Server Failures Research Articles

Modeling and Verification of Service Allocation Policies for Multi-Access Edge Computing Using Probabilistic Model Checking

In recent times, Multi-Access Edge Computing (MEC) has emerged as a new paradigm allowing low-latency access to services deployed on edge nodes offering computation, storage and communication facilities. Vendors deploy their services on MEC servers to improve performance and mitigate network latencies often encountered in accessing cloud services. An allocation policy determines how to allocate service requests from users to MEC servers. A number of proposals for binding user service requests to nearby edge servers enroute have been proposed in literature. However, none of these proposals, to the best of our knowledge, provide quantitative guarantees on performance metrics. Indeed, the evolving environment, along with a large allocation configuration space makes proving performance guarantees for such allocation policies a challenging task. Further, the implications of MEC server failures on allocation policies have been relatively unexplored. To address such issues, we propose a trace driven approach to derive a formal model of allocation policies and perform quantitative verification to produce probabilistic guarantees on performance metrics. We use the San Francisco taxi dataset, the LDNS availability dataset and allocation policies from recent literature to validate our approach. Experimental results demonstrate how our model can be utilized to quantitatively compare performance metrics of service allocation policies in MEC systems.

Failure-resilient DAG task scheduling in edge computing

Through placing computation, storage, and communications facilities near the data source, Edge Computing (EC) is anticipated to extend the intelligence from the central cloud to the network edge. The Quality of Experience (QoE) of user and energy efficiency of mobile device could be significantly improved through offloading their computation-intensive tasks to the network edge. With the increasing popularity of intelligent devices, tasks offloaded to the edge are becoming more complex, consisting of multiple sub-tasks with data dependency, which are typically modeled as a Directed Acyclic Graph (DAG). The scheduling of DAG tasks is more complex, which has been proved to be NP-hard. Traditional DAG scheduling algorithms developed in non-edge computing scenarios could not be directly applied due to their neglect of: (1) the competition of communication resources; and (2) the rescheduling requirement in case of edge server failure in dynamic edge network environment. In this backdrop, this paper presents a failure-resilient DAG task scheduling algorithm to minimize the response delay experienced by the tasks. After formulating the DAG task scheduling problem, a context-aware greedy task scheduling (CaGTS) algorithm is proposed. Then, to cope with the failure event of edge server, a dependency-aware task rescheduling (DaTR) algorithm is designed. To evaluate the performance of the proposed algorithms, extensive experiments have been conducted on a simulator developed using Python. Experimental results with diverse parameter settings have shown that CaGTS could reduce at least 10.47% average completion time than benchmarks, and DaTR can effectively avoid task scheduling interruption caused by server failure events.

Ergodicity and perturbation bounds forMt/Mt/1 queue with balking, catastrophes, server failures and repairs

In this paper, we display methods for the computation of convergence and perturbation bounds forMt/Mt/1 system with balking, catastrophes, server failures and repairs. Based on the logarithmic norm of linear operators, the bounds on the rate of convergence, perturbation bounds, and the main limiting characteristics of the queue-length process are obtained. Finally, we consider the application of all obtained estimates to a specific model.

Multi-Machine Repairable System with One Unreliable Server and Variable Repair Rate

This paper analyzed the multi-machine repairable system with one unreliable server and one repairman. The machines may break at any time. One server oversees servicing the machine breakdown. The server may fail at any time with different failure rates in idle time and busy time. One repairman is responsible for repairing the server failure; the repair rate is variable to adapt to whether the machines are all functioning normally or not. All the time distributions are exponential. Using the quasi-birth-death(QBD) process theory, the steady-state availability of the machines, the steady-state availability of the server, and other steady-state indices of the system are given. The transient-state indices of the system, including the reliability of the machines and the reliability of the server, are obtained by solving the transient-state probabilistic differential equations. The Laplace–Stieltjes transform method is used to ascertain the mean time to the first breakdown of the system and the mean time to the first failure of the server. The case analysis and numerical illustration are presented to visualize the effects of the system parameters on various performance indices.

