Replication and Resubmission Based Adaptive Decision for Fault Tolerance in Real Time Cloud Computing

Author(s):  
Prasenjit Kumar Patra ◽  
Harshpreet Singh ◽  
Rajwinder Singh ◽  
Saptarshi Das ◽  
Nilanjan Dey ◽  
...  

Cloud computing an adoptable technology is the upshot evolution of on demand service in the computing epitome of immense scale distributed computing. With the raising asks and welfares of cloud computing infrastructure, society can take leverage of intensive computing capability services and scalable, virtualized vicinity of cloud computing to carry out real time tasks executed on a remote cloud computing node. Due to the indeterminate latency and minimal control over computing node, sway the reliability factor. Therefore, there is a raise of requisite for fault tolerance to achieve reliability in the real time cloud infrastructure. In this paper, a model which provides fault tolerance named “Replication and resubmission based adaptive decision for fault tolerance in real-time cloud computing (RRADFTRC)” for real time cloud computing is projected with result. In the projected model, the system endure the faults and makes the adaptive decision on the basis of proper resource allocation of tasks with a new style of approach in real time cloud vicinity.

Since cloud computing has numerous benefits and demands, it is much efficient performing real time computing in cloud infrastructure. This real time system takes benefits of scalable virtualized environment and intensive cloud computing capabilities for executing real time tasks. Most of the real time applications undergone its processing stage on remote cloud computing nodes. Here chances of error occurrence is also higher due to undecided latency. And it is necessary that the safety critical applications should have higher reliability. To determine whether the fault tolerance mechanism is having the higher reliability and availability or not. This paper proposes an efficient adaptive fault tolerance mechanism through reliability assessment architecture to enhance the reliability of the system. The decision mechanism is also proposed in this scheme to improve the efficiency a bit higher than normal scheme. At last the fine grained check point algorithm is utilized for reducing the latency. Thus from the analysis of the data it is proved that the execution time of proposed method hikes when compared to other conventional schemes.


The basic fault tolerance issues seen in cloud computing are identification and recovery. To fight with these issues, so many fault tolerance methods have been designed to decrease the faults. However, due to the reliability and web based service giving behavior, fault tolerance in cloud computing will be a huge challenge. The present model is not just on tolerating faults but also to decrease the possibility of future faults as well[4].The fault tolerance deals with the exact and constant operation of the fault segments. The processing on computing nodes can be done remotely in the real time cloud applications, so there could be more possibilities of errors. Hence there lies an immense necessity for fault tolerance to attain consistency to the real time computing on cloud infrastructure. The “fault tolerance” can be explained through fault processing that have two basic stages. The stages are (i) The effective error processing stage which is used to intended for carrying the “effective error” back to inactive state, i.e., before the error occurred (ii) The latent error processing stage intended for guaranteeing that the fault does not get effective once again.


The computing resource availability in a cloud computing environment is considered as the vital attribute among the security essentialities due to the consequence of on its on demand service. The class of adversaries related to the Distributed Denial of Service (DDoS) attack is prevalent in the cloud infrastructure for exploiting the vulnerabilities during the implementation of their attack that still make the process of providing security and availability at the same time as a challenging objective. In specific, The in cloud computing is the major threat during the process of balancing security and availability at the same time. In this paper, A Reliable Friedman Hypothesis-based Detection and Adaptive Load Balancing Scheme (RFALBS-RoQ-DDOS) is contributed for effective detection of RoQDDoS attacks through Friedman hypothesis testing. It also inherited an adaptive load balancing approach that prevents the degree of imbalance in the cloud environment. The simulation results of the proposed RFALBS-RoQ-DDoS technique confirmed a superior detection rate and a adaptive load balancing rate of nearly 23% and 28% predominant to the baseline DDoS mitigation schemes considered for investigation.


2021 ◽  
Author(s):  
◽  
Kyle Chard

<p>The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains.</p>


Author(s):  
Suvendu Chandan Nayak ◽  
Sasmita Parida ◽  
Chitaranjan Tripathy ◽  
Prasant Kumar Pattnaik

The basic concept of cloud computing is based on “Pay per Use”. The user can use the remote resources on demand for computing on payment basis. The on-demand resources of the user are provided according to a Service Level Agreement (SLA). In real time, the tasks are associated with a time constraint for which they are called deadline based tasks. The huge number of deadline based task coming to a cloud datacenter should be scheduled. The scheduling of this task with an efficient algorithm provides better resource utilization without violating SLA. In this chapter, we discussed the backfilling algorithm and its different types. Moreover, the backfilling algorithm was proposed for scheduling tasks in parallel. Whenever the application environment is changed the performance of the backfilling algorithm is changed. The chapter aims implementation of different types of backfilling algorithms. Finally, the reader can be able to get some idea about the different backfilling scheduling algorithms that are used for scheduling deadline based task in cloud computing environment at the end.


2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tianpeng Ye ◽  
Zhou Su ◽  
Jun Wu ◽  
Longhua Guo ◽  
Jianhua Li ◽  
...  

The Intelligent Transportation System (ITS) becomes an important component of the smart city toward safer roads, better traffic control, and on-demand service by utilizing and processing the information collected from sensors of vehicles and road side infrastructure. In ITS, Vehicular Cloud Computing (VCC) is a novel technology balancing the requirement of complex services and the limited capability of on-board computers. However, the behaviors of the vehicles in VCC are dynamic, random, and complex. Thus, one of the key safety issues is the frequent disconnections between the vehicle and the Vehicular Cloud (VC) when this vehicle is computing for a service. More important, the connection fault will disturb seriously the normal services of VCC and impact the safety works of the transportation. In this paper, a safety resource allocation mechanism is proposed against connection fault in VCC by using a modified workflow with prediction capability. We firstly propose the probability model for the vehicle movement which satisfies the high dynamics and real-time requirements of VCC. And then we propose a Prediction-based Reliability Maximization Algorithm (PRMA) to realize the safety resource allocation for VCC. The evaluation shows that our mechanism can improve the reliability and guarantee the real-time performance of the VCC.


Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5706
Author(s):  
Muhammad Shuaib Qureshi ◽  
Muhammad Bilal Qureshi ◽  
Muhammad Fayaz ◽  
Muhammad Zakarya ◽  
Sheraz Aslam ◽  
...  

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.


Sign in / Sign up

Export Citation Format

Share Document