An Analysis Of Cloud ReliabilityApproaches Based on Cloud Components And Reliability Techniques

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Volume 1, Issue 6, June 2014. ISSN 2348 - 4853

An Analysis Of Cloud ReliabilityApproaches Based on Cloud

Components And Reliability Techniques

Abishi Chowdhury1_,_{Priyanka Tripathi}2

National Institute of Technical Teachers’ Training and Research, Bhopal, India1,2

[email protected]_,_{[email protected]}2

A B S T R A C T

Cloud computing is powering the overall business and organizational growth by providing the threebasic services like Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). As the worldwide cloud users demand multiple services from cloud at a time, so it is the most important concern for cloud service providers to concentrate on the reliability of the system. The reliability of any system can be determined by the number of failures occurred in the cloud computing environment vs. the total number of tasks done by the cloud. The reliability of a system depends on the reliability of each and every component of the system with which the system is composed of. In this paper, an attempt has been made to analyze different cloud reliability techniques, different components for reliability measurement and the methodology for measuring reliability. Further, based on these parameters we have prepared a comparison table to compare these techniques.

Index Terms: Virtual Machine; Reliability; Cloud Manager; Fault Tolerance; Fault manager; Fault Tolerance middleware

I. INTRODUCTION

Cloud computing provides on demand services to its users and the users can demand any kind of services in the form of Software, Platform, Infrastructure and so on at anytime from anywhere [1][2]. Cloud reserves its abstract nature while providing these services to the cloud users. Cloud comprises different servers. A Datacenter can be a collection of thousands of the servers. Users request for the cloud infrastructure and use the servers for doing their tasks. The cloud provides infrastructure in the form of virtual machines (VMs). Cloud can also provide a whole virtual infrastructure for providing services to the cloud users. And for doing this cloud uses different types of approaches.

Any type system can go under failure. Failure in the cloud environment is not an exceptional case because fault is a nature of the technology. Failure can occur at any time. Failure affects different aspect of the system. Most importantly, it affects the vast worldwide business of the cloud computing. Sometimes a small failure can give a great loss to the cloud service provider. Failure can affect the revenue and the long term image of the cloud service providers.

Failures can be hardware failures and the software failures. Both require different strategy for getting a solution. Hardware failure can be the failure like failure of the memory, failure of the disk, etc. The software failure can be like the failure application failure, Execution time failure, Timeout failure etc.

Reliability of cloud resources does not depend on the reliability of the individual resources. But it depends on the reliability of their collective working. While calculating the reliability of cloud computing it should be kept in mind that whether the components are working in parallel or not.

This paper presents a study of cloud reliability and the techniques which are proposed for measuring and improving the cloud reliability. First the National Institute of Standards and Technology (NIST) standards

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about the reliability of cloud computing are discussed. And then different techniques about measuring the cloud reliability and the techniques to overcome the faults in the cloud environment are discussed.

II. RELIABILITY CONCEPT

A. NIST Standards

As stated in National Institute of Standards and Technology, NIST [3], broadly reliability is the function of four main components of cloud computing.

• The software and hardware offered by the cloud service providers • The personnel resources provided

• Connectivity

• The consumers’ personnel

It is very difficult task to measure the reliability of cloud computing environment. The main reasons for this, first, as there are a number of components in a cloud environment, the individual reliability of these components is different from the reliability of these components taken together. And the second is that it is highly dynamic and depends on the environment. Now, first we have to consider all the possible conditions of failures in cloud environment, then only a reliability model can be considered.

B. DIFFERENT RELIABILITY APPROACHES 1. Adaptive Fault Tolerance (AFT)

It is an adaptive fault tolerance technique [4] in real time cloud computing environment (AFTRC). It tolerates the faults on the basis of reliability of each virtual machine. Based on the reliability a virtual machine is selected. There are two types of nodes, the virtual machine which are running on the cloud and the adjudication node. On the running virtual machine we have, the real time application and the test for acceptance of logical validity. At adjudication node there is the decision system, reliability assessor and time checker. In brief, this technique uses

• Acceptance test: This is for checking the results of the real time algorithms. • Time checker: It checks the time of the results produced by each module. • Reliability assessor: It assesses the reliability of every virtual machine. • Decision mechanism: It is used for making decision about virtual machine. • Recovery cache: It is used for the checkpoints.

