Towards Hybrid Cloud-assisted Crowdsourced Live Streaming: Measurement and Analysis

Crowdsourced Live Streaming (CLS), most notably Twitch.tv, has seen explosive growth in its popularity in the past few years. In such systems, any user can lively broadcast video content of interest to others, e.g., from a game player to many online viewers. To fulfill the demands from both massive and heterogeneous broadcasters and viewers, expensive server clusters have been deployed to provide video ingesting and transcoding services. Despite the existence of highly popular channels, a significant portion of the channels is indeed unpopular. Yet as our measurement shows, these broadcasters are consuming considerable system resources; in particular, 25% (resp. 30%) of bandwidth (resp. computation) resources are used by the broadcasters who do not have any viewers at all. In this paper, we closely examine the challenge of handling unpopular live-broadcasting channels in CLS systems and present a comprehensive solution for service partitioning on hybrid cloud. The trace-driven evaluation shows that our hybrid cloud-assisted design can smartly assign ingesting and transcoding tasks to the elastic cloud virtual machines, providing flexible system deployment cost-effectively

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications

Planning Live-Migrations to Prepare Servers for Maintenance

International audienceIn a virtualized data center, server maintenance is a common but still critical operation. A prerequisite is indeed to relocate elsewhere the Virtual Machines (VMs) running on the production servers to prepare them for the maintenance. When the maintenance focuses several servers, this may lead to a costly relocation of several VMs so the migration plan must be chose wisely. This however implies to master numerous human, technical, and economical aspects that play a role in the design of a quality migration plan. In this paper, we study migration plans that can be decided by an operator to prepare for an hardware upgrade or a server refresh on multiple servers. We exhibit performance bottleneck and pitfalls that reduce the plan efficiency. We then discuss and validate possible improvements deduced from the knowledge of the environment peculiarities

Prediction-based virtual instance migration for balanced workload in the cloud datacenters

Datacenters in the cloud today provide virtualized resources of CPU, memory, disk, and networks so that millions of users can use the services at the same time in an efficient and scalable way. One of the major challenges in these datacenters is load balancing and shifting. As a huge number of requests are sent to a particular datacenter or a group of servers are asked to process more than their fair share, some of the servers are overloaded, slowed down, hot spots are created, and even hardware failures may occur. This unbalanced load in the end deteriorates the performance of the entire system easily. In this paper, we propose a load balancer that aims at alleviating hot spots and distributing the load from overloaded servers to underutilized servers. Our load balancer monitors the loads of the servers, detects indications of overloading, then migrates virtual instances from overloaded servers to target servers. We have implemented the load balancer in a real system using the Xen hypervisor. We have also conducted an event-driven simulation to evaluate the performance of our system on a large-scale. Our results indicate that our reactive-predictive load balancing algorithm helps balance load among servers in the cloud as much as the best-case scenario from the exhaustive search with much less overhead

Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy

Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE – a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3% - 43.6% less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2% with 0.17% SLA violation rate as the performance penalty

Migration Control in Cloud Computing to Reduce the SLA Violation

The requisition of cloud based services are more eminent because of the enormous benefits of cloud such as pay-as-you-use flexibility,scalability and low upfront cost. Day-by-day due to growing number of cloud consumers the load on the datacenters is also increasing. Various load distribution and dynamic load balancing approaches are being followed in the datacenters to optimize the resource utilization so that the performance may be maintained during the increased load. Virtual machine (VM) migration is primarily used to implement dynamic load balancing in the datacenters. But, the poorly designed dynamic VM migration policies may negate its benefits. The VM migration overheads result in the violations of service level agreement (SLA) in the cloud environment.In this paper,an extended VM migration control model is proposedto minimize the SLA violations while controlling the energy consumption of the datacenter during VM migration. The parameters of execution boundary threshold is used to extend an existing VM migration control model. The proposed model is tested through extensive simulations using CloudSim toolkit by executing real world workload. Results are obtained in terms of number of SLA violations while controlling the energy consumption in the datacenter. Results show that the proposed modelachieves better performance in comparison to the existing model