Efficient Heuristics for Virtual Machine Migration in Data Centers

Live migration of virtual machines is one of the essential virtualization technologies which enables the consolidation and load balancing in cloud data centers without interrupting the services. Main goals for optimizing a single virtual machine live migration is to minimize migration time, transferred data and downtime. Planning multiple live migrations in a data center has an essential impact on feasibility of consolidation and quality of services during migrations, however, optimizing parallel VM migrations has been studies less. Minimizing makespan (total migration time) while reducing energy and service quality degradation caused by using datacenter resources for migrations, are the main objectives of the problem. One of the issues in planning multiple live migrations is to detect and consider migrations order dependency constraints and possible deadlocks caused by lack of enough free resources in servers during the process. In the literature, exact mathematical models are not scalable and heuristics are not optimal and they don't consider the quality of service and energy efficiency of migration process when resources are restricted. In this work we propose a heuristic algorithm for scheduling the migration of virtual machines in a data center in order to minimize makespan (total migration time) and solve the conflicts (deadlocks) caused by limitation of resources with minimum cost and quality degradation

Exploring Wireless Data Center Networks: Can They Reduce Energy Consumption While Providing Secure Connections?

Data centers have become the digital backbone of the modern world. To support the growing demands on bandwidth, Data Centers consume an increasing amount of power. A significant portion of that power is consumed by information technology (IT) equipment, including servers and networking components. Additionally, the complex cabling in traditional data centers poses design and maintenance challenges and increases the energy cost of the cooling infrastructure by obstructing the flow of chilled air. Hence, to reduce the power consumption of the data centers, we proposed a wireless server-to-server data center network architecture using millimeter-wave links to eliminate the need for power-hungry switching fabric of traditional fat-tree-based data center networks. The server-to-server wireless data center network (S2S-WiDCN) architecture requires Line-of-Sight (LoS) between servers to establish direct communication links. However, in the presence of interference from internal or external sources, or an obstruction, such as an IT technician, the LoS may be blocked. To address this issue, we also propose a novel obstruction-aware adaptive routing algorithm for S2S-WiDCN. S2S-WiDCN can reduce the power consumption of the data center network portion while not affecting the power consumption of the servers in the data center, which contributes significantly towards the total power consumption of the data center. Moreover, servers in data centers are almost always underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. To address the high power consumption of the servers, we proposed a network-aware bandwidth-constrained server consolidation algorithm called Network-Aware Server Consolidation (NASCon) for wireless data centers that can reduce the power consumption up to 37% while improving the network performance. However, due to the arrival of new tasks and the completion of existing tasks, the consolidated utilization profile of servers change, which may have an adverse effect on overall power consumption over time. To overcome this, NASCon algorithm needs to be executed periodically. We have proposed a mathematical model to estimate the optimal inter-consolidation time, which can be used by the data center resource management unit for scheduling NASCon consolidation operation in real-time and leverage the benefits of server consolidation. However, in any data center environment ensuring security is one of the highest design priorities. Hence, for S2S-WiDCN to become a practical and viable solution for data center network design, the security of the network has to be ensured. S2S-WiDCN data center can be vulnerable to a variety of different attacks as it uses wireless links over an unguided channel for communication. As being a wireless system, the network has to be secured against common threats associated with any wireless networks such as eavesdropping attack, denial of services attack, and jamming attack. In parallel, other security threats such as the attack on the control plane, side-channel attack through traffic analysis are also possible. We have done an extensive study to elaborate the scope of these attacks as well as explore probable solutions against these issues. We also proposed viable solutions for the attack against eavesdropping, denial of services, jamming, and control-plane attack. To address the traffic analysis attack, we proposed a simulated annealing-based random routing mechanism which can be adopted instead of default routing in the wireless data center

Time dependent virtual machine consolidation with SLA(Service Level Agreement) consideration

Cloud data center is becoming the most essential infrastructure for computing services. In effect, the operational cost of a data center is also increasing drastically. To decrease this cost, consolidation of VMs with less degradation of performance is so important. To guarantee the expected Quality of Service (QOS) the important factors to be controlled are performance of the service including timely leverage and overall resource utilization of the data center. In this paper, we tried to investigate how to efficiently utilize resources with reduced SLA violation in a data center. In order to optimize efficiency, VMs ought to be consolidated as tight as possible. To achieve this, an algorithm based on first fit decreasing (FFD) bin packing is designed and implemented. Hence, the algorithm is implemented on the following three approaches to pursue the goal: a)Deterministic Approach, which is mainly based on mean of the individual VMs;b)Stochastic Approach I, which is basically done by treating individual VMs based on their mean and variances and ;c) Stochastic Approach II, which depends on mean and covariance of individual VMs. The results obtained show that consolidating VMs based on mean and variance(stochastic approach I) performed better than the other two approaches for minimizing total percentage of SLA violation and stochastic approach II performed better than the two approaches for minimizing the number of PMs in consolidation

