A Secured Decentralized Cloud Firewall to achieve Resources Provisioning Cost Optimization and QOS

DOI : 10.17577/IJERTCONV5IS20042

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A Secured Decentralized Cloud Firewall to achieve Resources Provisioning Cost Optimization and QOS

Mallika T M

Nandini K

Sowndarya M H

Dept. Of CSE

Dept. Of CSE

Dept. Of CSE

GMIT, Bharathinagar

GMIT, Bharathinagar

GMIT, Bharathinagar

Likitha R

Bhramaramba D S

Pradeep B M

Dept. Of CSE

Dept. Of CSE

Dept. Of CSE

GMIT, Bharathinagar

GMIT, Bharathinagar

GMIT, Bharathinagar

AbstractCloud computing technique is becoming popular as the next infrastructure of computing platform. Security has become the major concern that people hesitate to transfer their applications to clouds. Concretely, cloud computing platform is under numerous attacks. As a result, it is denitely expected to establish a secured rewall to protect cloud from attacks. By employing embedded Markov chain and Z-transform techniques, we obtain a mean packet response time. However, setting up a secured centralized rewall for a whole cloud data center is infeasible from both performance and nancial aspects. In this paper, we propose a secured decentralized cloud rewall framework for individual cloud customers. We investigate how to dynamically allocate resources to feasibility of resources provisioning cost, while satisfying QoS requirement requested by individual customers simultaneously. Our numerical results also indicate that we are able to set up cloud rewall with affordable cost to cloud customers. Through extensive simulations and experiments, we conclude that an M/Geo/1 model results in the cloud rewall real system much better than a traditional M/M/1 model.

Keywords Cloud computing, rewall, resources allocation, system modeling


    Cloud computing is becoming popular as the next infra- structure of computing platform in the IT industry [1].

    The large volume hardware and software resources pooling and delivered on demand, cloud computing provides rapid elasticity. In this service-oriented architecture, cloud services are broadly offered in three forms: Infrastructure- as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a- Service (SaaS). Cloud computing also brings down both capital and operational expenditure for cloud customers by outsourcing their data and business.

    On one hand, traditional attacks such as Distributed Denial of Service (DDoS), viruses and phishing still exist in clouds. The new specic attacks on the computing mechanisms of cloud have also been found, including Economic Denial of Sustainability (EDoS) attack [3], cross Virtual Machine (VM) attack [4], and so on. It is an effective and necessary choice of

    establishing a cloud rewall to protect cloud data centers from all these attacks. Compared to a cloud platform, traditional rewalls are generally deployed for private networks which host relatively specic services [7], [8].

    Only a few work [9], [10] have been done on cloud rewall, and both proposed a centralized cloud rewall. The diversity of heterogeneous services and complex attacks denitely means a large rule set and high packet arrival rate if a centralized rewall is applied for a whole cloud data center. As a result, it is hard to guarantee QoS requirement specied by cloud rewall customers. Moreover, important design factors like packet arrival rateduration and number of rules are customers specic. Therefore, it is more practical to offer a cloud rewall for individual cloud rewall customers.

    Generally, a question that arises in setting up a cloud rewall is how to price this service. From cloud rewall customers perspective, they prefer to rent a cloud rewall from rewall providers as cheap as possible. While for cloud rewall providers, their primary goal is nancial reward. Therefore, cloud rewall providers need to optimize resources provisioning cost, which offers a chance of lowering the cloud rewall price on behalf of customers without reducing providers prot. There is an inherent tradeoff between the two goals: to guarantee a pressed response time, large volume of resources should be invested by cloud rewall providers, which in turn increase provisioning cost (and vice-versa). Meanwhile, QoS requirement about the cloud rewall system specied by customers should be satis- ed.

    In our paper, we propose a decentralized cloud rewall framework. The cloud rewall is offered by Cloud Service Providers (CSP) and placed at access points between cloud data center and the Internet. Individual cloud customer rents the rewall for protecting his cloud hosted applications. Hosting servers of applications are grouped into several clusters, and resources are then dynamically allocated to set up an individual rewall for each cluster. All these parallel rewalls will work together to monitor incoming packets, and

    guarantee QoS requirement specied by cloud customers at the same time. By covering the vast cloud and rewall related parameter space, we formulate the resources provisioning cost.