Demand-Aware Erasure Coding for Distributed Storage Systems

Distributed storage systems provide cloud storage services by storing data on commodity storage servers. Conventionally, data are protected against failures of such commodity servers by replication. Erasure coding consumes less storage overhead than replication to tolerate the same number of failures and thus has been replacing replication in many distributed storage systems. However, with erasure coding, the overhead of reconstructing data from failures also increases significantly. Under the ever-changing workload where data accesses can be highly skewed, it is challenging to deploy erasure coding with appropriate values of parameters to achieve a well trade-off between storage overhead and reconstruction overhead. In this paper, we propose Zebra, a framework that encodes data by their demand into multiple tiers that deploy erasure codes with different values of parameters. Zebra automatically determines the number of such tiers and dynamically assigns erasure codes with optimal values of parameters into corresponding tiers. With Zebra, a flexible trade-off between storage overhead and reconstruction overhead is achieved with multiple tiers. When demand changes, Zebra adjusts itself with a marginal amount of network transfer. We demonstrate that Zebra can work with two representative families of erasure codes in distributed storage systems, Reed-Solomon codes and local reconstruction codes.

WITHDRAWN: Web log analysis using Spark with solution recommendation

The robustness required by the web server that operates behind those giant service providers must be taken into account in today's world, where any service is accessible on the internet in the form of large sites or exceptional web applications. The businesses spend a great deal of money on maintaining these servers so that they work always at their highest level. The amount of data that is transacted and the intricacies involved in the Internet every microsecond is beyond man's imagination. The server needs to run 24X7 to make it easier for their demanding clients. Incidents, however, have contributed to millions of dollars losses for the organization due to a malfunction of a company server and it merely emphasizes the value of a stable server. Each server contains details about the requests that it has previously handled, called web server logs, and it is extremely valuable because it gives us information about the potential reasons for the server failure. For a certain period, famine attacks (DDOS attacks) and many others may be overloaded. The web server's audit logs include details about the response code it generates and should the error codes involved be well examined, the reasons for a server failure might probably be identified. We aim to review the error code documents to gain their information and evaluate web server logs using a solution-recommendation framework that informs the server administrator of the potential reasons for any previous server breakdowns and how it can be prevented. For analysis, we will be using Apache Spark keeping in mind its in-memory processing of data, making it a lot faster than Map Reduce of the Hadoop ecosystem which goes for batch processing.

Robust Task Offloading in Dynamic Edge Computing

Multi-access edge computing achieves better application responsiveness by offloading tasks from end devices to edge servers installed at the vicinity. Practical scenarios, such as post-disaster rescuing and battlefield monitoring, make it attractive to use end devices themselves as edge servers. This, however, introduces a new challenge: Due to mobility and power limitation, the set of edge servers becomes dynamic. As some servers fail, the tasks that run on them will also fail. This paper introduces a new dynamic edge computing model and conducts the first study on robust task offloading which is tolerant to <inline-formula><tex-math notation="LaTeX">$h$</tex-math></inline-formula> server failures. We propose online primal-dual algorithms that offload tasks as they arrive. We evaluate the performance of our robust task offloading solutions through extensive simulations based on real task sets. The results show that our proposed solutions can well handle edge dynamics and achieve near optimal throughput (above 95 percent) compared to the optimal offline benchmark algorithm.

Privacy-Preserving Mutual Authentication and Key Agreement Scheme for Multi-Server Healthcare System

The usage of different technologies and smart devices helps people to get medical services remotely for multiple benefits. Thus, critical and sensitive data is exchanged between a user and a doctor. When health data is transmitted over a common channel, it becomes essential to preserve various privacy and security properties in the system. Further, the number of users for remote services is increasing day-by-day exponentially, and thus, it is not adequate to deal with all users using the one server due to the verification overhead, server failure, and scalability issues. Thus, researchers proposed various authentication protocols for multi-server architecture, but most of them are vulnerable to different security attacks and require high computational resources during the implementation. To Tackle privacy and security issues using less computational resources, we propose a privacy-preserving mutual authentication and key agreement protocol for a multi-server healthcare system. We discuss the proposed scheme’s security analysis and performance results to understand its security strengths and the computational resource requirement, respectively. Further, we do the comparison of security and performance results with recent relevant authentication protocols.

