Manar Jammal

Software defined networking: State of the art and research challenges

Network usage and demands are growing at a rapid pace, while the network administrators are facing difficulties in tracking the frequent users’ access of the network. Consequently, managing the infrastructure supporting these demands has become a complicated and time-consuming task. Networks are also in a flux state, they are not only expanding but require reconfigurations to meet the business needs. Software defined networking (SDN) and network function virtualization (NFV) technologies have emerged as promising solutions that change the cost profile and agility of internet protocol (IP) networks. Conceptually, SDN separates the network control logic from its underlying hardware, enabling network administrators to exert more control over network functioning and providing a unified global view of the network. However, SDN and NFV can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as Service offerings. We also present the various challenges facing SDN, from scalability to reliability and security concerns, and discuss existing solutions to these challenges.

Resource allocation in a network-based cloud computing environment: design challenges

Cloud computing is a utility computing paradigm that has become a solid base for a wide array of enterprise and end-user applications. Providers offer varying service portfolios that differ in resource configurations and provided services. A comprehensive solution for resource allocation is fundamental to any cloud computing service provider. Any resource allocation model has to consider computational resources as well as network resources to accurately reflect practical demands. Another aspect that should be considered while provisioning resources is energy consumption. This aspect is getting more attention from industrial and government parties. Calls for the support of green clouds are gaining momentum. With that in mind, resource allocation algorithms aim to accomplish the task of scheduling virtual machines on the servers residing in data centers and consequently scheduling network resources while complying with the problem constraints. Several external and internal factors that affect the performance of resource allocation models are introduced in this article. These factors are discussed in detail, and research gaps are pointed out. Design challenges are discussed with the aim of providing a reference to be used when designing a comprehensive energy-aware resource allocation model for cloud computing data centers.

High availability-aware optimization digest for applications deployment in cloud

Cloud computing is continuously growing as a business model for hosting information and communication technology applications. Although on-demand resource consumption and faster deployment time make this model appealing for the enterprise, other concerns arise regarding the quality of service offered by the cloud. One major concern is the high availability of applications hosted in the cloud. This paper demonstrates the tremendous effect that the placement strategy for virtual machines hosting applications has on the high availability of the services provided by these applications. In addition, a novel scheduling technique is presented that takes into consideration the interdependencies between applications components and other constraints such as communication delay tolerance and resource utilization. The problem is formulated as a linear programming multi-constraint optimization model. The evaluation results demonstrate that the proposed solution improves the availability of the scheduled components compared to OpenStack Nova scheduler.

CHASE: Component High Availability-Aware Scheduler in Cloud Computing Environment

Cloud computing promises flexible integration of the compute capabilities for on-demand access through the concept of virtualization. However, uncertainties are raised regarding the high availability of the cloud-hosted applications. High availability is a crucial requirement for multi-tier applications providing business services for a broad range of enterprises. This paper proposes a novel component high availability-aware scheduling technique, CHASE, which maximizes the availability of applications without violating service level agreements with the end-users. Using CHASE, prior criticality analysis is conducted on applications to schedule them based on their impact on their execution environment and business functionality. This paper presents the advantages and shortcomings of CHASE compared to an optimal solution, Open Stack Nova scheduler, high availability-agnostic, and redundancy-agnostic schedulers. The evaluation results demonstrate that the proposed solution improves the availability of the scheduled components compared to the latter schedulers. CHASE prototype is also defined for runtime scheduling in Open Stack environment.

A Formal Model for the Availability Analysis of Cloud Deployed Multi-tiered Applications

High availability is a critical requirement for cloud deployed services. Cloud providers offer different availability zones with geo-redundancy to protect their infrastructure and consequently their tenants against failures and natural disasters. Nevertheless, different zones may have different reliability levels depending on the hardware equipment, the geo-location, the energy source powering the facility, etc. Hence, the ability to assess the expected availability of a given deployment is extremely important for both the cloud tenants and providers that are bound by a service level agreement. Due to the stochastic nature of failures, a formal stochastic model is needed to quantify the expected availability offered by an application deployment. This paper presents a Stochastic Petri Net model to evaluate the availability of cloud services and their deployment in geographically distributed data centers. The proposed Stochastic Petri Net model captures the characteristics of the cloud provider and user. It translates them into elements of an availability model that can be solved to calculate the expected availability and subsequently be used to guide the cloud scheduling solution.