    As aforementioned, the essential issue to achieve a nancial balance between rewall providers and customers is to optimize resources cost. In order to conduct the optimization, we need to capture mean packet response time through the rewall system. As widely adopted in cloud performance analysis [5], [11], we employ queuing theory to undertake system model- ing. However, we have to point out that the cloud rewall service times follow a geometric distribution according to rule match discipline.

    The contributions of this paper are summarized as follows:

    • We propose a decentralized cloud rewall framework for individual cloud rewall customers.

    • Resources are dynamically allocated to optimize the provisioning cost, and guarantee QoS requirement specied by customers at the same time.

    • We introduce novel queuing theory based model M/ Geo/1 or M/Geo/m for performance analysis of the proposed cloud rewall. By employing Z- transform and embedded Markov chain techniques, a closed- form expression of mean packet response time is derived.

    • Extensive simulations and experiments are con- ducted to verify our analytical model. The simulation results claim that geometric distribution is more suitable for rewall system modeling, and give a deep insight into tradeoff among optimal resources provisioning cost, QoS requirement and packet arrival rate.


    In this section, we rst discuss several important character- istics about cloud rewall, and then present our decentralized cloud rewall framework.

      1. Basic Knowledge About Cloud Firewall

        Dynamic packet arrival rate. In general, cloud services are hired by legitimate customers. However, cloud applications are also vulnerable to various attacks, and a long time attack is usually rare as they can easily detected [11]. Therefore, incoming packets to cloud rewalls are composed of long term legitimate packets and bursty attack packets. In addition, packet arrival rate is dynamically changing over the time. Moreover, arrival rate of legitimate packets from benign customers is relatively low, while attack packets for malicious purposes are usually at a high rate. In conclusion, it requires a feasible model to capture the dynamic packet arrival rate in both attack and normalperiod.

        As a main threat to cloud availability [2], here we take DDoS attack for example. Moore et al. [12] indicated that the average DDoS attack duration is around 5 minutes, with the average DDoS attack rate being around 500 requests per second. While Yu et al. [11] presented that the mean arrival

        rate to an observed e-business site in normal period is lower than 10 requests per second.

        On-demand resources provisioning. In order to provide a cloud rewall, rewall service providers should invest vari- ous resources to ght against possible attacks. Current CSPs usually pack resources such as CPU, bandwidth and storage into Virtual Machine instances for service. Generally, multi- ple VM instance types are offered and each type has a lim- ited service capacity for a particular application, which is evident by analysis results in [13].

        In our case, VM instances are launched by providers to host the cloud rewall. When packet arrival rate increases, a single VM instance tends to be incapable of handling the massive incoming packets, or the response time will violate QoS requirement specied by customers. According to QoS requirement, packet arrival rate and VM instances service rate, rewall service providers need to invest more resources on- demand by launching additional VM instances. New VM instances can be cloned based on the image le of the original rewall using the existing clone technology [14], [15]. Specically, rewall providers have to invest different volume of resources in attack and normal period.

        Cost and performance tradeoff. There is an inherent tradeoff between the following two goals:

        • QoS requirement satisfaction. Mean packet response time requirement specied in QoS should be satised.

        • Resources provisioning cost optimization. Resources provisioning cost of cloud rewall should be mini- mized as long as QoS requirement is satised.

      2. A Decentralized Cloud Firewall Framework

    As aforementioned, each VM instance has a limited service capacity for a cloud firewall application. Hosting a cloud firewall in a single VM instance (even the most powerful one) tends to be incapable of satisfying customer specific QoS requirement. In other words, its hard to guarantee response time through a centralized cloud firewall. Therefore, we propose a decentralized framework where several firewall run in parallel. As shown in Fig. 1, hosting servers are grouped into several clusters and a VM instance is launched to host an individual firewall for each cluster. By distributing the packet arrival rate into several parallel firewalls and launching suitable VM instance for each firewall, response time through each firewall can satisfy the QoS requirement.