Reliability-Aware Offloading and Allocation in Multilevel Edge Computing System

Mobile edge computing system provides cloud computing capabilities at the edge of wireless mobile networks, ensuring low latency, highly efficient computing, and improved user experience. At the same time, computationally intensive components are offloaded from mobile devices to edge servers and distributed among the servers. Due to the special constraints (mobile devices' battery capacities, limited computing resources of one single edge server, inevitable edge server failure, etc.), there emerges a following problem. 1) How to guarantee the reliability of the offloaded computing? This problem brings in the following two other problems. 2) How to find the appropriate offloading point in the mobile program such that the computing tasks offloaded to cloud can be maximized, while the transmission energy consumption is minimized? 3) What is the achievable minimum latency tasks allocation strategy among multiple users' mobile devices and multiple edge servers? In this paper, we try to address the aforementioned problems. First, for the appropriate offloading point problem, we consider the offloading valuable basic constraint and propose a task merging strategy based on mobile program component call graphs to minimize the computational complexity of the program partition. Second, we formulate the second problem as a combinatorial optimization problem and transform it into an n-fold integer programming problem by mapping the remaining computing resources to a virtual component. Third, we design a reliable shadow component scheme between multilevel severs for the reliability problem. Finally, we develop a fast algorithm for the mix problem and analyze its performance and conduct experiments to prove the accuracy of our theoretical results.

An improved security and privacy management system for data in multi-cloud environments using a hybrid approach

Storing and accessing the information in the multi-cloud hosting environment becomes popular these days. It offers benefits like the assurance of data protection, preventing information corruption, unethical issues from vendors. In this paper, a hybrid approach with the multi-cloud hosting environment is designed and implemented for improved security and privacy of cloud data. The hybrid method consists of three modules (a) Byzantine protocol to tolerant security breaches to server failures cloud, which is independent. (b) DepSky architecture enhances the reliability and secrecy of data preserved in the cloud using encoding and decoding techniques (c) Shamir secret sharing procedure to improve trustiness & privacy of data storage without affecting the performance. The privacy and security issues of the hybrid approach are implemented and compared with the protocols like SAML with proxy re-encryption and Kerberos for different user service requests. The performance of the hybrid approach in terms of memory utilization, encryption/decryption time, total authentication time is improved compared to that of protocol environments SAML, SAML with proxy re-encryption and Kerberos. The results were encouraging in the Hybrid Approach in terms of encryption time/decryption time, Memory utilization and average precision values.

Heuristic Approach to Distributed Server Allocation with Preventive Start-Time Optimization against Server Failure

This paper presents a distributed server allocation model with preventive start-time optimization against a single server failure. The presented model preventively determines the assignment of servers to users under each failure pattern to minimize the largest maximum delay among all failure patterns. We formulate the proposed model as an integer linear programming (ILP) problem. We prove the NP-completeness of the considered problem. As the number of users and that of servers increase, the size of ILP problem increases; the computation time to solve the ILP problem becomes excessively large. We develop a heuristic approach that applies simulated annealing and the ILP approach in a hybrid manner to obtain the solution. Numerical results reveal that the developed heuristic approach reduces the computation time by 26% compared to the ILP approach while increasing the largest maximum delay by just 3.4% in average. It reduces the largest maximum delay compared with the start-time optimization model; it avoids the instability caused by the unnecessary disconnection permitted by the run-time optimization model.

Load balancing in cloud environs: Optimal task scheduling via hybrid algorithm

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well.

