEventListener --- p.77Chapter 8.1.6 --- The CollaborationManager --- p.77Chapter 8.1.7 --- The CollaborationCore --- p.78Chapter 8.2 --- Messaging Mechanism --- p.79Chapter 8.3 --- Nested Transaction --- p.84Chapter 8.4 --- Fault Detection --- p.85Chapter 8.5 --- Atomic Commitment Protocol --- p.88Chapter 8.5.1 --- Message Flow --- p.89Chapter 8.5.2 --- Timeout Actions --- p.91Chapter Chapter 9 --- Example --- p.93Chapter 9.1 --- System Model --- p.93Chapter 9.2 --- Auction Lifecycle --- p.94Chapter 9.2.1 --- Initialization --- p.94Chapter 9.2.2 --- Resource Gathering --- p.98Chapter 9.2.3 --- Results Delivery --- p.100Chapter Chapter 10 --- Discussions --- p.104Chapter 10.1 --- Compatibility --- p.104Chapter 10.2 --- Hierarchical Group Infrastructure --- p.106Chapter 10.3 --- Flexibility --- p.107Chapter 10.4 --- Atomicity --- p.108Chapter 10.5 --- Fault Tolerance --- p.109Chapter Chapter 11 --- Conclusion and Future Work --- p.111Chapter 11.1 --- Conclusion --- p.111Chapter 11.2 --- Future Work --- p.112Chapter 11.2.1 --- Electronic Commerce --- p.112Chapter 11.2.2 --- Workflow Management --- p.114Bibliography --- p.116Publication List --- p.12

Big SaaS: The Next Step Beyond Big Data

Software-as-a-Service (SaaS) is a model of cloud computing in which software functions are delivered to the users as services. The past few years have witnessed its global flourishing. In the foreseeable future, SaaS applications will integrate with the Internet of Things, Mobile Computing, Big Data, Wireless Sensor Networks, and many other computing and communication technologies to deliver customizable intelligent services to a vast population. This will give rise to an era of what we call Big SaaS systems of unprecedented complexity and scale. They will have huge numbers of tenants/users interrelated in complex ways. The code will be complex too and require Big Data but provide great value to the customer. With these benefits come great societal risks, however, and there are other drawbacks and challenges. For example, it is difficult to ensure the quality of data and metadata obtained from crowdsourcing and to maintain the integrity of conceptual model. Big SaaS applications will also need to evolve continuously. This paper will discuss how to address these challenges at all stages of the software lifecycle

A Taxonomy of Virtualization Security Issues in Cloud Computing Environments

Objectives: To identify the main challenges and security issues of virtualization in cloud computing environments. It reviews the alleviation techniques for improving the security of cloud virtualization systems. Methods/ Statistical Analysis: Virtualization is a fundamental technology for cloud computing, and for this reason, any cloud vulnerabilities and threats affect virtualization. In this study, the systematic literature review is performed to find out the vulnerabilities and risks of virtualization in cloud computing and to identify threats, and attacks result from those vulnerabilities. Furthermore, we discover and analyze the effective mitigation techniques that are used to protect, secure, and manage virtualization environments. Findings: Thirty vulnerabilities are identified, explained, and classified into six proposed classes. Furthermore, fifteen main virtualization threats and attacks ar defined according to exploited vulnerabilities in a cloud environment. Application/Improvements: A set of common mitigation solutions are recognized and discovered to alleviate the virtualization security risks. These reviewed techniques are analyzed and evaluated according to five specified security criteria

Automating Fault Tolerance in High-Performance Computational Biological Jobs Using Multi-Agent Approaches

Background: Large-scale biological jobs on high-performance computing systems require manual intervention if one or more computing cores on which they execute fail. This places not only a cost on the maintenance of the job, but also a cost on the time taken for reinstating the job and the risk of losing data and execution accomplished by the job before it failed. Approaches which can proactively detect computing core failures and take action to relocate the computing core's job onto reliable cores can make a significant step towards automating fault tolerance. Method: This paper describes an experimental investigation into the use of multi-agent approaches for fault tolerance. Two approaches are studied, the first at the job level and the second at the core level. The approaches are investigated for single core failure scenarios that can occur in the execution of parallel reduction algorithms on computer clusters. A third approach is proposed that incorporates multi-agent technology both at the job and core level. Experiments are pursued in the context of genome searching, a popular computational biology application. Result: The key conclusion is that the approaches proposed are feasible for automating fault tolerance in high-performance computing systems with minimal human intervention. In a typical experiment in which the fault tolerance is studied, centralised and decentralised checkpointing approaches on an average add 90% to the actual time for executing the job. On the other hand, in the same experiment the multi-agent approaches add only 10% to the overall execution time.Comment: Computers in Biology and Medicin