Multi-objective ACO resource consolidation in cloud computing environment

Cloud computing systems provide services to users based on a pay-as-you-go model. High volume of interest and a number of requests by user in cloud computing has resulted in the creation of data centers with large amounts of physical machines. These data centers consume huge amounts of electrical energy and air emissions. In order to improve Datacenter efficiency, resource consolidation using virtualization technology is becoming important for the reduction of the environmental impact caused by the data centers (e.g. electricity usage and carbon dioxide). By using Virtualization technology multiple VM (logical slices that conceptually called VMs) instances can be initialised on a physical machine. As a result, the amounts of active hardware are reduced and the utilisations of physical resources are increased. The present thesis focuses on problem of virtual machine placement and virtual machine consolidation in cloud computing environment. VM placement is a process of mapping virtual machines (Beloglazov and Buyya) to physical machines (PMs). VM consolidation reallocates and optimizes the mapping of VMs and PMs based on migration technique. The goal is to minimize energy consumption, resource wastage and energy communication cost between network elements within a data center under QoS constraints through VM placement and VM consolidation algorithms. The multi objective algorithms are proposed to control trade-off between energy, performance and quality of services. The algorithms have been analyzed with other approaches using Cloudsim tools. The results demonstrate that the proposed algorithms can seek and find solutions that exhibit balance between different objectives. Our main contributions are the proposal of a multi- objective optimization placement approach in order to minimize the total energy consumption of a data center, resource wastage and energy communication cost. Another contribution is to propose a multiobjective consolidation approach in order to minimize the total energy consumption of a data center, minimize number of migrations, minimize number of PMs and reconfigure resources to satisfy the SLA. Also the results have been compared with other single-objective and multi-objective algorithms

SERCON-BASED TIMESTAMPED VIRTUAL MACHINE MIGRATION SCHEME FOR CLOUD

With the advent of cloud computing, the need for deploying multiple virtual machines (VMs) on multiple hosts to address the ever-increasing user demands for services has raised concerns regarding energy consumption. Considerable energy is consumed while keeping the data centers with a large number of servers active. However, in data centers, there are cases where these servers may not get utilized efficiently. There can be servers that consume sufficient energy while running resources for a small task (demanding fewer resources), but there can also be servers that receive user requests so frequently that resources may be exhausted, and the server becomes unable to fulfill requests. In such a scenario, there is an urgent need to conserve energy and resources which is addressed by performing server consolidation. Server consolidation aims to reduce the total number of active servers in the cloud such that performance does not get compromised as well as energy is conserved in an attempt to make each server run to its maximum. This is done by reducing the number of active servers in a data center by transferring the workload of one or more VM(s) from one server to another, referred to as VM Migration (VMM). During VMM, time is supposed as a major constraint for effective and user-transparent migration. Thus, this paper proposes a novel VM migration strategy considering time sensitivity as a primary constraint. The aim of the proposed Time Sensitive Virtual Machine Migration (TS-VMM) is to reduce the number of migrations to a minimum with effective cost optimization and maximum server utilization

An evaluation of urban consolidation centers through continuous analysis with non-equal market share companies

This paper analyzes the logistic cost savings caused by the implementation of Urban Consolidation Centers (UCC) in a dense area of a city. In these urban terminals, freight flows from interurban carriers are consolidated and transferred to a neutral last-mile carrier to perform final deliveries. This operation would reduce both last-mile fleet size and average distance cost. Our UCC modeling approach is focused on continuous analytic models for the general case of carriers with different market shares. Savings are highly sensitive to the design of the system: the increment of capacity in interurban vehicles and the proximity of the UCC terminal to the area in relation to current distribution centers. An exhaustive collection of possible market shares distributions are discussed. Results show that market shares distribution does not affect cost savings significantly. The analysis of the proposed model also highlights the trade-off between savings in the system and a minimum market share per company when the consolidation center is established.Postprint (published version

New Jersey 9-1-1 Consolidation Study: Site Visit Results and Implications for Consolidation

In 1999, the Center for Government Services at Rutgers, The State University of New Jersey completed a study of New Jersey's E9-1-1 system. The study offered a snapshot of the extensive and decentralized network of communications centers that receive incoming calls requesting emergency assistance and that dispatch police, fire, and medical units. In 2005, the New Jersey Office of Management and Budget commissioned the John J. Heldrich Center for Workforce Development at Rutgers University to build on the findings of the 1999 study by exploring ways to improve the efficiency of New Jersey's E9-1-1 system while maximizing the use of available funding.This report is the result of site visits and interviews with officials from 12 PSAPs. The focus of this report is on the current landscape of local operations, funding, staffing, equipment, and technology. In addition, this report identifies issues associated with consolidation, including barriers and opportunities, and presents recommendations for promoting consolidation in New Jersey. It is the third of four deliverables to be produced by the Heldrich Center for the State of New Jersey's 9-1-1 Consolidation Study.Presently, there are over 200 Public Safety Answering Points (PSAPs) and more than 100 enhanced Public Safety Dispatch Points (PSDPs) operating in New Jersey. The central goal of this study is to determine whether a consolidation of PSAPs and PSDPs could reduce costs while maintaining and/or improving the level of service. In this report, consolidation is defined as the reduction in the number of locally managed PSAPs and PSDPs that provide emergency communications services

Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable Last-Mile Strategies

In the last decade, e‐commerce has grown substantially, increasing business‐to‐business, business‐to‐consumer, and consumer‐to‐consumer transactions. While this has brought prosperity for the e-retailers, the ever-increasing consumer demand has brought more trucks to the residential areas, bringing along externalities such as congestion, air and noise pollution, and energy consumption. To cope with this, different logistics strategies such as the introduction of micro-hubs, alternative delivery points, and use of cargo bikes and zero emission vehicles for the last mile have been introduced and, in some cases, implemented as well. This project, hence, aims to develop an analytical framework to model urban last mile delivery. In particular, this study will build upon the previously developed econometric behavior models that capture e-commerce demand. Then, based on continuous approximation techniques, the authors will model the last-mile delivery operations. And finally, using the cost-based sustainability assessment model (developed in this study), the authors will estimate the economic and environmental impacts of residential deliveries under different city logistics strategies.View the NCST Project Webpag