Using Nagios on a Raspberry Pi to Monitor Anetwork with Emphasis on Security

The objective of this paper is to present an advanceduse of Nagios on Raspberry Pi to monitor a network. RaspberryPi is a tiny, low-cost yet powerful computer board with manyapplications. Network monitoring systems constantly monitor acomputer network for malfunctions and failures of servers andnotifies the network administrator in case of trouble. In ourcase, the free version of Nagios installed on a Raspberry Pi hasbeen used to monitor a network at minimal cost. Furthermore,two bash scripts automating the complex process of Nagiosinstallation on Raspberry Pi, as well as Linux host have beendeveloped. In order to elevate the security level of Networkmonitoring we have proposed a triple set of Raspberry Pi Nagiosservers, each one monitoring hosts and servers including theother two Nagios servers (triple modular redundancy). Finally,custom scripts providing useful information about monitoredhosts, such as their operating system, hardware and networking,as well as a special script examining and rating the security levelof Apache web servers, have been developed and incorporatedinto the network monitoring process.

Clairvoyant state machine replication

We consider the state machine replication problem in semi-synchronous systems subject to Byzantine server failures. We propose a new solution to the problem in the form of a protocol for the generalized consensus problem. The protocol solves the consensus problem in a setting in which information about conflicts between transactions is available (such information can be in the form of transaction read and write sets). Unlike most prior proposals (for generalized or classical consensus), which use a leader to order transactions, this protocol is leaderless, and relies on non-skipping timestamps for transaction ordering. Being leaderless, the protocol does not need to pause for leader elections. The use of non-skipping timestamps permits servers to commit transactions as soon as they know that no conflicting transaction can be ordered earlier. For n servers of which f may be faulty, this protocol requires n>4f.

Performability Analysis of a Redundant Parallel Task in Network Systems

Recently, application of network systems ( e.g. , cloud computing systems) is increasingly prevalent for achieving integration, sharing and efficient utilization of various resources. A parallel task in a network system has multiple subtasks that can be executed in different servers in parallel. However, failures of any subtask inevitably result in that the entire task cannot be complete. To avoid such a situation, the network system can create some copies from a subtask and make them run on different servers simultaneously. This redundant parallel execution manner is an efficient approach to improve performance and guarantee reliability. However, it also brings complexity in modeling, evaluation and optimization. For example, link failures inevitably lead to inaccessibility of some servers, and server failures also result in that subtasks hosted on the server cannot be complete. This is the complicated failure correlation that cannot be ignored in modeling and evaluation. This paper first presents a reliability-performance correlation model for a redundant parallel task in the network system. The model captures precedence constraints of subtasks, multiple types of failures and complicated failure correlations to improve fidelity. This paper also design an algorithm that encompasses the Graph theory and the Bayesian theorem to evaluate a performability metric, which can be used to quantify important reliability-performance correlation. Finally, a heuristic algorithm is designed to search an optimal task execution strategy that maximizes the performability metric. Illustrative examples are presented.

Reliability analysis of (&lt;i&gt;n&lt;/i&gt;) clients system under star topology and copula linguistic approach

This paper contrasts with the study of a computer laboratory system with n clients under star topology and k-out-of-n: G scheme. The system has two servers, one is operational and another a redundant server. The main server connects the clients, and failure of the main server acknowledge as a partial failure. The system also has another needed device such as switch and router, and failure in switch and router brings the system in the entire failed situation. The main server bears a partial failure, and the redundant server before the repair of the main server brings the system in a completely failed state. The failure rates of transmuting one state to another state are constant and presume to follow an exponential time distribution. The repairs follow two types of distributions the system analysed by employing the supplementary variable and the Laplace transform. The various traditional measures of system performance through reliability context have computed for different types of failures and repair. Some computations reliability parameters corresponding to assumed values of parameters has illustrated as examples.

