A Comparative and Analytical Study for Choosing the Best Suited SDN Network Operating System for Cloud Data Center

The growing deployment of Software Defined Network (SDN) paradigm in the academic and commercial sectors resulted in many different Network Operating Systems (NOS). As a result, adopting the right NOS requires an analytical study of the available alternatives according to the target use case. This study aims to determine the best NOS according to the requirements of Cloud Data Center (CDC). This paper evaluates the specifications of the most common open-source NOSs. The studied features have been classified into two groups, i.e., non-functional features such as availability, scalability, ease of use, maturity, security and interoperability, and functional features, such as virtualization, fault verification and troubleshooting, packet forwarding techniques and traffic protection solutions. A Decision support system, Analytical Hierarchy Process (AHP) has been applied for assessing specifications of the inspected NOSs, namely, ONOS, Opendaylight (ODL), Floodlight, Ryu, POX and Tungsten. Our investigation revealed that ODL is the most suitable NOS for CDC compared to the rest studied NOSs. However, ODL and ONOS have almost similar scores compared to the rest NOSs.

Edge-Based Detection of Varroosis in Beehives with IoT Devices with Embedded and TPU-Accelerated Machine Learning

One of the causes of mortality in bees is varroosis, a bee disease caused by the Varroa destructor mite. Varroa destructor mites may occur suddenly in beehives, spread across them, and impair bee colonies, which finally die. Edge IoT (Internet of Things) devices capable of processing video streams in real-time, such as the one we propose, may allow for the monitoring of beehives for the presence of Varroa destructor. Additionally, centralization of monitoring in the Cloud data center enables the prevention of the spread of this disease and reduces bee mortality through monitoring entire apiaries. Although there are various IoT or non-IoT systems for bee-related issues, such comprehensive and technically advanced solutions for beekeeping and Varroa detection barely exist or perform mite detection after sending the data to the data center. The latter, in turn, increases communication and storage needs, which we try to limit in our approach. In the paper, we show an innovative Edge-based IoT solution for Varroa destructor detection. The solution relies on Tensor Processing Unit (TPU) acceleration for machine learning-based models pre-trained in the hybrid Cloud environment for bee identification and Varroa destructor infection detection. Our experiments were performed in order to investigate the effectiveness and the time performance of both steps, and the study of the impact of the image resolution on the quality of detection and classification processes prove that we can effectively detect the presence of varroosis in beehives in real-time with the use of Edge artificial intelligence invoked for the analysis of video streams.

Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.

Diagnosing Evolution of Cloud Cluster via Spatio-temporal Trace Analysis

To analyze inner-enterprise cloud cluster performance, the role of workload analysis is of paramount interest to system designers. However, the ever-evolving nature of inner-enterprise cloud platforms such as diversity and spatio-temporal nature of workloads makes evolution diagnosing a challenging task. In this paper, we propose MuCoTrAna-Inner, an evolution diagnosing approach for a large-scale cloud data center based on comparative spatio-temporal trace analysis. Moreover, we present a case study on two representative big traces: Alibaba 2017 trace, and Alibaba 2018 trace. Novel quantitative findings along with the performance bottleneck inferences and recommendations based on workload analysis are provided. Our multifaceted analyses of the traces and new findings not only reveal interesting insights that are of interest to system designers and administrators, but also establish a new view to diagnosing the evolution of inner-enterprise cloud cluster based on trace analysis.

Optimal Switching and Load Distribution Strategy for Energy Minimization and Performance Optimization of Cloud Data Center

Cloud computing is a promising computing technology utilized in every stage of the business. The cloud offers different services to cloud users from anytime to anywhere, and it is attained with different parameters, like load optimization, resource optimization. Due to the increase in data center, energy consumption has become a major issue in green data centers. The majority of data centers are function using peak load with huge scales. Thus, it is essential for carrying out energy saving in cloud data centers. This paper designed an energy-saving method using fat tree. The proposed techniques optimize the load at different zones of data center and user in the cloud platform. Here, the distribution of load in cloud data centers is performed using Taylor-based Manta Ray Foraging Optimization (Taylor-MRFO), which is an integration of Manta Ray Foraging Optimization (MRFO) and Taylor series. The method utilized different objectives that involve power, load, latency, and bandwidth. With the load distribution, the switching of cloud data center to the desired mode is performed using Actor critic neural network (ACNN). Thus, the dual strategy leads to performance optimization in cloud infrastructure and also in consolidating parallel workload in data centers more effectively. The proposed Taylor-MRFO+ACNN outperformed other methods with minimal energy of 0.553, minimal load of 0.363, and minimal fitness of 0.437, respectively.