2. Cloud Service Reliability Modelling and Analysis

In this paper the authors had presented [5] an inventive reliability model for cloud computing, which deals with several types of failures that affect the success and failure of cloud services. First, a cloud computing system in the VGrADS (Virtual Grid Application Development Software) is proposed. In this system, there is a CMS (Cloud Management System) which is composed of a set of servers that serves four different responsibilities. Such as,

• Managing a request queue that contains the requests of different cloud users • Managing computing resources, such as PCs, etc.

• Managing data resources, such as, Databases, etc.

• Scheduling requests, and assigning these to different computing and data resources.

Whenever any request comes it passes through the CMS, then CMS provides the resources to them. A number of failures were analyzed such as Computing resource missing, Data resource missing, Overflow

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failure, timeout failure, Database failure, Network failure, Software failure and Hardware failure [6][7][8]. Classification of reliability stages is given in figure1.

Figure 1. Reliability Stages

The solution proposed for the Request Stage Reliability is the Markov Model and for Execution Stage Reliability is a graph based model. This model is further enhanced with the combination of the graph based model and the Bayesian network model. The overall reliability is given by the multiplication of the request stage reliability and the execution stage reliability.

3. Fault-Tolerant and Reliable Computation in Cloud Computing

In this paper the authors have explored the security aspect of scientific computation in cloud computing. The proper cloud selection strategy and protection against faulty and mischievous cloud was investigated [9]. They have considered the scientific computation in large matrix multiplication. At first, they assumed that there are several clouds and each of which contains several servers. These servers are trusted partially based on the experience of the individual client and the client knows the reliability and cost of each of the cloud. Different cost for different cloud. The work is divided in the multiplication of the rows and columns on the different clouds. The cost calculation of different clouds is given. Now, the problem is, suppose a client dispatches li rows of matrix A for being multiplied with matrix B in the cloud i. Then the overall cost will be:

C = ∑ The reliability of the dispatched task will be:

R= ( ∑ )/ l Where, l = no of rows in matrix A and ∑

The main objective is to minimize the overall cost C, subject to R >= Rs, where Rs is a minimum reliability requirement which is previously specified. Now, the overall reliability of this task can be expressed as the minimum value of the reliability of all the clouds involved in this calculation, i.e.

, ,…

Then we can simply discard all the clouds with reliability less than the minimum required reliability, Rs. Now, from the remaining set of the clouds we can choose the cloud with reliability value greater than Rs and with the lowest cost Ci first. Then we can choose other clouds with higher reliability as per necessity.

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4. Fault Tolerance and Resilience

In this paper [10], the concept of fault, errors and failure can be expressed by applying the following chain:

Fault Error Failure

The failure behavior of the servers that are contained in the data center can be obtained by the study about the server failures and the hardware failures. It is necessary to apply fault tolerance system to enhance the reliability of hard disks in order to considerably cut down the number of failures. According to the study of the system, failed machines are replaced. The study of the failure behavior of networks should also be done as several network components are associated for constructing the data center. Based on this study, it is observed that the reliability of comprehensive data center network is almost 99.99%. Fault tolerance is the capability of the system to achieve its function in spite of the presence of failures. The classification of faults are done into two categories as shown in the below figure 2:

Figure 2: Classification of Faults

First, Crash faults which block the several system components from functioning or to remain idle at the time of failures for example hard disk crash, power outage, etc.

Second is Byzantine faults that cause the system components to behave incorrectly at the time of failure. As a result, the system shows erratic behavior.

The most popular methods to resolve these above two types of faults are described below:

Checking and monitoring: In this method the system is being observed continuously during its runtime

in order to justify the correctness of the system specification.

Checkpoint and restart: In this method the state of the system is grabbed and stored so that when the

system goes through a failure its correct state is restored using the checkpoint information.

Replication: And in this method the essential system components are replicated or imitated in such a

way so that a copy of this system components is available during a failure.

5. Fault Tolerance Middleware

The Low latency Fault Tolerance (LLFT) middleware [11] is a service that contributes fault tolerance reliable services for distributed applications within data centers that comprises of several servers, storage and networks. By using leader/follower approach, this LLFT middleware imitates the application process in order to secure the application from several faults, particularly, the Crash fault and Timing fault. Due to crash fault, a process or processor does not yield any further result and it does not yield any result within a specific time constraint due to the timing fault. But the Byzantine fault is not handled by this middleware. Two types of leader/follower replications are supported by this LLFT middleware. These are as follows:

Semi active replication: In this process the primary replica orderly arranges the received

messages and executes the operations and also provides the ordering information to the backup replicas for the non-deterministic operations.

