Result Integrity Check for MapReduce Computation on Hybrid Clouds

Abstract — Large scale adoption of MapReduce computations on public clouds is hindered by the lack of trust on the participat-ing virtual machines, because misbehaving worker nodes can compromise the integrity of the computation result. In this paper, we propose a novel MapReduce framework, Cross Cloud MapRe-duce (CCMR), which overlays the MapReduce computation on top of a hybrid cloud: the master that is in control of the entire computation and guarantees result integrity runs on a private and trusted cloud, while normal workers run on a public cloud. In order to achieve high accuracy, CCMR proposes a result integrity check scheme on both the map phase and the reduce phase, which combines random task replication, random task verification, and credit accumulation; and CCMR strives to reduce the overhead by reducing cross-cloud communication. We implement our ap-proach based on Apache Hadoop MapReduce and evaluate our implementation on Amazon EC2. Both theoretical and experi-mental analysis show that our approach can guarantee high result integrity in a normal cloud environment while incurring non-negligible performance overhead (e.g., when 16.7 % workers are malicious, CCMR can guarantee at least 99.52 % of accuracy with 33.6 % of overhead when replication probability is 0.3 and the credit threshold is 50)

Referential integrity and dependencies between documents in a document oriented database

Reliability of foreign keys, which is natural in relationaldatabases, requires additional efforts when working withnon-relational databases, as non-relational database managementsystems generally don’t support foreign key constraints due totheir distributed nature. Referential integrity is an importantproperty whenever documents need to refer to each other, whichis the common case. This work discusses an implementationof a verification approach which makes use of the MapReduceprogramming model, in order to detect incorrect references indocument oriented databases that may be caused by errors inthe program code or incomplete transactions. Furthermore, themethod can be applied for the verification of more complex dependenciesbetween documents, such that bind aggregated valuesfrom certain sets of documents with the values of documentsreferred by them

A component-based framework for certification of components in a cloud of HPC services

HPC Shelfis a proposal of a cloud computing platform to provide component-oriented services for High Performance Computing (HPC) applications. This paper presents a Verification-as-a-Service (VaaS) framework for component certification onHPC Shelf. Certification is aimed at providing higher confidence that components of parallel computing systems ofHPC Shelfbehave as expected according to one or more requirements expressed in their contracts. To this end, new abstractions are introduced, starting with certifier components. They are designed to inspect other components and verify them for different types of functional, non-functional and behavioral requirements. The certification framework is naturally based on parallel computing techniques to speed up verification tasks.NORTE-01-0145- FEDER-000037

Towards Secure Cloud Data Management

This paper explores the security challenges posed by data-intensive applications deployed in cloud environments that span administrative and network domains. We propose a data-centric view of cloud security and discuss data management challenges in the areas of secure distributed data processing, end-to-end query result verification, and cross-user trust policy management. In addition, we describe our current and future efforts to investigate security challenges in cloud data management using the Declarative Secure Distributed Systems (DS2) platform, a declarative infrastructure for specifying, analyzing, and deploying secure information systems

Hybrid Cloud Model Checking Using the Interaction Layer of HARMS for Ambient Intelligent Systems

Soon, humans will be co-living and taking advantage of the help of multi-agent systems in a broader way than the present. Such systems will involve machines or devices of any variety, including robots. These kind of solutions will adapt to the special needs of each individual. However, to the concern of this research effort, systems like the ones mentioned above might encounter situations that will not be seen before execution time. It is understood that there are two possible outcomes that could materialize; either keep working without corrective measures, which could lead to an entirely different end or completely stop working. Both results should be avoided, specially in cases where the end user will depend on a high level guidance provided by the system, such as in ambient intelligence applications. This dissertation worked towards two specific goals. First, to assure that the system will always work, independently of which of the agents performs the different tasks needed to accomplish a bigger objective. Second, to provide initial steps towards autonomous survivable systems which can change their future actions in order to achieve the original final goals. Therefore, the use of the third layer of the HARMS model was proposed to insure the indistinguishability of the actors accomplishing each task and sub-task without regard of the intrinsic complexity of the activity. Additionally, a framework was proposed using model checking methodology during run-time for providing possible solutions to issues encountered in execution time, as a part of the survivability feature of the systems final goals