Reliability analysis of (&lt;i&gt;n&lt;/i&gt;) clients system under star topology and copula linguistic approach

This paper contrasts with the study of a computer laboratory system with n clients under star topology and k-out-of-n: G scheme. The system has two servers, one is operational and another a redundant server. The main server connects the clients, and failure of the main server acknowledge as a partial failure. The system also has another needed device such as switch and router, and failure in switch and router brings the system in the entire failed situation. The main server bears a partial failure, and the redundant server before the repair of the main server brings the system in a completely failed state. The failure rates of transmuting one state to another state are constant and presume to follow an exponential time distribution. The repairs follow two types of distributions the system analysed by employing the supplementary variable and the Laplace transform. The various traditional measures of system performance through reliability context have computed for different types of failures and repair. Some computations reliability parameters corresponding to assumed values of parameters has illustrated as examples.

Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services

Edge computing services are exposed to infrastructural failures due to geographical dispersion, ad hoc deployment, and rudimentary support systems. Two unique characteristics of the edge computing paradigm necessitate a novel failure resilience approach. First, edge servers, contrary to cloud counterparts with reliable data center networks, are typically connected via ad hoc networks. Thus, link failures need more attention to ensure truly resilient services. Second, network delay is a critical factor for the deployment of edge computing services. This restricts replication decisions to geographical proximity and necessitates joint consideration of delay and resilience. In this article, we propose a novel machine learning based mechanism that evaluates the failure resilience of a service deployed redundantly on the edge infrastructure. Our approach learns the spatiotemporal dependencies between edge server failures and combines them with the topological information to incorporate link failures. Ultimately, we infer the probability that a certain set of servers fails or disconnects concurrently during service runtime. Furthermore, we introduce Dependency- and Topology-aware Failure Resilience (DTFR), a two-stage scheduler that minimizes either failure probability or redundancy cost, while maintaining low network delay. Extensive evaluation with various real-world failure traces and workload configurations demonstrate superior performance in terms of availability, number of failures, network delay, and cost with respect to the state-of-the-art schedulers.

A Quadtree-based synchronization protocol for inter-server game state synchronization

Online games are a fundamental part of the entertainment industry but the current IP infrastructure does not satisfactorily fulfill the needs of these services. The novel networking architecture Named Data Networking (NDN) inherently supports network-level multicast and packet-level security and thereby introduces promising features for online games. In this paper, we propose an NDN-based approach to synchronize game state in a server cluster, a task necessary to allow multiple players in large numbers to play in the same game world. The proposed Quadtree Synchronization Protocol applies NDN’s data-centric nature to decouple the game world from the game servers hosting it. This means that requesting changes of a specific game world region becomes possible without knowing which game server is responsible for the requested region. We use a hierarchic game world structure when requesting data that allows the network to forward requests to the responsible game server without directly addressing it. This region-based naming scheme decouples world regions from servers which eases the management of the game server cluster and allows easier recovery after server failures. In addition, this decoupling allows exchanging information about a geographical region, such as a game world, without knowledge of the other participants changing the world. Such a region-based synchronization mode is not possible to implement with existing protocols. However, it allows building distributed systems that do not require a central server to work. Besides architectural benefits, network emulations show that our protocol increases the efficiency of data transport by utilizing network-level multicast. Our proposed approach can keep up with current protocols which can be used for inter-server game state synchronization.

On retrial queue with customer balking and feedback subject to server breakdowns

Motivated by some real stochastic service systems, we study a feedback retrial queue with customer balking and unreliable servers (servers subject to breakdowns). A customer will either join a retrial orbit or balk if all servers are busy upon his arrival. After a service completion, the customer either leave the system or enter the retrial group again for additional service. The customer who is interrupted by the server failure will be served by another server if available; otherwise, he will be contacted later and treated as leaving the system. We use a quasi-birth-and-death process to analyze this system with almost all possible uncertain factors and derive major performance measures of the system. We also develop a cost function determining the optimal parameter settings of the system under the stability condition. Probabilistic Global Search Lausanne approach is employed in solving the optimization issue. The effects of parameters on performance measures of the system are examined numerically. Finally, we discuss the application of the model in the telephone medical consulting service system.