    Suppose that there are M servers in the cloud data center hosting applications of an individual cloud firewall customer. xn denotes packet arrival rate to these applications in non-attack period


    1 j J

    1 j J

    1 jn n

    (superscript n stands for normal or non-attack). The M servers are grouped into J clusters (m n,m n,m n) for processing legitimate packets. (V n,V ,V ) denote the set of VM instances which host the parallel cloud firewall for each cluster. ( n, n, n) denote packet arrival rates to each firewall,

    the parallel firewalls. In order to cover the vast cloud firewall related parameter space, the resources provisioning cost is formulated as follows:



    Here pnj and pak denote unit price of VM instance V n j in non- attack period and Vak in attack period, respectively (If the two VM instances are of the same type, then pnj = pak ). µn and µak denote service rate of the two VM instances when running the

    cloud firewall, which are in terms of packets per second (pps) and will be given later. rn and ra are response time through

    then n j a k


    1 j J

    1 j J 1 j

    We define the corresponding variables in attack period as follows: xa denotes packet arrival rate to the hosting servers (superscript a stands for attack), which are grouped into K clusters (m a,m a,m a) for processing attack packets. (V a,V a,V a) denote VM instances space, and ( a, a, a) denote packet arrival rates to each firewall. Similarly we have,


    We first formulate resources provisioning cost. As firewall service rate modeling is critical to resources provisioning cost optimization, we establish a mathematical model according to cloud firewall rule matching discipline and derive that system service times follow geometric distribution.

    3.1 Resources Provisioning Cost

    Let Tn denote the unit time interval that CSPs charge VM instances. Ta denotes average attack duration in Tn. For simplicity, the scenario that various types of attacks occur with unequal attack rate and attack duration is not covered in this paper. In fact, our model can be easily extended to this general case.

    Our primary goal is to optimize resources provisioning cost, while satisfying QoS requirement at the same time. It is intuitive that resources provisioning cost for our proposed cloud firewall depends on packet arrival rate. Given xa and xb, it further relies on how many clusters (J and K) are formed. Moreover, it is determined by VM instance configuration for

    firewall for cluster m j and m k in non-attack and attack period respectively, and they also will be given later. T is an acceptable response time threshold specified in firewall customers QoS requirement.

    The objective function (3) is to minimize resources provisioning cost for our proposed cloud firewall. Equations

    1. and (5) are the conditions that have to be met when configuring VM instances for each firewall in non-attack and attack period, respectively. Concretely, QoS requirement constraint has to be met, and arrival rate to each firewall should be less than its service rate to keep the system in a stable state.

      In general, CSPs specify a limitation of concurrent VM instances that are available to an account [4]. For example, this threshold is 20 in Amazon EC2. As a result, we can simply iterate J and K to find the optimal solution to equation (3). In each iteration, a greedy algorithm is applied to get an optimal cost for each J and K. Concretely, we rank the VM instances in ascending order according to service rate µ then choose VM instance which satisfies QoS requirement T with least µ.

      These obtained VM instances are just the VM configuration that leads to the optimal cost for each given J and K. Finally, by minimizing these optimal costs over J and K, we are able to find an optimal solution to Equation (3).

      In order to simplify the calculation, we assume that packet arrival rate to each firewall is proportional to number of servers included in the cluster. Then the mean packet arrival rate to firewall for cluster mnj and mak are given by,


    In this section, we first validate our analytical model and investigate basic parameter settings of the proposed cloud firewall. Then the tradeoff among resources provisioning cost, QoS requirement and packet arrival rate is thoroughly studied.

      1. Analytical Model Validation

        In the following experiments, we take VM instances offered by Amazon EC2 for calculation. Two pricing options for VM instances are offered by Amazon EC2: on-demand and reservation. To capture the dynamic resources provisioning and allow for request of VM instances at any time, we employ the on demand pricing option.

        We first have to give a sensible estimation of service rate of each VM instance, which is determined by N, T, m and p according to Equation (13). Here N is set 1,000 and p = 1/N. As cloud firewall should be transparent to users, we assume response time through each cloud firewall is in granularity of millisecond (which is reasonable according to analysis results in [5]). As a resul, rule matching time T should be in granularity of microsecond. In this paper, T is set as 27 µs. Service rate of VM instances are listed in Table 1.

        As discussed previously, we use average packet response time through the cloud firewall as a key metric for our performance evaluation. First, we are interested in the comparison between our M/Geo/1 model and the general M/M/1 model. The experimental results are shown in Fig. 4. It is easy to find that our M/Geo/1 model outperforms the M/M/1 model as it matches the simulation results much better. In other words, its more reasonable to assume the firewall service rate follows a discrete geometric distribution than a continuous exponential distribution. Here is set at most 50 packets per second as service rate of the small VM instance is 58.

