Scalable traffic-aware virtual machine management for cloud data centers

Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime

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

Network and Server Resource Management Strategies for Data Centre Infrastructures: A Survey

The advent of virtualisation and the increasing demand for outsourced, elastic compute charged on a pay-as-you-use basis has stimulated the development of large-scale Cloud Data Centres (DCs) housing tens of thousands of computer clusters. Of the signi�cant capital outlay required for building and operating such infrastructures, server and network equipment account for 45% and 15% of the total cost, respectively, making resource utilisation e�ciency paramount in order to increase the operators' Return-on-Investment (RoI). In this paper, we present an extensive survey on the management of server and network resources over virtualised Cloud DC infrastructures, highlighting key concepts and results, and critically discussing their limitations and implications for future research opportunities. We highlight the need for and bene �ts of adaptive resource provisioning that alleviates reliance on static utilisation prediction models and exploits direct measurement of resource utilisation on servers and network nodes. Coupling such distributed measurement with logically-centralised Software De�ned Networking (SDN) principles, we subsequently discuss the challenges and opportunities for converged resource management over converged ICT environments, through unifying control loops to globally orchestrate adaptive and load-sensitive resource provisioning

Scalable Traffic-Aware Virtual Machine Management for Cloud Data Centers

Virtual Machine (VM) management is a powerful mechanism for providing elastic services over Cloud Data Centers (DC)s. At the same time, the resulting network congestion has been repeatedly reported as the main bottleneck in DCs, even when the overall resource utilization of the infrastructure remains low. However, most current VM management strategies are traffic-agnostic, while the few that are traffic-aware only concern a static initial allocation, ignore bandwidth oversubscription, or do not scale. In this paper we present S-CORE, a scalable VM migration algorithm to dynamically reallocate VMs to servers while minimizing the overall communication footprint of active traffic flows. We formulate the aggregate VM communication as an optimization problem and we then define a novel distributed migration scheme that iteratively adapts to dynamic traffic changes. Through extensive simulation and implementation results, we show that S-CORE achieves significant (up to 87%) communication cost reduction while incurring minimal overhead and downtime. Index Terms—Virtual Machine, Migration, Consolidation, Communication Cost, Scalable, Traffic-Aware, Data Center Networ

CloudMirror: Application-Aware Bandwidth Reservations in the Cloud

Cloud computing providers today do not offer guarantees for the network bandwidth available in the cloud, preventing tenants from running their applications predictably. To provide guarantees, several recent research proposals offer tenants a virtual cluster abstraction, emulating physical networks. Whereas offering dedicated virtual network abstractions is a significant step in the right direction, in this paper we argue that the abstractions exposed to tenants should aim to model tenant application structures rather than aiming to mimic physical network topologies. The fundamental problem in providing users with dedicated network abstractions is that the communication patterns of real applications do not typically resemble the rigid physical network topologies. Thus, the virtual network abstractions often poorly represent the actual communication patterns, resulting in overprovisioned/wasted network resources and underutilized computational resources. We propose a new abstraction for specifying bandwidth guarantees, which is easy to use because it closely follows application models; our abstraction specifies guarantees as a graph between application components. We then propose an algorithm to efficiently deploy this abstraction on physical clusters. Through simulations, we show that our approach is significantly more efficient than prior work for offering bandwidth guarantees.

Application-driven Bandwidth Guarantees in Datacenters

Providing bandwidth guarantees to specific applications is be-coming increasingly important as applications compete for shared cloud network resources. We present CloudMirror, a solution that provides bandwidth guarantees to cloud applications based on a new network abstraction and workload placement algorithm. An effective network abstraction would enable applications to easily and accurately specify their requirements, while simultaneously enabling the infrastructure to provision resources efficiently for deployed applications. Prior research has approached the bandwidth guarantee specification by using abstractions that resemble physical network topologies. We present a contrasting approach of deriving a network abstraction based on application communication structure, called Tenant Application Graph or TAG. CloudMirror also incorporates a new workload place-ment algorithm that efficiently meets bandwidth requirements specified by TAGs while factoring in high availability consider-ations. Extensive simulations using real application traces and datacenter topologies show that CloudMirror can handle 40% more bandwidth demand than the state of the art (e.g., the Ok-topus system), while improving high availability from 20 % to 70%