Failure-prone Environment Research Articles

Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A Survey

The advent of the edge computing network paradigm places the computational and storage resources away from the data centers and closer to the edge of the network largely comprising the heterogeneous IoT devices collecting huge volumes of data. This paradigm has led to considerable improvement in network latency and bandwidth usage over the traditional cloud-centric paradigm. However, the next generation networks continue to be stymied by their inability to achieve adaptive, energy-efficient, timely data transfer in a dynamic and failure-prone environment—the very optimization challenges that are dealt with by biological networks as a consequence of millions of years of evolution. The transcriptional regulatory network (TRN) is a biological network whose innate topological robustness is a function of its underlying graph topology. In this article, we survey these properties of TRN and the metrics derived therefrom that lend themselves to the design of smart networking protocols and architectures. We then review a body of literature on bio-inspired networking solutions that leverage the stated properties of TRN. Finally, we present a vision for specific aspects of TRNs that may inspire future research directions in the fields of large-scale social and communication networks.

Dynamic Load Rebalancing Algorithm for Private Cloud

Infrastructure-As-A-Service (IAAS) provides an environmental setup under any type of cloud. In Distributed file system (DFS), nodes are simultaneously serve computing and storage functions; that is parallel Data Processing and storage in cloud. Here, file is considered as a data or load. That file is partitioned into a number of File chunks (FC) allocated in distinct nodes so that Map Reduce tasks can be performed in parallel over the nodes. Files and Nodes can be dynamically created, deleted, and added. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed as uniformly as possible among the Chunk Servers (CS). Emerging distributed file systems in production systems strongly depend on a central node for chunk reallocation or Distributed node to maintain global knowledge of all chunks. This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size, it may thus become the performance bottleneck and the single point of failure and memory wastage in distributed nodes. So, we have to enhance the Client side module with server side module to create, delete and update the file chunks in Client Module. And manage the overall private cloud and apply dynamic load balancing algorithm to perform auto scaling options in private cloud. In this project, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem.

Performance evaluation of snapshot isolation in distributed database system under failure-prone environment

Database systems are widely used in many fields. As the scale of database systems increases, more firms decide to deploy the database system in a distributed environment for the sake of data integrity fault tolerance. For guaranteeing the consistency of data, database management systems adopt isolation policies. This paper discusses probabilistic models for snapshot isolation of database management system. Snapshot isolation is an effective method to enhance the consistency of database system, although it degrades the system performance where the system has large network latency such as distributed database system. Also, under the failure-prone environment, a restart scheme is considered as one countermeasure. This paper proposes probabilistic models for the dynamics of snapshot isolation of database system and exhibits the optimization of system performance with respect to updating interval of snapshot isolation within the failure-prone environment from the analytical point of view. Numerical experiments are conducted to validate the effectiveness of analytical results by using real traffic data.

Load Rebalancing for Distributed File Systems in Clouds

Distributed file systems are key building blocks for cloud computing applications based on the MapReduce programming paradigm. In such file systems, nodes simultaneously serve computing and storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that MapReduce tasks can be performed in parallel over the nodes. However, in a cloud computing environment, failure is the norm, and nodes may be upgraded, replaced, and added in the system. Files can also be dynamically created, deleted, and appended. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed as uniformly as possible among the nodes. Emerging distributed file systems in production systems strongly depend on a central node for chunk reallocation. This dependence is clearly inadequate in a large-scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size, and may thus become the performance bottleneck and the single point of failure. In this paper, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem. Our algorithm is compared against a centralized approach in a production system and a competing distributed solution presented in the literature. The simulation results indicate that our proposal is comparable with the existing centralized approach and considerably outperforms the prior distributed algorithm in terms of load imbalance factor, movement cost, and algorithmic overhead. The performance of our proposal implemented in the Hadoop distributed file system is further investigated in a cluster environment.

Simple Load Rebalancing For Distributed Hash Tables In Cloud

Distributed file systems are key building blocks for cloud computing applications based on the Map Reduce programming paradigm. In such file systems, nodes simultaneously serve computing and storage functions; a file is partitioned into a number of chunks allocated in distinct nodes so that Map Reduce tasks can be performed in parallel over the nodes. However, in a cloud computing environment, failure is the norm, and nodes may be upgraded, replaced, and added in the system. This dependence is clearly inadequate in a large- scale, failure-prone environment because the central load balancer is put under considerable workload that is linearly scaled with the system size, and may thus become the performance bottleneck and the single point of failure. In this paper, a fully distributed load rebalancing algorithm is presented to cope with the load imbalance problem. Our algorithm is compared against a centralized approach in a production system and a competing distributed solution presented in the literature. The simulation results indicate that our proposal is comparable with the existing centralized approach and considerably outperforms the prior distributed algorithm in terms of

Reliable Web service selection in choreographed environments

Previous research into Web service selection mainly uses an orchestration model, in which a single entity is responsible for the selection and invocation of various Web services. However in many application scenarios, each Web service independently performs a selection, even though achieving the business's goals demands collective, pairwise interactions across the total set of Web services. This study instead considers the Web service selection problem in a choreographed environment, in which each Web service maintains its confidentiality about its collaborators and exchanges only limited amounts of information with its partners. The goal is to maximize the likelihood of completing the entire choreography in a failure-prone environment. Several experiments show that the proposed method performs similarly to a centralized method and better than three distributed Web service selection methods that involve various degrees of information about other services.

