Wesam Dawoud

Elastic VM for Cloud Resources Provisioning Optimization

Rapid growth of E-Business and frequent changes in websites contents as well as customers’ interest make it difficult to predict workload surge. To maintain a good quality of service (QoS), system administrators must provision enough resources to cope with workload fluctuations considering that resources over-provisioning reduces business profits while under-provisioning degrades performance. In this paper, we present elastic system architecture for dynamic resources management and applications optimization in virtualized environment. In our architecture, we have implemented three controllers for CPU, Memory, and Application. These controllers run in parallel to guarantee efficient resources allocation and optimize application performance on co-hosted VMs dynamically. We evaluated our architecture with extensive experiments and several setups; the results show that considering online optimization of application, with dynamic CPU and Memory allocation, can reduce service level objectives (SLOs) violation and maintain application performance…

Elastic Virtual Machine for Fine-Grained Cloud Resource Provisioning

Elasticity is one of the distinguishing characteristics associated with Cloud computing emergence. It enables cloud resources to auto-scale to cope with workload demand. Multi-instances horizontal scaling is the common scalability architecture in Cloud; however, its current implementation is coarse-grained, while it considers Virtual Machine (VM) as a scaling unit, this implies additional scaling-out overhead and limits it to specific applications. To overcome these limitations, we propose Elastic VM as a fine-grained vertical scaling architecture. Our results proved that Elastic VM architecture implies less consumption of resources, mitigates Service Level Objectives (SLOs) violation, and avoids scaling-up overhead. Furthermore, it scales broader range of applications including databases.

Accurate Mutlicore Processor Power Models for Power-Aware Resource Management

Power management is one of the biggest challenges facing current data centers. As processors consume the dominant amount of power in computer systems, power management of multicore processors is extremely significant. An efficient power model that accurately predict the power consumption of a processor is required to develop effective power management techniques. However, this challenge rises with using virtualization and increasing number of cores in the processors. In this paper, we analyze power consumption of a multicore processor, we develop three statistical CPU-Power models based on the number of active cores and average running frequency using a multiple liner regression. Our models are built upon a virtualized server. The models are validated statistically and experimentally. Statistically, our models cover 97\% of system variations. Furthermore, we test our models with different workloads and three benchmarks. The results show that our models achieve better performance compared to the recently proposed model for power management in virtualized environments. Our models provide highly accurate predictions for un-sampled combinations of frequency and cores, 95\% of the predicted values have less than 7\% error. Thus, we can integrate these models into power management mechanisms for a dynamic configuration of a virtual machine in terms of the number of its virtual-CPUs and the frequency of physical cores to achieve both performance and power constrains.

Analysis and Simulation of HPC Applications in Virtualized Data Centers

Cloud computing is a consolidation environment that hosts heterogeneous applications. Currently, clouds use virtualization technologies to provide an isolated execution environment and agile resource provisioning. Furthermore, live migration is widely exploited to achieve energy saving. However, consolidation and live migration can cause resource contention and utilization overhead that influence application performance. In this paper, we implement a network model and a memory subsystem model into CloudSim simulator. Then, we present a thorough analysis of the High Performance Computing NPB suite including memory bandwidth demand, communication patterns between processes, and migration overhead. To study the performance of the NPB Benchmark suite, we simulate the behaviour of these benchmarks including multi-thread communication via shared memory, multi-process communication via network. Thus, this allows implementing efficient VMs scheduling and resource provisioning policies. The results show that using CPU utilization as a trigger to perform management action such as VM migration is misleading and might aggravate application performance. Furthermore, we present a power model including the utilization of CPU, memory-bus, and network. This model provides a precise estimation of power consumption.

Dynamic scalability and contention prediction in public infrastructure using Internet application profiling

Recently, the advance of cloud computing services has attracted many customers to host their Internet applications in the cloud. Infrastructure as a Service (IaaS) is on top of these services where it gives more control over the provisioned resources. The control is based on online monitoring of specific metrics (e.g., CPU, Memory, and Network). Despite the fact that these metrics guide resources provisioning, the lack of understanding application behavior can lead to wrong decisions. Moreover, current monitored metrics alone do not help in resources contention prediction, which is very common in shared infrastructures like IaaS. Nevertheless, the architecture of Internet applications, as multi-tier systems, makes contention prediction more complex while its influence can migrate from one tier to another. In this paper, we propose a pro-active global controller not only for dynamic resources provisioning, but also for predicting and eliminating contentions in multi-tier applications. Our technique combines monitored metrics, which are provided by current IaaS providers, with models that are built depending on the Internet applications profiling. The fitness of the monitored metrics to the application model is used for contention prediction. We examined our technique using RUBiS benchmark. The results express the efficiency of the developed algorithms in maintaining Internet applications performance even in shared infrastructures.

Security in Tele-Lab — Protecting an online virtual lab for security training

The rapid burst of Internet usage and the corresponding growth of security risks and online attacks for the everyday user or the enterprise employee have emerged the terms Awareness Creation and Information Security Culture. Nevertheless, security education widely has remained an academic issue. Teaching system or network security on the basis of practical experience inherits a great challenge for the teaching environment, which is traditionally solved using a computer laboratory at a university campus. The Tele-Lab project offers a system for hands-on IT security training within a remote virtual lab environment — over the web, accessible by everyone. Such a system is inherently exposed to various security threats, since it has to provide full access to virtual machines running attack tools for potentially malicious users. The paper at hand introduces usage, management and operation of Tele-Lab as well as its architecture. Furthermore, this work focuses on possible attacks, the challenges when securing such a system, and shows how to set up an infrastructure that ensures the main security objectives identified as authentication, authorisation and availability.