Approximation Algorithms for Energy Minimization in Cloud Service Allocation under Reliability Constraints

We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated to a capacity constraint, that can be chosen using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability of failure. On the other hand, we assume that the service runs as a set of independent instances of identical Virtual Machines. Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances which must be alive at the end of the day, and the Cloud provider offers a list of pairs (price,compensation), this compensation being paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair corresponds actually to a guaranteed success probability of fulfilling the constraint on the minimal number of instances. In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints during a given time period while minimizing the energy consumption of used resources. We consider two energy consumption models based on DVFS techniques, where the clock frequency of physical resources can be changed. For each allocation problem and each energy model, we prove deterministic approximation ratios on the consumed energy for algorithms that provide guaranteed probability failures, as well as an efficient heuristic, whose energy ratio is not guaranteed

Hybridized Darts Game with Beluga Whale Optimization Strategy for Efficient Task Scheduling with Optimal Load Balancing in Cloud Computing

A cloud computing technology permits clients to use hardware and software technology virtually on a subscription basis. The task scheduling process is planned to effectively minimize implementation time and cost while simultaneously increasing resource utilization, and it is one of the most common problems in cloud computing systems. The Nondeterministic Polynomial (NP)-hard optimization problem occurs due to limitations like an insufficient make-span, excessive resource utilization, low implementation costs, and immediate response for scheduling. The task allocation is NP-hard because of the increase in the amount of combinations and computing resources. In this work, a hybrid heuristic optimization technique with load balancing is implemented for optimal task scheduling to increase the performance of service providers in the cloud infrastructure. Thus, the issues that occur in the scheduling process is greatly reduced. The load balancing problem is effectively solved with the help of the proposed task scheduling scheme. The allocation of tasks to the machines based on the workload is done with the help of the proposed Hybridized Darts Game-Based Beluga Whale Optimization Algorithm (HDG-BWOA). The objective functions like higher Cloud Data Center (CDC) resource consumption, increased task assurance ratio, minimized mean reaction time, and reduced energy utilization are considered while allocating the tasks to the virtual machines. This task scheduling approach ensures flexibility among virtual machines, preventing them from overloading or underloading. Also, using this technique, more tasks is efficiently completed within the deadline. The efficacy of the offered arrangement is ensured with the conventional heuristic-based task scheduling approaches in accordance with various evaluation measures

Power Modeling and Resource Optimization in Virtualized Environments

The provisioning of on-demand cloud services has revolutionized the IT industry. This emerging paradigm has drastically increased the growth of data centers (DCs) worldwide. Consequently, this rising number of DCs is contributing to a large amount of world total power consumption. This has directed the attention of researchers and service providers to investigate a power-aware solution for the deployment and management of these systems and networks. However, these solutions could be bene\ufb01cial only if derived from a precisely estimated power consumption at run-time. Accuracy in power estimation is a challenge in virtualized environments due to the lack of certainty of actual resources consumed by virtualized entities and of their impact on applications\u2019 performance. The heterogeneous cloud, composed of multi-tenancy architecture, has also raised several management challenges for both service providers and their clients. Task scheduling and resource allocation in such a system are considered as an NP-hard problem. The inappropriate allocation of resources causes the under-utilization of servers, hence reducing throughput and energy e\ufb03ciency. In this context, the cloud framework needs an e\ufb00ective management solution to maximize the use of available resources and capacity, and also to reduce the impact of their carbon footprint on the environment with reduced power consumption. This thesis addresses the issues of power measurement and resource utilization in virtualized environments as two primary objectives. At \ufb01rst, a survey on prior work of server power modeling and methods in virtualization architectures is carried out. This helps investigate the key challenges that elude the precision of power estimation when dealing with virtualized entities. A di\ufb00erent systematic approach is then presented to improve the prediction accuracy in these networks, considering the resource abstraction at di\ufb00erent architectural levels. Resource usage monitoring at the host and guest helps in identifying the di\ufb00erence in performance between the two. Using virtual Performance Monitoring Counters (vPMCs) at a guest level provides detailed information that helps in improving the prediction accuracy and can be further used for resource optimization, consolidation and load balancing. Later, the research also targets the critical issue of optimal resource utilization in cloud computing. This study seeks a generic, robust but simple approach to deal with resource allocation in cloud computing and networking. The inappropriate scheduling in the cloud causes under- and over- utilization of resources which in turn increases the power consumption and also degrades the system performance. This work \ufb01rst addresses some of the major challenges related to task scheduling in heterogeneous systems. After a critical analysis of existing approaches, this thesis presents a rather simple scheduling scheme based on the combination of heuristic solutions. Improved resource utilization with reduced processing time can be achieved using the proposed energy-e\ufb03cient scheduling algorithm

Power Consumption and Carbon Emission Equivalent for Virtualized Resources – An Analysis: Virtual Machine and Container Analysis for Greener Data Center

The International Energy Agency (IEA) revealed that the worldwide energy-related carbon dioxide (CO2) situation has hit a historic high of 33.1 Giga tonnes (Gt) of CO2. 85% of the rise in emissions was due to China, India, and the United States. The increase in emissions in India was 4.8%, or 105 Mega tonnes (Mt) of CO2, with the increase in emissions being evenly distributed across the transportation and industrial sectors, according to Beloglazov et al (2011). Environmental contamination brought on by carbon emissions is harmful to the environment. As a result, there is an urgent need for the IT sectors to develop effective and efficient technology to eliminate such carbon emissions. The primary focus is on lowering carbon emissions due to widespread awareness of the issue

Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future