Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers

Dynamic consolidation of Virtual Machines (VMs) can effectively enhance the resource utilization and energy-efficiency of the Cloud Data Centers (CDC). Existing research on Cloud resource reservation and scheduling signify that Cloud Service Users (CSUs) can play a crucial role in improving the resource utilization by providing valuable information to Cloud service providers. However, utilization of CSUs' provided information in minimization of energy consumption of CDC is a novel research direction. The challenges herein are twofold. First, finding the right benign information to be received from a CSU which can complement the energy-efficiency of CDC. Second, smart application of such information to significantly reduce the energy consumption of CDC. To address those research challenges, we have proposed a novel heuristic Dynamic VM Consolidation algorithm, RTDVMC, which minimizes the energy consumption of CDC through exploiting CSU provided information. Our research exemplifies the fact that if VMs are dynamically consolidated based on the time when a VM can be removed from CDC-a useful information to be received from respective CSU, then more physical machines can be turned into sleep state, yielding lower energy consumption. We have simulated the performance of RTDVMC with real Cloud workload traces originated from more than 800 PlanetLab VMs. The empirical figures affirm the superiority of RTDVMC over existing prominent Static and Adaptive Threshold based DVMC algorithms

Energy harvesting aware clustering-based routing protocols for wireless sensor networks

Routing protocols for wireless sensor networks (WSNs) play an important role in the performance of WSNs. They have an impact on, for instance, energy efficiency, reliable data transmission, channel utilization, and faster data delivery. Routing protocols can be broadly classified into two groups: i) Flat and ii) Hierarchical or clustering based techniques. The latter techniques are more energy efficient and scalable than the former. However, clustering techniques inherently create extra load on cluster heads and cluster heads (CHs) are more prone to breakdown. To address these issues and to support a sustainable environment, energy harvesting aware clustering techniques are evolving. However, there are only a limited number of these techniques available in the literature. Most are either single hop or location aware or not mostly self-organized. Therefore, they are not appropriate and economically viable for medium and large scale WSNs. In this dissertation, we have developed an innovative multi-hop energy harvesting aware clustering technique for location unaware WSNs, the Energy Harvesting Aware Energy Efficient (EHAEE) clustering scheme. EHAEE takes into account the intra-cluster communication cost, maximum storage capacity, and the dynamic values of load, gain rate, and remaining energy of a sensor node during the CHs selection and joining phases. This enables EHAEE to be more self-organized in the clustering process and makes it more suitable for non-uniform node distribution. The performance of EHAEE is evaluated through the network simulation models and is also compared and contrasted with another promising and widely accepted clustering technique, HEED, in the context of many different real-world network scenarios. Simulation results demonstrated that EHAEE increases network lifetime and reliability simultaneously. We have also conducted a statistical significance test using the t-test which exhibits a significant improvement of the performance of EHAEE over HEED

Multi-objective dynamic virtual machine consolidation algorithm for cloud data centers with highly energy proportional servers and heterogeneous workload

Present Dynamic VM Consolidation (DVMC) algorithms assume that optimal energy efficiency can be achieved via maximum load on Physical Machines (PMs). Such assumption has become invalid with the advent of the highly energy proportional PMs. Additionally, these algorithms consider only varying resource demand, ignoring dissimilarity of workload finishing time, aka the VM Release Time (VMRT), whereas both aspects are strongly associated with energy consumption. Consequently, traditional algorithms fail to proffer optimal performance under real Cloud scenarios. Although minimization of VM migration brings massive benefit for Cloud Data Center (CDC), it is complete opposite of what is needed to minimize energy consumption through DVMC. As such, our proposed multi-objective Stochastic Release Time aware DVMC (SRTDVMC) algorithm is unique in addressing concomitant minimization of energy consumption and VM migration in the presence of state-of-the-art PMs and heterogeneous workloads. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management : A review

As envisioned by Leonard Kleinrock [1], Cloud computing has transformed the dream of “computing as a utility” into reality, so much so it has turned out as the latest computing paradigm [2]. Cloud computing is called as Service-on-demand, as Cloud Service Providers (CSPs) assure users about potentially unlimited amount of resources that can be chartered on demand. It is also known as elastic computing, since Cloud Service Users (CSUs) can dynamically scale, expand, or shrink their rented resources anytime and expect to pay for the exact tenure of resource usage under Service Level Agreements (SLA). Through such flexibilities and financial benefits, CSPs have been attracting millions of clients who are simultaneously sharing the underlying computing and storage resources that are collectively known as Cloud data centers

Implementing virtual machine:a performance evaluation

A hypervisor is a hardware virtualization technique that allows multiple guest operating systems to run on a single host machine at the same time. Each Virtual Machine (VM) or known as guest operating system emulates all interfaces and resources of a real computer system. Virtualization is beneficial as one of the educational tools to facilitate students’ hands-on experiences and research activities. However, the performance of VM needs to be taken into consideration. We investigate the performance of a set of VMs using Oracle VirtualBox on several host machines, each of which has its own system specifications. We observe the resource utilization of each host machine in terms of its CPU utilization, CPU speed as well as memory usage. Experimental results show that the CPU utilization averages are 51.78%, 60.7% and 62.57% for cases before memory allocation, 1/2 of memory capacity and 2/3 of memory capacity, respectively. It is indicate that the utilization of a host processor is directly proportional to the memory capacity assigned for a virtual machine