An efficient deadlock prevention approach for service oriented transaction processing

Transaction processing can guarantee the reliability of business applications. Locking resources is widely used in distributed transaction management (e.g., two phase commit, 2PC) to keep the system consistent. The locking mechanism, however, potentially results in various deadlocks. In service oriented architecture (SOA), the deadlock problem becomes even worse because multiple (sub)transactions try to lock shared resources in the unexpectable way due to the more randomicity of transaction requests, which has not been solved by existing research results. In this paper, we investigate how to prevent local deadlocks, caused by the resource competition among multiple sub-transactions of a global transaction, and global deadlocks from the competition among different global transactions. We propose a replication based approach to avoid the local deadlocks, and a timestamp based approach to significantly mitigate the global deadlocks. A general algorithm is designed for both local and global deadlock prevention. The experimental results demonstrate the effectiveness and efficiency of our deadlock prevention approach. Further, it is also proved that our approach provides higher system performance than traditional resource allocation schemes. © 2011 Elsevier Ltd. All rights reserved.link_to_subscribed_fulltex

A comparative study of concurrency control algorithms for distributed databases

The declining cost of computer hardware and the increasing data processing needs of geographically dispersed organizations have led to substantial interest in distributed data management. These characteristics have led to reconsider the design of centralized databases. Distributed databases have appeared as a result of those considerations. A number of advantages result from having duplicate copies of data in a distributed databases. Some of these advantages are: increased data accesibility, more responsive data access, higher reliability, and load sharing. These and other benefits must be balanced against the additional cost and complexity introduced in doing so. This thesis considers the problem of concurrency control of multiple copy databases. Several synchronization techniques are mentioned and a few algorithms for concurrency control are evaluated and compared

Upgraded Deadlock Averting Algorithms in Distributed Systems

Distributed system deadlock is like ordinary deadlock but it is difficult to prevent or detect when it is traced down. In the distributed system all, the related information is distributed over many machines. However, deadlock in distributed systems is tremendously serious. Therefore, it is important to understand how this deadlock is different from the ordinary deadlock and how to prevent it. To prevent deadlock in the distributed system there are two techniques to prevent it one wound-wait and other is wait-die. Therefore, the problem in these algorithms are that they just attend to the timestamp of the process but not the priority of them but in the real operating system priority of the process is very important. In this paper, we present upgraded deadlock averting algorithms and these algorithms are deal with both priority and time stamp of processes

A categorization scheme for concurrency control protocols in distributed databases

The problem of concurrency control in distributed databases is very complex. As a result, a great number of control algorithms have been proposed in recent years. This research is aimed at the development of a viable categorization scheme for these various algorithms. The scheme is based on the theoretical concept of serializability, but is qualitative in nature. An important class of serializable execution sequences, conflict-preserving-serializable, leads to the identification of fundamental attributes common to all algorithms included in this study. These attributes serve as the underlying philosophy for the categorization scheme. Combined with the two logical approaches of prevention and correction of nonserializability, the result is a flexible and extensive categorization scheme which accounts for all algorithms studied and suggests the possibility of new algorithms

Transaction management on collaborative application services

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2000.Includes bibliographical references (leaves 86-87).by Koon-Po Paul Wong.M.Eng

A systems approach to synchronization and naming in a distributed computing environment

This thesis describes the development of a distributed computer architecture that supports the interconnection of loosely-coupled computing resources (sites) by heterogeneous communication networks. The internetwork system, named the intelligent message transport system, provides for reliable delivery of intersite messages even though the interconnecting networks may use a probabilistic ( best-effort ) delivery scheme. Processors are interfaced to each other by a network gateway processor. The gateway processors are autonomous network controllers that provide a simple and consistent site interface to the internetwork system. The gateway processors also provide for high level functions at the Transport Layer of the system rather than burdening the user with networking details at the Application Layer. These high-level functions include the synchronization of concurrent updates to replicated data objects, and the management of the system-wide name space for shared data

Resource Management in Multi-Access Edge Computing (MEC)

This PhD thesis investigates the effective ways of managing the resources of a Multi-Access Edge Computing Platform (MEC) in 5th Generation Mobile Communication (5G) networks. The main characteristics of MEC include distributed nature, proximity to users, and high availability. Based on these key features, solutions have been proposed for effective resource management. In this research, two aspects of resource management in MEC have been addressed. They are the computational resource and the caching resource which corresponds to the services provided by the MEC. MEC is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to end-user Internet of Things (IoT) and mobile devices. MEC would support latency-critical user applications such as driverless cars and e-health. These applications will depend on resources and services provided by the MEC. However, MEC has limited computational and storage resources compared to the cloud. Therefore, it is important to ensure a reliable MEC network communication during resource provisioning by eradicating the chances of deadlock. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning resources on MEC to achieve a highly reliable and readily available system to support latency-critical applications. In this research, a deadlock avoidance resource provisioning algorithm has been proposed for industrial IoT devices using MEC platforms to ensure higher reliability of network interactions. The proposed scheme incorporates Banker’s resource-request algorithm using Software Defined Networking (SDN) to reduce communication overhead. Simulation and experimental results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms. Additionally, this research explores the use of MEC as a caching platform as it is proclaimed as a key technology for reducing service processing delays in 5G networks. Caching on MEC decreases service latency and improve data content access by allowing direct content delivery through the edge without fetching data from the remote server. Caching on MEC is also deemed as an effective approach that guarantees more reachability due to proximity to endusers. In this regard, a novel hybrid content caching algorithm has been proposed for MEC platforms to increase their caching efficiency. The proposed algorithm is a unification of a modified Belady’s algorithm and a distributed cooperative caching algorithm to improve data access while reducing latency. A polynomial fit algorithm with Lagrange interpolation is employed to predict future request references for Belady’s algorithm. Experimental results show that the proposed algorithm obtains 4% more cache hits due to its selective caching approach when compared with case study algorithms. Results also show that the use of a cooperative algorithm can improve the total cache hits up to 80%. Furthermore, this thesis has also explored another predictive caching scheme to further improve caching efficiency. The motivation was to investigate another predictive caching approach as an improvement to the formal. A Predictive Collaborative Replacement (PCR) caching framework has been proposed as a result which consists of three schemes. Each of the schemes addresses a particular problem. The proactive predictive scheme has been proposed to address the problem of continuous change in cache popularity trends. The collaborative scheme addresses the problem of cache redundancy in the collaborative space. Finally, the replacement scheme is a solution to evict cold cache blocks and increase hit ratio. Simulation experiment has shown that the replacement scheme achieves 3% more cache hits than existing replacement algorithms such as Least Recently Used, Multi Queue and Frequency-based replacement. PCR algorithm has been tested using a real dataset (MovieLens20M dataset) and compared with an existing contemporary predictive algorithm. Results show that PCR performs better with a 25% increase in hit ratio and a 10% CPU utilization overhead

The Kepler DB, a Database Management System for Arrays, Sparse Arrays and Binary Data

The Kepler Science Operations Center stores pixel values on approximately six million pixels collected every 30-minutes, as well as data products that are generated as a result of running the Kepler science processing pipeline. The Kepler Database (Kepler DB) management system was created to act as the repository of this information. After one year of ight usage, Kepler DB is managing 3 TiB of data and is expected to grow to over 10 TiB over the course of the mission. Kepler DB is a non-relational, transactional database where data are represented as one dimensional arrays, sparse arrays or binary large objects. We will discuss Kepler DB's APIs, implementation, usage and deployment at the Kepler Science Operations Center