Distributed Fault-Tolerant Quality of Wireless Networks

A mobile ad hoc network (MANET) consists of a group of communicating hosts that form an arbitrary network topology by means of any of several wireless communication media. MANET communications represent a diversification in communication technology necessary to solve the stringent end-to-end requirements of QoS-based communication networks. Of the many challenges in this complex distributed system, the problem of routing based on a predefined set of customer preferences, critical to guaranteeing quality-of-service, is the focus of this research. Specifically, this paper modifies a cluster-based QoS routing algorithm for mobile ad hoc networks with the aim of providing fault tolerance, which is a critical feature in providing QoS in the link failure-prone environment of mobile networks. Performance of this new fault-tolerant cluster-based QoS wireless algorithm is evaluated according to failure recovery time, dropped packets, throughput, and sustained flow bandwidth via simulations involving node failure scenarios along QoS paths.

Dynamic Web Service Selection for Reliable Web Service Composition

This paper studies the dynamic web service selection problem in a failure-prone environment, which aims to determine a subset of Web services to be invoked at run-time so as to successfully orchestrate a composite web service. We observe that both the composite and constituent web services often constrain the sequences of invoking their operations and therefore propose to use finite state machine to model the permitted invocation sequences of Web service operations. We assign each state of execution an aggregated reliability to measure the probability that the given state will lead to successful execution in the context where each web service may fail with some probability. We show that the computation of aggregated reliabilities is equivalent to eigenvector computation and adopt the power method to efficiently derive aggregated reliabilities. In orchestrating a composite Web service, we propose two strategies to select Web services that are likely to successfully complete the execution of a given sequence of operations. A prototype that implements the proposed approach using BPEL for specifying the invocation order of a web service is developed and served as a testbed for comparing our proposed strategies and other baseline Web service selection strategies.

Mace

Building distributed systems is particularly difficult because of the asynchronous, heterogeneous, and failure-prone environment where these systemsmust run. Tools for building distributed systems must strike a compromise between reducing programmer effort and increasing system efficiency. We present Mace , a C++ language extension and source-to-source compiler that translates a concise but expressive distributed system specification into a C++ implementation. Mace overcomes the limitations of low-level languages by providing a unified framework for networking and event handling, and the limitations of high-level languages by allowing programmers to write program components in a controlled and structured manner in C++. By imposing structure and restrictions on how applications can be written, Mace supports debugging at a higher level, including support for efficient model checking and causal-path debugging. Because Mace programs compile to C++, programmers can use existing C++ tools, including optimizers, profilers, and debuggers to analyze their systems.

Randomization helps to perform independent tasks reliably

AbstractThis paper is about algorithms that schedule tasks to be performed in a distributed failure‐prone environment, when processors communicate by message‐passing, and when tasks are independent and of unit length. The processors work under synchrony and may fail by crashing. Failure patterns are imposed by adversaries. Linearly‐bounded adversaries may fail up to a constant fraction of the processors. Weakly‐adaptive adversaries have to select, prior to the start of an execution, a subset of processors to be failure‐prone, and then may fail only the selected processors, at arbitrary steps, in the course of the execution. Strongly adaptive adversaries have a total number of failures as the only restriction on failure patterns. The measures of complexity are work, measured as the available processor steps, and communication, measured as the number of point‐to‐point messages. A randomized algorithm is developed, that attains both 𝒪(n log*n) expected work and 𝒪(n log*n) expected communication, against weakly‐adaptive linearly‐bounded adversaries, in the case when the numbers of tasks and processors are both equal to n. This is in contrast with performance of algorithms against strongly‐adaptive linearly‐bounded adversaries, which has to be Ω(n log n/log log n) in terms of work. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 2004