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Semi passive replication: It performs not only the same of the above but also in addition to this

the primary replica always communicates state update to the backup replicas. It uses lesser processing power than semi active replication but if the primary fails it acquires greater latency for the purpose of recovery and reconfiguration.

The LLFT middleware comprises of the following:

Low Latency Fault Tolerance (LLFT) Messaging Protocol: It basically contributes two main

services for application messages; these are the following:

• Reliable delivery: In this all the members of a group receive each and every message that

is multicast to this group on a network connection.

• Total ordering: The primary replica in a group communicates the ordering information

to all the backups in this group and all the members in a group hand over the messages to the application maintaining the same order.

Low Latency Fault Tolerance (LLFT) Membership Protocol: This protocol confirms that all the

members of a particular group must have a consistent view about the membership set and the primary replica of that group. It is much faster than a multi-round consensus protocol which is mainly necessary in the case when primary replica fails. The primary replica decides the inclusion and exclusion of the backup replicas to or from the group on the basis of their ranks and precedence.

The precedence of a group member is determined by the order in which the members join the group and the rank of the primary replica is 1 and for the backup replicas it will be 2, 3, 4… which are assigned by the primary replica based on their precedence.

Low Latency Fault Tolerance (LLFT) Virtual Determinizer Framework: The applications in

cloud computing environment commonly incorporate several sources of non-determinism. Therefore, to preserve firm replica consistency, it is vital to mask these sources of non-determinism. It records the ordering information and the results of each non deterministic operation accomplished by the primary replica and at the back up replicas it carries out the same ordering as the primary.

6. A System Level Approach

The purpose of this approach [12] is to overcome the limitations of current existing methodologies by providing fault tolerance properties as on demand services. It contributes flexibility for the applications to dynamically regulate its fault tolerance properties and the level of the required availability and reliability overtime. The cost of the resources can be reduced to a certain extent and the performance level can be adjusted according to the particular business needs. It allows the users to obtain an explicitly fault tolerance support for its applications without having a comprehensive knowledge about the system level proceedings. By adding a new dedicated service layer between the computing framework and the applications, it is possible to provide fault tolerance reliable support to each application abstracting the complications of the elemental infrastructure. To promote a well-developed support, it is necessary for the service layer to accommodate a range of reliability mechanisms and also to construct a fault tolerance solution which can be dispatched to different applications. And to accomplish this, fault tolerance solution can be viewed as a combination of a set of definite activities. For example, each fault tolerance mechanism, such as fault detection, replication of an application, masking and recovery, etc. can be observed as a specific or distinct activity that are combined together to build a fault tolerance solution. Now, this each individual activity can be accomplished as a stand-alone configurable module which produces a consistent solution to a repetitive system failure. Moreover, each module is combined with a set of metadata which characterize its functional, structural and operational properties. These metadata

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can be inspected during runtime and compared with diffe

relevant activities. This approach can be achieved by implementing each module separately as a web service in the form of WSDL [13]

designing a scheme, the Fault Tolerance Manager (FTM). FTM is composed of a set of following components:

• Replication Manager: It incorporates techniques to manage firmness in a replica group by updating

the state of the backup replicas and the primary replica. • Fault Detection/Prediction Manager:

and to send notification regarding this to FTM kernel in order to invoke services from Fault Masking Manager and Recovery Manager. It also notifies Resource Man

the resource state of the cloud.

• Fault Masking Manager: This component involves a collection of algorithms that mask the

occurrence of failures and restrict the faults to meet high availabil

• Recovery Manager: It incorporates all the mechanisms which is used to resume er

a normal node.

• Messaging Monitor: It is used to convey necessary messages among different replicas of a replica

group and also for inter-component c

• Client/Admin Interface: This is used to achieve users’ requirements and act as an interface

FTM and the end users.

• FTM Kernel: It is the pivotal computing component of FTM which manages the reliability m present in the scheme.

• Resource Manager: This component is used to efficiently allocate required resources and to avoid

under provisioning and over provisioning during failures.

7. Fault Tolerant Approaches in Cloud Infrastructure

In most of the recent approaches, fault

customers. But there are no collaborations between them. Therefore, sometimes this leads to a partial or faulty solution. To overcome this issue, different fault tolerance policies in clo

investigated in this paper [14]. There are mainly two types of policies, i.e. in the first one fault tolerance mechanisms are handled solely by either the cloud service provider or the customer and in the second policy there is a collaborative management between the custo

Fig

In general there are three layers in a cloud platform that is shown in the figure Virtual machines and Resources and

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can be inspected during runtime and compared with different users requirements in order to choose relevant activities. This approach can be achieved by implementing each module separately as a web document. The feasibility of this proposed approach is obtained by heme, the Fault Tolerance Manager (FTM).

