Online virtual machine evacuation for disaster resilience in inter-data center networks

With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi-objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.publishe

Cicada: Predictive Guarantees for Cloud Network Bandwidth

In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know what bandwidth guarantees they want. However, application bandwidth demands can be complex and time-varying, and many tenants might lack sufficient information to request a bandwidth guarantee that is well-matched to their needs. A tenant's lack of accurate knowledge about its future bandwidth demands can lead to over-provisioning (and thus reduced cost-efficiency) or under-provisioning (and thus poor user experience in latency-sensitive user-facing applications). We analyze traffic traces gathered over six months from an HP Cloud Services datacenter, finding that application bandwidth consumption is both time-varying and spatially inhomogeneous. This variability makes it hard to predict requirements. To solve this problem, we develop a prediction algorithm usable by a cloud provider to suggest an appropriate bandwidth guarantee to a tenant. The key idea in the prediction algorithm is to treat a set of previously observed traffic matrices as "experts" and learn online the best weighted linear combination of these experts to make its prediction. With tenant VM placement using these predictive guarantees, we find that the inter-rack network utilization in certain datacenter topologies can be more than doubled

On the dynamics of valley times and its application to bulk-transfer scheduling

Periods of low load have been used for the scheduling of non-interactive tasks since the early stages of computing. Nowadays, the scheduling of bulk transfers—i.e., large-volume transfers without precise timing, such as database distribution, resources replication or backups—stands out among such tasks, given its direct effect on both the performance and billing of networks. Through visual inspection of traffic-demand curves of diverse points of presence (PoP), either a network, link, Internet service provider or Internet exchange point, it becomes apparent that low-use periods of bandwidth demands occur at early morning, showing a noticeable convex shape. Such observation led us to study and model the time when such demands reach their minimum, on what we have named valley time of a PoP, as an approximation to the ideal moment to carry out bulk transfers. After studying and modeling single-PoP scenarios both temporally and spatially seeking homogeneity in the phenomenon, as well as its extension to multi-PoP scenarios or paths—a meta-PoP constructed as the aggregation of several single PoPs—, we propose a final predictor system for the valley time. This tool works as an oracle for scheduling bulk transfers, with different versions according to time scales and the desired trade-off between precision and complexity. The evaluation of the system, named VTP, has proven its usefulness with errors below an hour on estimating the occurrence of valley times, as well as errors around 10% in terms of bandwidth between the prediction and actual valley trafficThis work has been partially supported by the European Commission under the project H2020 METRO-HAUL (Project ID: 761727

On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft

System Support for Managing Risk in Cloud Computing Platforms

Cloud platforms sell computing to applications for a price. However, by precisely defining and controlling the service-level characteristics of cloud servers, they expose applications to a number of implicit risks throughout the application’s lifecycle. For example, user’s request for a server may be denied, leading to rejection risk; an allocated resource may be withdrawn, resulting in revocation risk; an acquired cloud server’s price may rise relative to others, causing price risk; a cloud server’s performance may vary due to external factors, triggering valuation risk. Though these risks are implicit, the costs they bear on the applications are not. While some risks exist in all Infrastructure-as-a-Service offerings, they are most pronounced in an emerging category called transient cloud servers. Since transient servers are carved out of instantaneous idle cloud capacity, they exhibit two distinct features: (i) revocations that are intentional, frequent and come with advanced warning, and (ii) prices that are low in average but vary across time and location. Thus, despite enabling inexpensive access to at-scale computing, transient cloud servers expose applications to risks, the scale of which were unseen in the past platforms. Unfortunately, the current generation system software are not designed to handle these risks, which in turn results in inconsistent performances, unexpected failures, missed savings, and slower adoption. In this dissertation, we elevate risk management to a first-class system design principle. Our goal is to identify the risks, quantify their costs, and explicitly manage them for applications deployed on cloud platforms. Towards that goal, we adapt and extend concepts from finance and economics to propose a new system design approach called financializing cloud computing. By treating cloud resources as investments, and by quantifying the cost of their risks, financialization enables system software to manage the risk-reward trade-offs, explicitly and autonomously. We demonstrate the utility of our approach via four contributions: (i) mitigating revocation risk with insurance policy, (ii) reducing price risk through active trading, (iii) eliminating uncertainty risk by index tracking, and (iv) minimizing server’s valuation risk via asset pricing. We conclude by observing that diversity and asymmetry in the creation and consumption of cloud compute resources is on the rise, and that financialization can be effectively employed to manage its complexity and risks