A SAFE AND SCALABLE PAYMENT INFRASTRUCTURE FOR TRADE OF ELECTRONIC CONTENT

BARTER (a Backbone ARchitecture for Trade of ElectRonic content) is a payment infrastructure that facilitates digital content trade over an open network. BARTER is designed to operate over a large-scale, global and heterogeneous communication network. The BARTER protocols address two vital requirements, neglected from existing electronic commerce systems: scalability and transactional efficiency. These protocols possess strong properties such as delivery atomicity, agreement validation and the ability to resolve several classes of disputes. BARTER's novelty is twofold: First, BARTER servers are not required to perform expensive cryptographic operations such as commitment verification; commitments are cross-verified by the parties themselves, thus reducing the overhead of online transaction processing by orders of magnitude. Consequently, BARTER can serve as an efficient online/offline clearing infrastructure. Second, BARTER integrates scalability considerations into several system components (the authentication subsystem, the account management subsystem, and the maintenance of global data) that are likely to suffer service degradation in a world-wide setting. In addressing these issues, BARTER takes into account the inherent asynchronous, unreliable, insecure and failure-prone environment assumptions. We contend that by employing service distribution, BARTER is expected to scale well, meeting the demands of a world-wide setting, over which it is intended to operate.

Unifying concurrency control and recovery of transactions with semantically rich operations

The classical theory of transaction management contains two different aspects, namely concurrency control and recovery, which ensure serializability and atomicity of transaction executions, respectively. Although concurrency control and recovery are not independent of each other, the criteria for these two aspects were developed orthogonally and as a result, in most cases these criteria are incompatible with each other. Recently a unified theory of concurrency control and recovery for databases with read and write operations has been introduced in [19, 1] that allows reasoning about serializability and atomicity within the same framework. In [19, 1] a class of schedules (called prefix reducible), which guarantees both serializability and atomicity in a failure prone environment with read/write operations was introduced. Several protocols were developed to generate such schedules by a database concurrency control mechanism. We present here a unified transaction model for databases with an arbitrary set of semantically rich operations. We investigate constructive characterization of the class of prefix reducible schedules with semantically rich operations. It turns out that unlike databases with only read/write operations, the exact characterization of prefix reducible schedules in databases with arbitrary operations is rather infeasible. Thus, we propose here several sufficiently rich subclasses of prefix reducible schedules, and design concurrency control protocols that guarantee both serializability and atomicity for schedules from these classes.

Queueing analysis of fault-tolerant computer systems (extended abstract)

Queueing models provide a useful tool for predicting the performance of many service systems including computer systems, telecommunication systems, computer/communication networks and flexible manufacturing systems. Traditional queueing models predict system performance under the assumption that all service facilities provide failure-free service. It must, however, be acknowledged that service facilities do experience failures and that they get repaired. In recent years, it has been increasingly recognized that this separation of performance and reliability/availability models is no longer adequate. An exact steady-state queueing analysis of such systems is considered by several authors and is carried out by means of generating functions, supplementary variables, imbedded Markov process and renewal theory, or probabilistic techniques [1,2,7,8]. Another approach is approximate, in which it is assumed that the time to reach the steady-state is much smaller than the times to failures/repairs. Therefore, it is reasonable to associate a performance measure (reward) with each state of the underlying Markov (or semi-Markov) model describing the failure/repair behavior of the system. Each of these performance measures is obtained from the steady-state queueing analysis of the system in the corresponding state [3,5]. Earlier we have developed models to derive the distribution of job completion time in a failure-prone environment [3,4]. In these models, we need to consider a possible loss of work due to the occurrence of a failure, i.e., the interrupted job may be resumed or restarted upon service resumption. Note that the job completion time analysis includes the delays due to failures and repairs. The purpose of this paper [9] is to extend our earlier analysis so as to account for the queueing delays. In effect, we consider an exact queueing analysis of fault-tolerant systems in order to obtain the steady-state distribution and the mean of the number of jobs in the system. In particular, we study a system in which jobs arrive in a Poisson fashion and are serviced according to FCFS discipline. The service requirements of the incoming jobs form a sequence of independent and identically distributed random variables. The failure/repair behaviour of the system is modelled by an irreducible continuous-time Markov chain, which is independent of the number of jobs in the system. Let the state-space be {1,2, …, n }. When the computer system is in state i it delivers service at rate r i ≥ 0. Furthermore, depending on the type of the state, the work done on the job is preserved or lost upon entering that state. The actual time required to complete a job depends in a complex way upon the service requirement of the job and the evolution of the state of the system. Note that even though the service requirements of jobs are independent and identically distributed, the actual times required to complete these jobs are neither independent nor identically distributed, and hence the model cannot be reduced to a standard M/G/ 1 queue [8]. As loss of work due to failures and interruptions is quite a common phenomenon in fault-tolerant computer systems, the model proposed here is of obvious interest. Using our earlier results on the distribution of job completion time we set up a queueing model and show that it has the block M/G/ 1 structure. Queueing models with such a structure have been studied by Neuts, Lucantoni and others [6]. We demonstrate the usefulness of our approach by performing the numerical analysis for a system with two processors subject to failures and repairs.