FTM is composed of a set of following components:

It incorporates techniques to manage firmness in a replica group by updating plicas and the primary replica.

t Detection/Prediction Manager: It is used to detect the faults promptly after their occurrence

and to send notification regarding this to FTM kernel in order to invoke services from Fault Masking Manager and Recovery Manager. It also notifies Resource Manager about the faulty replica to update

This component involves a collection of algorithms that mask the occurrence of failures and restrict the faults to meet high availability demands of the cloud

It incorporates all the mechanisms which is used to resume er

It is used to convey necessary messages among different replicas of a replica component communication.

This is used to achieve users’ requirements and act as an interface

It is the pivotal computing component of FTM which manages the reliability m

This component is used to efficiently allocate required resources and to avoid under provisioning and over provisioning during failures.

Fault Tolerant Approaches in Cloud Infrastructure

In most of the recent approaches, fault tolerance is entirely handled by the cloud service providers or the customers. But there are no collaborations between them. Therefore, sometimes this leads to a partial or faulty solution. To overcome this issue, different fault tolerance policies in cloud computing have been . There are mainly two types of policies, i.e. in the first one fault tolerance mechanisms are handled solely by either the cloud service provider or the customer and in the second

laborative management between the customers and the service providers.

Figure 3. Cloud computing Architecture

In general there are three layers in a cloud platform that is shown in the figure 3

Virtual machines and Resources and each of these are associated with several failures. That is why, there

rent users requirements in order to choose relevant activities. This approach can be achieved by implementing each module separately as a web . The feasibility of this proposed approach is obtained by

It incorporates techniques to manage firmness in a replica group by updating

It is used to detect the faults promptly after their occurrence and to send notification regarding this to FTM kernel in order to invoke services from Fault Masking ager about the faulty replica to update

This component involves a collection of algorithms that mask the ity demands of the cloud users. It incorporates all the mechanisms which is used to resume erroneous nodes to

It is used to convey necessary messages among different replicas of a replica

This is used to achieve users’ requirements and act as an interface between

It is the pivotal computing component of FTM which manages the reliability mechanism

This component is used to efficiently allocate required resources and to avoid

tolerance is entirely handled by the cloud service providers or the customers. But there are no collaborations between them. Therefore, sometimes this leads to a partial or ud computing have been . There are mainly two types of policies, i.e. in the first one fault tolerance mechanisms are handled solely by either the cloud service provider or the customer and in the second

mers and the service providers.

3, these are Applications, each of these are associated with several failures. That is why, there

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are mainly three types of failures: Application failure, Virtual machine failure and Hardware failure. And for these failures there are some fault tolerance so

First fault tolerance method concentrates in stateless applications like proxy e.g. HAProxy or MySQL Proxy. The second is a state-full method, in this customer must implement the functions for storing the state of the server, so that on the next start of t

fault, sensors can be used. First, the faulty VM is deallocated from the job. Second, a new VM is allocated. Third, start the tasks that are running on the failed VM. Fourth, restore the state of th

Fault tolerance system. The customer cannot see all these types of the fault. These can be monitored the cloud service providers. This is done by a monitoring system composed of the sensors. These techniques are used in [15][16].

8. A Virtualization and Fault Tolerance Approach

Fault tolerance is provided to cloud infrastructure by implementing the cloud manager [17], Load balancer, Fault Handler and the Decision maker. A parameter, success rate is used specially for fault tolerance. In this, Job is given to the virtual machine which has the success rate more than some specific value. In this way the chances of the fault decrease. The fault handler has the responsibility that when a VM is found to be faulty its performance table must b

performance table the cloud infrastruct

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are mainly three types of failures: Application failure, Virtual machine failure and Hardware failure. And for these failures there are some fault tolerance solutions that are described in figure 4

Figure 4. Fault Tolerance

First fault tolerance method concentrates in stateless applications like proxy e.g. HAProxy or MySQL full method, in this customer must implement the functions for storing the state of the server, so that on the next start of the system this state can be resumed. For repairing the VM fault, sensors can be used. First, the faulty VM is deallocated from the job. Second, a new VM is allocated. Third, start the tasks that are running on the failed VM. Fourth, restore the state of th