Availability Analysis of Cloud Deployed Applications

High availability (HA) is a main key performance indicator for cloud deployed services. Cloud providers offer different availability zones possibly located in different geographical regions. To protect cloud services against failures and natural disasters, it is recommended to deploy the applications on redundant resources across multiple zones and distribute the workload through a load-balancer. Different cloud infrastructure, located in different geographical zones with different energy source powering, hardware quality, etc., may have different reliability levels. Scheduling a cloud service on different zones while meeting the service level agreement availability requirements necessitate a solution to assess the expected availability of a given deployment. To quantify the expected availability offered by an application deployment, a formal stochastic model is required to capture the stochastic behavior of failures. This paper proposes a stochastic Petri Net model that captures the stochastic characteristics of cloud services and translates them into elements of an availability model. The model evaluates the availability of cloud services and their deployments in geographically distributed data centers (DCs). The results are useful to generate guidelines for an HA-aware scheduling.

Orchestrating network function virtualization platform: Migration or re-instantiation?

Network function virtualization (NFV) provokes the evolution of network functions to overcome various challenges facing the network service providers (NSPs). To exploit the advantages of the virtualization technology, NFV platforms should use the cloud environment to provide their services. Typically, an NFV service is represented by a service function chain (SFC) that consists of multiple virtualized network functions (VNFs). Hosting and orchestrating these VNFs in a cloud environment are challenging tasks. In this paper, we discuss the VNF orchestration problem from the perspective of VNFs migration and re-instantiation mechanism to achieve carrier grade-aware NFV services in a cloud-based platform. This paper also provides detailed insights on the NFV system modeling, building blocks, and various challenges hindering its cloud adoption. Also, a novel mixed integer linear programming (MILP) optimization model is proposed as a solution to facilitate the NFV platform orchestration in a cloud environment. The model decides between triggering either VNFs migration or re instantiation while achieving minimal downtime of the VNF, satisfying carrier grade requirements, and finding an optimal placement for the migrated or re-instantiated VNF that minimizes the SFC delays. The proposed model is compared to two availability-agnostic greedy algorithms. The simulation results show that finding an optimized decision whether to migrate or re-instantiate a VNF while associating it with an optimal placement can achieve a minimal VNFs downtime and SFCs delays and can enhance the quality of service accordingly.

Mitigating the Risk of Cloud Services Downtime Using Live Migration and High Availability-Aware Placement

The growing dependency of users on social media, telecommunication services, mobile applications, banking amenities, and other cloud services requires a plan that mitigates inevitable failures and ensures the always-on access to these services. This emanates high availability (HA) concerns regarding the adoption of cloud. To maintain HA, the cloud provider and/or user should design a system that is immune to both application and infrastructure failures. This paper proposes live migration approach to maintain service delivery upon a sudden failure, a virtual machine (VM)/infrastructure overload, or maintenance. It develops a mixed integer linear programming model that minimizes the migration downtime based on the VM memory pages and the optimal HA-aware placement of the VM. It also provides different design considerations to achieve HA-aware applications placement. The proposed placement is used in the migration approach to find new hosts for the VMs. It considers VMs/applications deployments in geographically distributed data centers and satisfies redundancy, applications interdependency, and other HA and performance requirements. Then the deployments are assessed using a formal Petri Net model to improve them in terms of HA. The HA-aware placement and migration approaches are evaluated on 3-tier web applications.

Improvement of Markov chain processes for mathematical optimization of cancer treatment

Biologists have uncovered some of the most basic mechanisms by which normal cells develop into cancerous tumors. These biological theories can be transformed into adequate mathematical models. For this reason, we attempt to study the evolution of cancer cells using the Markov Chain Processes. Based on Markov chain Processes, cancer chemotherapy will be applied on them to treat the disease. However, chemotherapy is a complex treatment mode that requires balancing the benefits of treating tumors using anti-cancer drugs with the adverse toxic side-effects caused by these drugs. Some methods of computational optimization, Genetic Algorithm (GA) in particular, have proven to be useful in helping to strike the right balance. The purpose of this paper is to put in place a strategy to solve an optimal problem to facilitate finding optimal chemotherapeutic treatments which cause the death of cancer and have fewer side effects based on a chemotherapy treatment defined by the oncologist.