        For an M/Geo/m queue, its closed-form response time is approximately given, which is a decision variable to resources provisioning cost optimization. Therefore, we have to check whether this approximation is reasonable. We simulate the relationship between average request response time and attack rate, and compare simulation results with analytical results derived for M/Geo/m. The simulation is conducted for an extra large VM instance, i.e., m = 8.

        Both analytical and experimental results are illustrated in Fig.

  5. Our M/Geo/m model is confirmed by the simulation results that the mean experimental response time fluctuates around the expectation obtained from Equation. As can be seen from Fig. 5, the average response time smoothly increase when attack rate grows. However, as offered load 1, the response time increase sharply. The reason for this sharp increase is that the arrival packets to the extra large VM instance reaches its maximum processing rate of 1/t, which is approximately 468 packet per second (pps).

The results also confirm our earlier claim that a centralized firewall for a whole cloud platform is impractical. It is rather easy for packet arrival rate in attack period to exceed service rate of VM instances. A much larger N (e.g., 10,000 rules) makes it even worse. Based on these results, we claim that the proposed decentralized cloud firewall is necessary and feasible.

    1. Firewall Parameter Settings

      Cloud customers usually have personalized requirements for firewall, which is mainly due to that different applications are of varying degrees vulnerable to various attacks. For example, an e-business website is highly likely more vulnerable to phishing attack compared to a news site due to that cloud customers earn much more money from the former. Therefore, rule set in cloud firewalls differs for cloud customers. In this section, we aim to find the relationship between N, p and mean packet response time. In Fig. 6, we show the relationship between number of rules N and average response time of a cloud firewall. Here p=1/5, 000 and is set at most 90 pps due to that service capacity of the extra large VM instance is now approximately 93 pps according to Equation. From our analytical model, its expected that more rules decrease service capacity and result in more time to process arrival packets of a given attack rate, which is confirmed by the simulation results.

      Fig. 7 exhibits the impact of rule matching probability p against average response time. is set as 90. Its easy to find that a larger matching probability p leads to less response time, which means firewall service providers are encouraged to put rules easier to match on top of the rule list in cloud firewall to satisfy firewall service customers QoS requirement.


      To the best of our knowledge, this paper is an early work to discuss resource provisioning cost optimization in the context of cloud firewall. As a new research field, there are many other mathematical tools available to address this optimization problem, such as game theory [25], integer linear programming [24] and stochastic programming [26]. Due to the limitations of knowledge, time and space, we have only employed queuing theory in this paper.

      Our analytical model assumes that packet arrivals to the cloud server follow Poisson distribution, and the service times follow Geometric distribution. For certain types of network traffic, assuming Poisson arrivals is feasible [27]. However, for general traffic like Ethernet, their arrivals do not always follow a Poisson distribution but are rather bursty or heavy- tailed [28], [29]. Also, the assumption that all rules share the same matching probability is hard to meet in reality. Considering different matching probability and non-Poisson distribution will make a closed-form analytical solution intractable. To address these limitations, Discrete Event Simulation (DES) can be employed [30].


      In this paper, we point out that its impractical to establish a firewall for a whole cloud data center. However, cloud service providers possess a potential to provide cloud firewalls for individual cloud customers. In view of this challenge, we propose a decentralized cloud firewall framework, where several firewall running in parallel to guarantee QoS requirement. As resources are dynamically allocated in cloud firewall, we investigate how to optimize the resources provisioning cost. We establish novel queuing theory based model for performance analysis of the proposed cloud firewall, where firewall service times are modeled to follow geometric distribution. Extensive simulations confirm that M/Geo/1 reflects the cloud firewall real system better than traditional M/M/1. Besides, it is feasible to set up firewall for individual cloud hosted services with an affordable cost to cloud customers.

      As future work, we first plan to improve the decentralized framework to capture more personalized details in application level. Second, we would like to propose a pricing model for the cloud firewall, which helps to achieve a financial balance between provider and customer. Real cloud environment experiments for the proposed cloud firewall are also expected in the near future.


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