Fault tolerance system. The customer cannot see all these types of the fault. These can be monitored This is done by a monitoring system composed of the sensors. These

A Virtualization and Fault Tolerance Approach

Fault tolerance is provided to cloud infrastructure by implementing the cloud manager [17], Load balancer, Fault Handler and the Decision maker. A parameter, success rate is used specially for fault In this, Job is given to the virtual machine which has the success rate more than some specific value. In this way the chances of the fault decrease. The fault handler has the responsibility that when a VM is found to be faulty its performance table must be updated. According to the success rate and the performance table the cloud infrastructure is made more fault tolerant.

are mainly three types of failures: Application failure, Virtual machine failure and Hardware failure. And in figure 4:

First fault tolerance method concentrates in stateless applications like proxy e.g. HAProxy or MySQL-full method, in this customer must implement the functions for storing the

he system this state can be resumed. For repairing the VM fault, sensors can be used. First, the faulty VM is deallocated from the job. Second, a new VM is allocated. Third, start the tasks that are running on the failed VM. Fourth, restore the state of the VM for physical Fault tolerance system. The customer cannot see all these types of the fault. These can be monitored by This is done by a monitoring system composed of the sensors. These

Fault tolerance is provided to cloud infrastructure by implementing the cloud manager [17], Load balancer, Fault Handler and the Decision maker. A parameter, success rate is used specially for fault In this, Job is given to the virtual machine which has the success rate more than some specific value. In this way the chances of the fault decrease. The fault handler has the responsibility that when a e updated. According to the success rate and the

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III. TABULAR ANALYSIS

Tabular analysis of different approaches is done as shown in table 1. First column tells about the component for reliability measurements, these components are like VM, some approaches broadly consider the reliability of the whole infrastructure and the system. The second column represents the methodology used for cloud reliability measurement. Third column represents the techniques and components used for reliability measurement. It also represents the effect of techniques on the reliability of the system. Sl No. Technique Name Components for the reliability measurement Methodology for measuring reliability Reliability measurement Used for B.1 Adaptive fault tolerance (AFT) Reliability of each virtual machine is measured. Virtual machines are divided into two category running and adjudication virtual machines. Acceptance test, Time checker, Reliability assessor, Decision mechanism, Recovery cache are used. Real time cloud computing B.2 Cloud Service Reliability Modelling and Analysis Reliability of the system is measured Reliability is divided into two parts: request time reliability and execution time reliability Total reliability is measured by the product of the two reliabilities. Handling different failures in Cloud Computing Environment B.3 Fault-Tolerant and Reliable Computation in Cloud Computing Reliability of the server is measured Reliability and the cost relation are studied. Reliable component with the reliability, greater than a threshold value and having less cost is selected. General Scientific computation B.4 Fault Tolerance and Resilience Failure of a machine Faults are divided into two parts Crash faults and Byzantine Faults. Reliability of the system increase with the checkpoint, restart, replacement. Characterizing recurrent failures in Cloud environment B.5 Fault Tolerance Middleware

Fault in the system Faults are divided into two parts Crash and Timing fault. By providing a middleware service overall reliability of the system increases. Distributed applications fault tolerance B.6 A System Level Approach Reliability as a service By introducing the FTM for Reliability of the system increases by providing fault tolerance property as on

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reliability using FTM. demand service B.7 Fault Tolerant Approaches in Cloud Infrastructure

Faults in the system Faults are divided into application, virtual machine and the physical node faults. Reliability is increased by the stateless and the state-full approaches. Replication and the sensors increase the fault tolerance. Autonomic repairing of faults B.8 A Virtualization and Fault Tolerance Approach Cloud infrastructure Faults are handled by the fault handler. Automatic updating the system by the fault handler and using success rate parameter to increase the reliability of the system. Reducing the service time and increasing the system availability in a Cloud environment

IV. CONCLUSION AND FUTURE WORK

In this paper, we have studied different approaches for cloud computing reliability. There are many issues about the cloud reliability like the heterogeneity, dynamic nature etc. Reliability of cloud computing depends on the reliability of its components like VM, Physical nodes or the application running on the cloud environment. There are several types of faults in the cloud environment like crash fault, timing fault, application faults, etc. Reliability of the cloud environment can be increased by replication, restart, continuous auditing of all the information about each component of cloud environment, by using efficient sensors for monitoring. In future we will work on the improvement of the cloud reliability by proposing a mechanism which implements a collection of these reliability approaches.

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